Preprint 5

# Simulation-based Evaluation of Indoor Positioning Systems in Connected
Aircraft Cabins

Paul Schwarzbacha, Jonas Ninnemanna, Hagen Ußlera, Oliver Michlera and Michael
Schultzb CONTACT Paul Schwarzbach. Email<EMAIL_ADDRESS>0000-0002-1091-782X 0000-0001-7988-079X 0000-0002-8599-5304
0000-0003-4056-8461 aChair of Transport Systems Information Technology, TUD
Dresden University of Technology, Hettnerstr. 3, 01062 Dresden, DE; bChair of
Air Traffic Concepts, University of the Bundeswehr Munich, Werner-Heisenberg-
Weg 39, 85577 Neubiberg, DE

###### Abstract

The manuscript discusses the increasing use of location-aware radio
communication systems to support operational processes for the demanding
aircraft cabin environment. In this context, the challenges for evaluation and
integration of radio-based localization systems in the connected cabin are
specifically addressed by proposing a hybrid deterministic and stochastic
simulation approach, including both model-based ray-tracing and empirical
residual simulation. The simulation approach is detailed in the manuscript and
a methodology for applying and evaluating localization methods based on
obtained geometric relations is conducted. This can also be used as a data
generation and validation tool for data-driven localization methods, which
further increase the localization accuracy and robustness. The derived
location information can in return be used in order to perform operational
prediction and optimization for efficient and sustainable passenger handling.
A dataset derived from the introduced simulation platform is publicly
available.

###### keywords:

Radio Propagation Simulation; Localization; Radio Positioning; Ultra-Wideband
(UWB); Machine Learning; Connected Cabin; Indoor Positioning Systems (IPS);
Location-awareness; Passenger (PAX) Boarding; Cabin Operation

##  1 Introduction

Efficient air traffic management and ground handling are crucial factors that
drive the performance of airline operations. The integration of aircraft and
passenger (PAX) trajectories presents a major challenge in future mobility
management. The COVID-19 pandemic has significantly impacted the
transportation network Schwarzbach et al. (2020), highlighting the need for
effective PAX handling processes at bottlenecks. PAX handling processes such
as boarding and disembarkation within the confined aircraft cabin pose a
challenge to air transportation. A key metric known as aircraft turnaround
constitutes all ground-handling processes, including freight and baggage
unloading and loading, refueling, cleaning, catering, and PAX boarding and
disembarking Schultz et al. (2020). PAX behavior largely influences the
duration of boarding, and convenient and fast boarding is a priority for both
PAX and airlines Schultz (2018c). Queuing at boarding areas is a major concern
for PAX. Airlines and airports aim to meet target times for ground operations
provided by the responsible stakeholder, considering the current state of
operations and experiences about the expected progress. Therefore, a high
demand on both technological and operational solutions is present.

Location-aware radio-based communication networks provide a technical solution
for PAX state monitoring and handling. While several general survey studies
for these so-called indoor positioning systems (IPS), including Zafari,
Gkelias, and Leung (2019); Mendoza-Silva, Torres-Sospedra, and Huerta (2019);
Geok et al. (2020), have been conducted, questions of technology selection,
usable hardware, applicable positioning methods, as well as the holistic
optimization of this emerging localization systems in the context of the
connected cabin still remain unresolved. In addition, the accuracy and
scalability of radio-enabled localization within the harsh propagation
environment still poses a major research question. In terms of challenges for
the development and integration of these systems within in the cabin
environment, a variety of challenges arise. Among others this includes: The
aircraft cabin is a restricted area so accessibility for integration and
evaluation studies is limited. The availability of modern technologies,
possible physical layer modifications or hardware components might not be
available during field trials, which also causes limitations in the
adaptability of the surveyed data, which is specific to the given scenario in
which the data was surveyed.

In order to evaluate the radio propagation channel within the aircraft cabin,
a variety of empirical studies for different radio technologies have been
conducted Chuang et al. (2007); Jacob et al. (2009); Chiu and Michelson
(2010); Schulte et al. (2011); Neuhold et al. (2016); Cogalan, Videv, and Haas
(2017). The focus of these works are empirical findings concerning general
characteristics of the propagation environment. In addition, localization
capabilities of e.g. 5G Cavdar et al. (2018), WiFi Wang et al. (2021) or
Ultra-Wideband (UWB) Karadeniz et al. (2020); Geyer and Schupke (2022) are
discussed. However, these studies are limited to the technologies,
constellation and hardware applied, so they allow no generalization.

To address the aforementioned challenges and shortcomings of conducted
studies, an adaptive and technology-open simulation procedure is presented in
this paper as simulation is typically applicable for early-stage evaluation.
This is realized by the consolidation of both deterministic, model-based 3D
ray-tracing Yun and Iskander (2015) in order to derive signal characteristics,
such as time-of-flight and visibility, and probabilistic error modeling in
order to address additional hardware- or propagation-specific influences
Schwarzbach, Weber, and Michler (2022). Unlike previous works, which focus on
the general network coverage Ußler et al. (2022) or passive radio sensing
Ninnemann et al. (2022); Schwarzbach, Ninnemann, and Michler (2022) within the
cabin, this contribution focuses on the specialized demands and requirements
of active localization using ray-tracing. However, these deterministic provide
a strong correlation with the environment and geometry, as they are highly
accurate but require complex modeling and computational efforts. These models
are spatially consistent and can predict the channel’s behavior with high
accuracy. In addition, stochastic models are based on empirical channel
measurements, and the distributions of signal parameters are determined for
various reference environments. They can easily be implemented and do not
require specific modeling of the environment. However, stochastic models may
not be as accurate as deterministic models and may have some level of
inconsistency in their spatial representation. To combine these advantages,
the paper utilizes a hybrid simulation approach.

The manuscript presents the capabilities of radio-based simulation with
respect to localization functionalities with applications for air transport
management and especially in-cabin systems. The foundation for this is a ray-
tracing respectively ray-launching based radio propagation simulation, which
is commonly applied for network coverage analysis. In order to provide a
feasible data basis for radio-based localization, the conventional simulation
approach is modified. The work addresses the following main research
objectives:

  * •

Presentation of a holistic radio propagation approach for active localization
systems addressing the present issues of spatial consistency and the influence
of stochastic errors by combining multiple simulation procedures;

  * •

Merging of operational processes and simulations, e.g. boarding simulation
with radio-based localization in a common framework;

  * •

Highlighting the potentials of the simulation framework to evaluate also radio
sensing applications and data-driven approaches.

The rest of the paper is structured as follows: Following this introduction,
Section 2 provides a comprehensive overview on the operational aspects and
potentials of location-awareness, as well as recent developments in radio-
based localization and the corresponding radio channel modeling and
simulation, with a specific emphasis on the connected aircraft cabin.
Furthermore, Section 3 discusses the methodology of this work, including the
proposed simulation portions and positioning capabilities. In Section 4 the
carried out simulation and respective results are discussed. The paper
concludes with a conclusion and suggestions for future research directions.

##  2 Literature Review

The scope of the manuscript represents a cross-section of diverse scientific
disciplines and their dedicated applications. These include operational
planning and turnaround optimization within air traffic management, the realm
of radio-based indoor positioning systems, as well as the modeling and
simulation of these systems. Therefore, this section provides a brief
literature review of the aforementioned research fields, including operational
aviation research, radio-based localization as well radio chanel modeling, and
highlights the resulting potential benefits of their integration.

###  2.1 Operational Perspective

Numerous studies have investigated the most efficient sequencing of PAX during
boarding and deboarding processes Ferrari and Nagel (2005); Van Landeghem and
Beuselinck (2002); Tang et al. (2012); Milne, Salari, and Kattan (2018). These
studies typically analyze the effects of various operationally relevant input
parameters, including seat load factor, group composition (such as families
and couples), volume of carry-on baggage, and use of baggage compartments. The
objective of optimizing boarding/deboarding processes is generally to minimize
the amount of time required. The duration necessary for establishing the
sequence or the stability of the solution is commonly disregarded (see Schultz
(2018c) for variability in boarding time).

Since the PAX cabin lacks sensors, optimization strategies cannot consider the
current situation in the cabin. Therefore, a sequence often deemed
advantageous might not be the optimal solution in a specific scenario.
Implementing a cabin sensor system would facilitate the collection of movement
data (such as walking speeds and position of bottlenecks Schultz (2018a)) and
status data regarding cabin configuration (such as seat occupancy and baggage
compartment usage).

As currently, sparse data on cabin processes are available, they could be
utilized to calibrate airline-specific models Schultz (2018b) and support
dynamic optimization approaches Notomista et al. (2016); Zeineddine (2017).
These dynamic approaches can then update the sequence considering the current
congestion situation. If congestion makes the rear rows to board problematic,
priority should be given to front-row seat PAX. Similarly, data on the
overhead storage compartments could be utilized. Operating at total capacity,
these compartments indicate that nearby PAX may take longer to stow their own
luggage, potentially obstructing the aisle for an extended duration. In
addition, the data of the cabin conditions can be recorded, used to develop
data-driven models, and finally predict the process flows, e.g., prediction of
boarding times Schultz and Reitmann (2019).

With the COVID-19 pandemic, new challenges have emerged for cabin operations
that focus not only on boarding time but also on minimizing the risk of virus
transmissions Milne, Delcea, and Cotfas (2021); Schultz and Soolaki (2021).
Given these new operational requirements, sensor-based location tracking of
PAX within the cabin is an important requirement and no longer just a desired
feature Schwarzbach et al. (2020).

###  2.2 Radio-based Localization

The determination of an absolute location information can be realized using
radio-based systems. In this context, radio devices that can be distinguished
as stationary infrastructure units (anchors) and mobile tags to be located. In
principle, range-based or range-free relations are derived from physical
parameters of the systems, which can then be used to calculate the tag’s
location with knowledge of the anchor’s location Mendoza-Silva, Torres-
Sospedra, and Huerta (2019).

As a technological basis, wireless communication systems have garnered
significant research attention over the past two decades due to their cost-
effectiveness, energy efficiency, retrofitting capabilities, and versatility
Zafari, Gkelias, and Leung (2019). This has led to the rapid emergence of the
Internet of Things (IoT) across various research and application domains,
enabled by affordable smart interconnections of devices, individuals, and
operations Al-Fuqaha et al. (2015); Lin et al. (2017). Researchers have
explored using radio-based localization technologies to enhance intelligent
and automated systems in various application areas, going beyond fundamental
IoT functions like communication and sensor data acquisitionLi et al. (2021).
Similar to the utilization of Global Navigation Satellite Systems (GNSS) Egea-
Roca et al. (2022), the widespread adoption of smartphones Xiao et al. (2016);
Davidson and Piché (2017) and connected wearables Ometov et al. (2021) has
driven the comprehensive use of location-enabled communication technologies.

Terrestrial location-aware communication systems, historically confined to
GNSS-denied scenarios, are commonly referred to as Indoor Positioning Systems
(IPS) Farahsari et al. (2022). An additional research focus involves the
ongoing transformation of mobile communication systems, particularly with the
advent of 5G New Radio technologydel Peral-Rosado et al. (2018); Chettri and
Bera (2020) and future systems Kanhere and Rappaport (2021); Chen et al.
(2022). These developments have transitioned from conventional assistance of
positioning using signals of opportunity to fully integrated communication
systems Liu et al. (2022).

In this context, integrated communication systems encompass the ability to
provide both active, device-based localization and passive, device-free
sensing Shastri et al. (2022).

A plethora of survey papers on location-awareness and location capabilities in
IPS and mobile cellular networks are available Gu, Lo, and Niemegeers (2009);
Xiao et al. (2016); Tariq et al. (2017); Gonçalves Ferreira et al. (2017);
Laoudias et al. (2018); Zafari, Gkelias, and Leung (2019); Geok et al. (2020);
Shastri et al. (2022). Analyzing these works reveals a lack of a unified
taxonomy for defining location-aware communication systems. Instead, existing
works are typically driven by either technology or application considerations.
However, the abundance of research indicates that technologies, applications,
and regulations are subject to continuous evolution. Concerning the
establishment of a framework for spatial and geometric relations within a
radio network, abstracted unified measurement types for data acquisition are
necessary. These encompass measurement principles related to signal
characteristics (e.g., time-based), physical attributes (e.g., frequency
bands, bandwidths), and connection-related properties (e.g., network topology,
directionality) of communication technologies.

In light of the aforementioned aircraft cabin environment, several empirical
studies have been conducted to assess radio channel and propagation
characteristics Chuang et al. (2007); Jacob et al. (2009); Chiu and Michelson
(2010); Schulte et al. (2011); Neuhold et al. (2016); Cogalan, Videv, and Haas
(2017). These investigations also explored the localization capabilities of
various radio technologies, including Ultra-Wideband (UWB) Karadeniz et al.
(2020); Geyer and Schupke (2022), WiFi Wang et al. (2021), and 5G Cavdar et
al. (2018). These studies primarily aim to provide empirical insights into
radio propagation in the challenging aircraft cabin environment. However,
their focus on assessing localization capabilities have been somewhat limited.
Consequently, there remains a need for integrated communication systems that
facilitate precise location determination through radio-based positioning
within such environments.

Possible technologies for radio-based positioning in the aircraft cabin are
presented in Figure 1. These radio technologies differ by their center
frequency, bandwidth, used waveform and other signal parameters that affects
positioning performance. These technologies can be mainly categorized as
cellular (LTE, 5G NR), Wireless Local Area Network (WLAN), Wireless Personal
Area Network (WPAN), such as Bluetooth Low Energy (BLE), Zigbee, UWB and other
technologies.

Figure 1: Overview of IPS technology candidates for location-based services
within the connected cabin.

###  2.3 Radio Channel and Modeling

The performance of wireless communication and localization is heavily
dependent on the radio channel and various propagation effects. The three
basic propagation phenomena for the interaction of the radio wave with objects
in the environment are reflection, scattering and diffraction. These phenomena
lead to large-scale and small-scale channel characteristics of the radio
propagation channel including path loss, shadowing and multipath fading.

Existing channel models take these effects into account and could be
categorized into deterministic, stochastic, and hybrid approaches. Stochastic
models use statistical approximations in form of statistical distributions to
characterize the received signal and channel characteristics such as path
loss, delay spread, multipath components (MPCs) and the channel impulse
response (CIR). These models are based on measurements and empirical
observations in specific types of environments (rural, urban, indoor, micro,
macro, etc.). The channel is modeled at a random location of the sensor in a
defined type of environment, so there is no spatial consistency between
different sensor locations. Due to the low complexity of stochastic models and
the suitability to model a communication channel, there are used for example
for the 3GPP TR 38.901 channel model 3GPP (2019).

Hybrid channel models often extend stochastic models by taking the geometry
into account. For example, quasi-deterministic channel models compute the
dominant MPC with a highly simplified environment map and add clusters of
stochastically modeled MPCs to the model Han et al. (2022). For geometry-based
stochastic models the most widely used academic simulator is QuaDRiGa Jaeckel
et al. (2017).

Deterministic models approximately solve the Maxwell’s equations. The models
achieve high accuracy, but require detailed knowledge of the geometry of the
environment, the electromagnetic properties of the materials, and the spatial
position of the sensors. The propagation paths or rays are calculated based on
the channel characteristics and the position of reflectors in the environment.
But such models are very computationally intensive. The signal parameters of
every propagation path are usually computed using geometrical optics with two
different methods: ray tracing and ray launching. Han et al. (2022)

Deterministic channel models consider the desired environment and sensor
positions, thus there are spatial consistent. This spatial consistency is
crucial to evaluate localization with indoor positioning systems (IPS) in a
specific environment such as the aircraft cabin.

3D ray tracing computes all propagation paths or rays between a given
transmitter and receiver position by tracing back from the receiver to the
transmitter (cf. Figure 4). Various propagation phenomena and interactions,
including reflection, diffraction, and scattering, are taken into account. By
tracing the rays, the electrical lengths, amplitudes, and phases of the
individual propagation paths is calculated. Thus, other channel
characteristics, such as signal energy, delay spread, and the CIR can be
determined. Ray launching on the other hand sends rays in all directions from
the transmitter and looks which ones survive and arrive at the receiver. This
procedure has a positive effect on the calculation time compared to ray
tracing, especially in scenarios with one transmitter and many possible
receiver positions, for example in the form of a grid. For this reason, we
used ray launching as deterministic algorithm to compute all propagation paths
and estimation of the signal parameters for our simulation toolchain presented
in section 3.

##  3 Methodology

###  3.1 Overview

This work proposes a radio-positioning simulation approach, which aims at
providing an universal and adaptable environment for investigating operational
applications enhanced by location information within the connected cabin. As
discussed before, for this application a simulation approach is required,
which addresses the shortcomings of conventional investigations and simulation
approaches concerning radio-based positioning. In this light, we present three
main pillars, which constitute a framework for application-driven location-
based services within the cabin. At first, operational requirements need to be
addressed. This includes different use cases, the types of locatable objects
and functional respectively quantitative requirements. The operation
simulation allows the derivation of reference states from the operational
inputs, e.g. a boarding sequence, where each PAX is associated to a reference
position at each time step. Based on this, a scene-specific deterministic
radio prapagation simulation can be performed, allowing to precisely derive
channel information (e.g. reflections or LOS/NLOS) with respect to a specific
environment and the operational inputs. This approach enables a spatial
consistency of the radio simulation. However, only channel specific influences
are considered. Lastly, a localization module needs to be implemented, which
derives location information of the objects of interest and can represent both
conventional state estimation or even data-driven approaches. Figure 2
summarizes a high-level overview on the embedding of the location simulation
and how it can be incorporated within an operational scenario, while
highlighting the pillars of the approach.

Figure 2: High-level flowchart of the proposed radio localization simulation
approach.

The main scope of the presented work is the adaptation of radio propagation
simulation (as shown in ivory in Figure 2). The developed approach utilizes
both deterministic ray-tracing for spatial scene analysis, which is
conventionally applied for network coverage analysis, and adaptive stochastic
error sampling, partially based on empirical findings. In contrary to
previously mentioned works, this spatial consistency is achieved by adopting
the observability results from deterministic simulation, here Line-of-Sight,
Obstructed Line-of-Sight or Non-Light-of Sight (Los, OLOS, NLOS) along
additional parameters such as the channel impulse response or the delay
spread. The obtained visibility constellations are then again used to further
consider stochastic error influences with specific noise and error
distributions for each propagation case as detailed in Section 3.3. Based on
these inputs, different localization methods can be trained and applied.

Since UWB-based positioning systems have previously been discussed for
applications within the connected cabin, e.g. Geyer and Schupke (2022), we
will also apply our simulation to the specifics and physical layer properties
of UWB. In unison, we apply the principle of two-way-ranging as specified
within the UWB standard Karapistoli et al. (2010).

While not being the main focus of the work, we still present the incorporation
of both operational simulation and localization methods (orange and green in
Figure 2) in order to emphasize the overall toolchain and interdependent
principles of each pillar.

###  3.2 Radio Propagation Simulation

For deterministic radio propagation simulation, various inputs and
prerequisites to the modeling are necessary in order to subsequently perform
the simulation based on the special propagation models and to derive the
necessary channel parameters Ußler et al. (2022). Figure 3 shows the
processing for the deterministic radio propagation simulation to compute the
visibility states (LOS/OLOS/NLOS) for the probabilistic sampling and the
localization.

Figure 3: Processing for the radio propagation simulation with inputs,
predictions models and results.

The inputs for the simulation include a detailed environment model that
includes both the geometry of the objects and their electromagnetic
properties. For this purpose we used a CAD model of the aircraft cabin with an
associated database of material properties for different frequencies. The CAD
model is an Airbus A320 including all seats, luggage compartments, but no
actual PAX (cf. Figure 4). The electromagnetic properties include the relative
permittivity, relative permeability and roughness of the various surfaces and
objects in the model. From these properties it is possible to calculate the
attenuation of the reflected wave and the loss due to transmission through the
object with a defined thickness. Ußler et al. (2022)

(a)

(b)

(c)

Figure 4: CAD-Model of the Airbus A320 aircraft cabin: (a) exterior, (b)
interior, (c) exemplary result of 3D ray-tracing.

The radio model consists of the signal parameters of the desired radio
technology, the specification of the antenna and other simulation parameters
that parameterize the computation method. The radio technology is specified by
a channel, the associated center frequency and the transmit power. The
modeling of the antenna is most accurately done by a 3D antenna pattern. From
the antenna pattern, the antenna gain, the half-power beam width and the size
the sidelobes can be obtained. Further parameters of the simulation include
the maximum number of reflections, transmissions and diffraction per ray.
Also, the specified sensitivity of the receiver determines the termination of
the calculation of individual propagation paths.

Another specification for modeling the radio link is the location of the
transmitter and receiver antennas. All propagation paths or rays are computed
between the respective locations bases on the channel model and used
prediction method. Transmit antennas are normally located at a fixed position,
where as in our cases the position of the receiver antenna is varying within
an equidistant grid. Thus, the visibility results and channel parameters are
calculated for all receiver locations in the grid and are available for
localization methods.

Ray launching provides different results and channel parameters, which are
based on the calculation of the propagation paths with their respective
length, amplitude and phase Yun and Iskander (2015). These signal and channel
parameters are listed in Table 1 and are conventionally used to plan and
optimize the performance of communication networks, however can also be
applied for radio-based localization systems.

Table 1: Radio channel simulation results, which fully characterize the propagation channel and are required for the localization methods. Results |  Description
---|---
Delay |  Delay of the respective ray.
Delay Spread |  Delay spread of all propagation paths either as Excess Delay Spread or RMS Delay Spread.
Path loss |  Attenuation of the propagation path due to free-space path loss, reflections, scattering, diffraction, etc.
Power / Field strength |  Calculated power and field strength at receiver based on the path loss of all propagation paths.
LOS/NLOS conditions |  Differentiation whether there is a LOS, OLOS or NLOS condition between transmitter and receiver.
Interactions with objects |  Location of interaction and type of interaction (reflection, scattering or diffraction) of the propagation path with objects.
Channel impulse response (CIR) |  Delays and amplitudes of individual propagation paths plotted in the form of a power delay profile (PDP) with Dirac pulses.

###  3.3 Ranging Simulation

As previously mentioned, the summarized parameters in Table 3 can be further
used for additional error sampling simulation. Specifically, stochastic
sampling based on a semi-empirical simulation model has been extensively
studied in previous works by examining the ranging capabilities of UWB in a
harsh environment Schwarzbach, Weber, and Michler (2022). In accordance, a
collaborative ranging simulation in a smart parking environment has been
performed using this approach Jung, Schwarzbach, and Michler (2022).
Therefore, the semi-empirical sampling approach is only briefly outlined below
and will be further enhanced in the following sections by combining it with
radio propagation simulation. Unlike aforementioned works, this paper enhances
the semi-empirical approach with the previously introduced deterministic scene
analysis and therefore allows more spatial consistency of the preserved
observation data. The necessary steps for this task are briefly depicted in
Figure 5.

Figure 5: High-level error sampling procedure.

The reference position are given from the operational scenario to be examined
(cf. Figure 2) and can either be time-variant or time-invariant. For each
measurement step (epoch), two parameters can be derived based on the reference
position and given infrastructure:

  * •

Reference distance $\boldsymbol{d}$, which is defined as the 3D distance
between the reference position $\boldsymbol{\mathrm{x}}_{\text{ref}}$ and the
position of available anchors $\boldsymbol{\mathrm{x}}_{a}$:
$\boldsymbol{d}=\left\|\boldsymbol{\mathrm{x}}_{a}-\boldsymbol{\mathrm{x}}_{\text{ref}}\right\|_{2}$.

  * •

Observability analysis to each available infrastructure node is estimated from
the given radio propagation simulation.

For the latter there are generally four propagation phenomena observable Qi,
Kobayashi, and Suda (2006), of which three are illustrated in Figure 6:

  * •

LOS: The signal is received via the direct path between transmitter and
receiver (cf. Figure 6(a)).

  * •

OLOS: The signal is still received via the geometrical direct path, however it
has penetrated at least one structure on its way (cf. Figure 6(b)).

  * •

NLOS: The direct signal is blocked in this case, however the signal still
received via a reflected path (cf. Figure 6(c)).

  * •

Not receivable: Given the propagation environment and the radio model, the
signal attenuation can exceed the receiver sensitivity and therefore lead to a
signal not being receivable at a given location. This effect is typically
caused by high distances or many obstructing objects between transceivers.

(a) LOS

(b) OLOS

(c) NLOS

Figure 6: Schematic examples of different radio propagation phenomena.

In addition to these four propagation phenomena derived from radio propagation
simulation, we additionally consider effects which are not induced by the
propagation environment. At first, we consider general measurement noise
modeled as additive white gaussian noise in order to quantify the overall
precision of the measurement technology. These intrinsic factors are hardware-
related and are empirically quantifiable Schwarzbach, Weber, and Michler
(2022). Secondly, gross errors, also refered to as outliers, are substantial
derivations between true and observable values. These effects can for example
be caused by target confusion, network errors or electromagnetic
interferences.

Given the aforementioned propagation scenarios and these additional error
influences, we propose to model a ranging measurement ${r}$ as follows Qi,
Kobayashi, and Suda (2006); Schwarzbach, Weber, and Michler (2022):

$\displaystyle r=\begin{cases}d+{\text{\char 101}}&\text{if
}p>\dfrac{d}{d_{\mathrm{max}}}\\\ \emptyset&\text{else,}\end{cases}$ (1)

where the observation constitutes the true distance $d$ and the additive
errors . The error terms  exhibit a linear dependence on distance, relative to
an empirical maximum range denoted as $d_{\text{max}}$. The choice of this
range is application and technology-specific. When the prescribed condition is
not satisfied, we simulate a measurement failure denoted as $\emptyset$.
Furthermore, the probability $p$ for occasional disturbances in measurements
is represented as a Bernoulli experiment with $p\sim\mathcal{U}(0,1)$.
Empirical evidence from our previous study Schwarzbach, Weber, and Michler
(2022) has substantiated the presence of distance-dependent characteristics in
errors. This phenomenon, in turn, exerts an influence on the success rate of
simulated measurements.

Depending on the observed propagation scenario, we sample our scenario-
specific ranging error following the decision tree given in Figure 7, in which
we introduce the following quantities:

  * •

${\text{\char 101}}_{\text{LOS}}$, ${\text{\char 101}}_{\text{OLOS}}$ and
${\text{\char 101}}_{\text{NLOS}}$ represent the ranging error for LOS, OLOS
and NLOS scenarios respectively;

  * •

$\mathcal{N}$ and $\mathcal{LN}$ denote a normal respectively a log-normal
distribution;

  * •

$\sigma_{r}^{2}$ represents the noise-related error term;

  * •

$\overline{R}_{r}$ and $\sigma_{\overline{r}}^{2}$ represent the location and
scale parameter of a $\mathcal{LN}$ distribution.

LOS${\text{\char 101}}_{\text{LOS}}\sim\mathcal{N}(0,\sigma_{r}^{2})$
OLOS${\text{\char
101}}_{\text{OLOS}}\sim\mathcal{N}(0,2\sigma_{r}^{2})$NLOS${\text{\char
101}}_{\text{NLOS}}\sim\mathcal{LN}(\overline{R}_{\overline{r}},\sigma_{\overline{r}}^{2})$

Figure 7: Error sampling decision highlighting the underlying stochastic
models.

In addition, an empirical outlier probability $p_{\text{out}}$ is defined,
which accounts for gross outliers. Outliers characterize the robustness of the
measurement system and can be caused by a variety of influences, such as
measurement confusions Schwarzbach, Weber, and Michler (2022). The quantity of
this variable is again dependent on the environment and the measurement
technology. Outliers are generally a major source for localization errors,
especially in demanding scenarios.

Therefore, a proper simulation of them is essential. Since outliers typically
do not follow a generic stochastic model, we assume a uniform error
distribution within physically measurable domain. Therefore outliers  are
additionally modeled as:

${\text{\char 101}}_{\text{OUT}}\sim\mathcal{U}(-d,d_{\mathrm{max}}-d).$ (2)

Further details on the stochastic sampling approach are given in Schwarzbach,
Weber, and Michler (2022).

In summary, the following error types are considered within the simulation:
LOS noise, OLOS noise, NLOS right-skewed noise, uniformly distributed outliers
with respect to an outlier probability and measurement failures with respect
to signal attenuation (maximum range) and random failures. Resulting overall
error (residual) distribution derived from the presented simulation approach
are presented in Section 4.

###  3.4 Positioning

In literature, a variety of applicable state estimation approaches for radio-
based positioning exist. Most commonly, these can be divided in the following
categories: Deterministic approaches use explicit observables and calculate
geometric relationships between fixed infrastructure and mobile devices on
this basis Stojmenović (2005). In addition, probabilistic approaches also use
the aforementioned observables, but consider the system states as stochastic
random variables and describe them as well as the observables by means of
probability density functions Chen (2003). Lastly, the localization problem
can also be solved by applying data-driven localization methods derived from
machine learning and artificial intelligence Burghal et al. (2020).

Since the focus of the paper is on highlighting the simulation procedure in
order to enable positioning applications (cf. Section 3), this manuscript will
not further elaborate applicable state estimation methods, but rather build up
on existing frameworks allowing a discussion of the simulated effects on the
localization process.

For dynamic systems, Recursive Bayes’ Filter (RBF) pose a common framework for
localization problems, including Kalman, Histogram or Particle Filters Chen
(2003). In this work, we apply a pre-defined, equidistant grid representation
of the state space, representing possible locations and allowing a multi-
dimensional implementation of an Histogram Filter Thrun, Burgard, and Fox
(2005) or a point-mass filter Bucy and Senne (1971). This representation is
applied for the given application because of two reasons. This non-paramtric
filtering approach provides inherent robustness to non-gaussian error types
compared to parametric filtering. With multiple locatable objects within the
confined cabin space, the grid can be used as a uniform state space for all
objects. When presented with various objects within the network, this approach
reduces computational load compared to, for example, a Particle Filter.

Since this localization methods has been previously detailed for several
radio-based localization problem, we only briefly outline the necessary
calculation steps and refer to previous works for implementational details
Schwarzbach, Michler, and Michler (2020); Jung, Schwarzbach, and Michler
(2022); Schwarzbach, Michler, and Michler (2023).

For this method, also refered to as Grid Filtering, the domain of the state
space representing realizations of the state vector $\boldsymbol{\mathcal{X}}$
is decomposed in a discrete and finite set of $M$-equidistant realizations
$X_{M}$:
$\text{dom}(\boldsymbol{\mathcal{X}})=\boldsymbol{X}_{1}\cup\boldsymbol{X}_{2}\cup\dots\boldsymbol{X}_{M}$.

Given this state space initialization, the Grid Filter follows the general
iterative RBF structure depicted in Figure 8, where the corresponding
calculations are given in Equations 3, 4 and 5 Thrun, Burgard, and Fox (2005).

Initialization $\text{dom}(\boldsymbol{\mathcal{X}})$ Prediction
$\overline{\boldsymbol{p}}_{t}$ Observation $\boldsymbol{p}_{t}$ Observations
Estimation $\boldsymbol{\hat{\mathcal{X}}_{t}}$ $t=0$$t=1:T$

Figure 8: General RBF structure.

$\displaystyle\overline{\boldsymbol{p}}_{t}$
$\displaystyle=\boldsymbol{p}_{t-1}\sum_{m}\boldsymbol{P}(\boldsymbol{\mathcal{X}}_{t,m}|{\boldsymbol{\mathcal{X}}}_{t-1})$
(3) $\displaystyle\boldsymbol{p}_{t}$
$\displaystyle=\eta\;\overline{\boldsymbol{p}}_{t}\sum_{m}\boldsymbol{P}(\mathcal{\boldsymbol{\mathcal{Z}}}_{t}|\boldsymbol{\mathcal{X}}_{t,m})$
(4) $\displaystyle\boldsymbol{\hat{\mathcal{X}}}_{t}$
$\displaystyle=\text{argmax}\;\boldsymbol{p}_{t},$ (5)

where:

  * •

$\overline{\boldsymbol{p}}_{t}$ denotes the predicted belief following a
motion model at timestep $t$,

  * •

$\boldsymbol{p}_{t}$ denotes the resulting belief using the given observations
modeled via

  * •

$\boldsymbol{P}(\mathcal{\boldsymbol{\mathcal{Z}}}_{t}|\boldsymbol{\mathcal{X}}_{t,m})$,
which denotes the observation Likelihood with respect to the normalization
constant $\eta$

  * •

$\boldsymbol{\hat{\mathcal{X}}}_{t}$ denotes the state estimation derived from
maximizing the current belief.

Figure 9 depicts an exemplary output of the applied state estimation
framework. Here, the given state space is shown as the - mostly - purple
discrete grid. Reference points (red) perform faulty ranging measurements
(grey) in accordance with Section 3.3, which influence the grid likelihood as
indicated by the yellow areas, which represent a higher likelihood. Finally,
the reference position is indicated in black.

Figure 9: Exemplary output of the observation step of the Grid Filter using
faulty ranging measurements. Anchors are depiced in red, reference point in
black. The discrete state space is represented by an equi-distant grid
(purple), where the likehoods of ranges (grey) are color-coded.

##  4 Simulation and Results

###  4.1 Simulation Overview

In accordance with previous works, we constitute our simulation within an
Airbus A320 model Schultz (2018c); Schwarzbach et al. (2020). In total, three
scenarios are covered as summarized in Table 2.

Table 2: Test cases on testbed for automated and connected driving. | Name | Description | Samples
---|---|---|---
I | Static, above seat | Antenna height 112cm | 5.000 positions, 40.000 ranges
II | Static, below seat | Antenna height 70cm | 5.000 positions, 40.000 ranges
III | Dynamic, 148 PAX | Boarding process | 40.184 positions, 321.472 ranges

At first, two static scenarios are examined in order to achieve a reproducible
scenario for different receiver heights. Scenario I refers to a receiver
height slightly above the seats within the cabin, this generally leads to a
high percentage of LOS propagation scenarios, while scenario II applies a
receiver height indicated a position below the seat, e.g. for the localization
of life vests Ninnemann et al. (2022). Both scenario I and II refer to an
empty cabin without PAX. Scenario III then covers an entire boarding process,
as previously described in Schultz (2018a). In total, 148 PAX are incorporated
in the boarding sequence, which are continuously tracked during the process.

Concerning the radio propagation simulation, ray launching is used to
calculate radio parameters for all possible receiver locations within the
cabin represented by an equi-distant grid. To limit the computational effort
and time the maximum number of interactions with objects is set to two and the
grid of possible receiver positions has a resolution of
$10\text{\,}\mathrm{cm}$. The simulation is performed with the software Altair
WinProp 2022.3 Altair Engineering Inc. (2023). Table 3 lists the parameter set
used for the radio propagation simulation in the aircraft cabin.

Table 3: Parameters and Inputs of the Radio Propagation Simulation with Altair WinPop 2022.3. |  Parameter |  Value
---|---|---
Geometric Model |  CAD Model |  Airbus A320
Radio Properties |  Center Frequency |  $6500\text{\,}\mathrm{MHz}$ (UWB Channel 5)
TX Power |  $-14.7\text{\,}\mathrm{dBm}$
Antenna |  Omidirectional Antenna
Position of Anchors [$\mathrm{m}$] |  A1: -1.36;11.11;1.50 A2: 1.37;13.77;1.50 A3: -1.33;16.55;1.50 A4: 1.33;19.28;1.50 A5: -1.36;21.99;1.50 A6: 1.33;24.69;1.50 A7: -1.32;27.41;1.50 A8: 1.35;30.06;1.50
Simulation Parameters |  RX Grid Resolution |  $10\text{\,}\mathrm{cm}$
RX Prediction Plane Heights |  $70\text{\,}\mathrm{cm}$ and $112\text{\,}\mathrm{cm}$ above floor
Prediction Model |  Ray Launching
Max. # of Reflections |  2
Max. # of Transmissions |  2
|  Max. # of Diffractions |  0

###  4.2 Visibility Analysis

The first result received from the deterministic radio simulation is the
observability between anchors and the reference positions. In order to
illustrate these constellations, the visibility states for scenario I and II
are compared in Figure 10. For each anchor indicated in black, the observation
state throughout the grid is given by the respective color.

Figure 10: Visibility Analysis provided by radio propagation simulation:
Scenario I (left) and scenario II (right) for the entire reception plane. The
visibility states are color-coded with respect to the given colorbar.

Given the results in Figure 10, the difference in relative antenna height
between anchors and the reception plane is apparent. While scenario I above
the seats comprises LOS reception in an empty cabin, scenario II constitutes
hardly any LOS states due to the obstruction of objects between anchors and
the reference plane. For scenario II, we also considered a distant-dependent
threshold between OLOS and NLOS observations, as additional signal attenuation
due to longer travel distances for the OLOS path would lead to a higher
likelihood for NLOs reception. In the given case, this threshold was
empirically set to $6\text{\,}\mathrm{m}$.

###  4.3 Ranging simulation

Based on the derived results from the visibility analysis, the stochastic
error sampling as detailed in Section 3.3 is performed. In order to provide
insight on the different visibility states, Figure 11 depicts the ranging
residuals (measurement errors) for each individual scenario. In order to
highlight the underlying data, the corresponding quantities are briefly
outlined in Table 4, which further highlights the presence of both measurement
failures in the confined cabin area ($21.4\text{\,}\mathrm{\char 37\relax}$ to
$22.5\text{\,}\mathrm{\char 37\relax}$ across all three scenarios) as well as
the presence of measurements outliers, which are not explicitly shown in
Figure 11 due to readability.

Figure 11: Residuals for simulated scenarios. Values are clipped at $[-5,15]$ m. Table 4: Qunatities of ranging measurements for each scenario Szenario | Total Measurements | Valid Measurements | Depicted Measurements
---|---|---|---
I | $40.000\;($100\text{\,}\mathrm{\char 37\relax}$)$ | $31.437\;($78.6\text{\,}\mathrm{\char 37\relax}$)$ | $30.269\;($75.7\text{\,}\mathrm{\char 37\relax}$)$
II | $40.000\;($100\text{\,}\mathrm{\char 37\relax}$)$ | $31.469\;($78.7\text{\,}\mathrm{\char 37\relax}$)$ | $28.353\;($70.9\text{\,}\mathrm{\char 37\relax}$)$
III | $322.376\;($100\text{\,}\mathrm{\char 37\relax}$)$ | $233.569\;($72.5\text{\,}\mathrm{\char 37\relax}$)$ | $224.683\;($70.0\text{\,}\mathrm{\char 37\relax}$)$

It is observable, that the measurement residuals follow distinct distributions
influenced by the simulated scenarios. Scenario I constitutes a majority of
LOS reception (as indicated by Figure 10), while still being prone to
environmentally unrelated outliers and measurement failures. In contrast,
scenario II is prone to a majority of comparably higher noise due to OLOS as
well as manifold NLOS receptions. Lastly, caused by the presence of additional
obstacles within the propagation environment, scenario III reveals a mix of
the two previous scenarios.

###  4.4 Positioning results

At last, the discussion of derivable positioning results based on the
introduced positioning method from Section 3.4 is presented. Figure 12 depicts
the qualitative localization results for the simulated scenarios. Furthermore,
a quantitative analysis of the positioning results is conducted. For this the
$\lvert\lvert\cdot\rvert\rvert_{2}$ norm between the estimated and reference
positions is utilized as error metric $\mathcal{Q}$ 18305:2016 (2016). For
this matter, Table 5 summarizes relevant statistical measures for the
positioning accuracy, while Figure 13 depicts these measures as well as an
empirical cumulative density function of the positioning error. The comparison
of scenario I and II in Figure 12 clearly reveals the influence of different
measurement error distributions on the positioning results, as the right-
skewed error distribution in scenario II also leads to a higher scattering of
positioning results. This is also supported by Figure 13, where scenario I
yields an average positioning error of
$\overline{\mathcal{Q}}_{\text{I}}=$0.17\text{\,}\mathrm{m}$$ compared to
$\overline{\mathcal{Q}}_{\text{II}}=$0.34\text{\,}\mathrm{m}$$ for scenario
II. The overall descriptive statistics for all scenarios are summarized in
Table 5, including average $\bar{\mathcal{Q}}$ (root mean square error),
median $\tilde{\mathcal{Q}}$ and the error variances
$\sigma_{\mathcal{Q}}^{2}$. In addition, the $\sigma$ quantiles and the
$25,50,75$ percentiles of the error distributions are given.

Figure 12: Qualitative positioning results for each simulation scenario:
Scenario I and II represent the static receiver position at different heights,
while for scenario III the trajectory of one exemplary PAX is depicted.

(a)

(b)

Figure 13: Statistical evaluation of the positioning error for all three scenarios a) Raincloud plot and individual error points. b) empirical cumulative density function (ECDF). Positional errors are clipped at $5\text{\,}\mathrm{m}$. Table 5: Quantitative evaluation scenario I, II and III. Measure | $\bar{\mathcal{Q}}$ | $\tilde{\mathcal{Q}}$ | $\sigma_{\mathcal{Q}}^{2}$ | Quantile $[$\text{\,}\mathrm{m}$]$ | Percentile $[$\text{\,}\mathrm{m}$]$
---|---|---|---|---|---
$[$\text{\,}\mathrm{m}$]$ | $[$\text{\,}\mathrm{m}$]$ | $[$\text{\,}\mathrm{m}$]^{2}$ | $\sigma$ | $2\sigma$ | $3\sigma$ | $25$ | $50$ | $75$
I | $0.17$ | $0.16$ | $0.01$ | $0.20$ | $0.35$ | $0.45$ | $0.10$ | $0.16$ | $0.23$
II | $0.34$ | $0.31$ | $0.04$ | $0.40$ | $0.69$ | $0.93$ | $0.19$ | $0.31$ | $0.44$
III | $0.29$ | $0.21$ | $0.69$ | $0.28$ | $0.54$ | $0.89$ | $0.13$ | $0.21$ | $0.31$

Furthermore, scenario III is briefly discussed. Figure 12 reveals clustering
points in different areas, where the PAX has to wait before moving on due to
other PAX. These occurrences can be monitored for operational planning and
turnaround estimation respectively the optimization of the overall boarding
strategy.

While Figure 12 only depicts the estimated trajectory for one PAX, the given
statistical measures include the positional errors for all 148 simulated PAX
during the boarding sequence. From the available results, it can be inferred
that the presence of PAX within the cabin has an additional negative impact on
visibility, and consequently, on ranging accuracy.

##  5 Conclusion & Research Directions

Radio propagation simulation is an ubiquitous tool in various engineering
fields, predominantly used for coverage and network analysis. However, it also
provides numerous advantages for radio-based location applications. However,
single-handedly applied deterministic, empirical and stochastic models each
have dedicated disadvantages for simulating the necessary inputs for radio-
based localization. Therefore, this paper proposes the integrated combination
of the aforementioned approaches in order to provide spatially consistent yet
enabling alternating residual distributions. Due to the adaptability of both
the deterministic and the stochastic simulation components, different
scenarios and error sampling distributions can easily be applied and assessed.
The framework also enables a cheap setup optimization, for example geometrical
layout of anchors, and parameter tuning, for example for outlier detection.

The implemented simulation toolchain can be used not only to evaluate the
performance of localization algorithms based on visibility states but is also
scalable in terms of evaluation opportunities. These opportunities encompass
assessing other signal parameters, such as RSSI, Delay, or CIR (as shown in
Table 1), and evaluating different propagation environments and radio
technologies, including specific antenna hardware.

In summary, the presented results encompass three scenarios, each yielding
different visibility states, resulting in demanding and partially right-skewed
error distributions based on the proposed sampling distributions. This further
underscores the need for robust positioning and effective outlier mitigation
strategies.

We want to further emphasize, that the paper only covers one specific network
constellation, one radio technology and a specific distance measurement
principle. Derived from that, a fixed set of proposal distributions and one
localization scheme is presented. However, due to the nature of the toolchain,
manifold of the expects can be altered and compared. This also allows the
simulation toolchain to be applied for the implementation and evaluation of
data-driven localization approaches and machine leaning in general by
addressing the data generation bottleneck. Based on the channel model the
signal parameters used for ranging and simulation could be obtained by the
deterministic radio propagation simulation with ray tracing or ray launching
for a desired environment and use case Michler et al. (2023). This also aligns
with research directions for the newest generation of radio positioning
systems Schwarzbach, Ninnemann, and Michler (2022).

## Funding

This work has been funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) following a resolution of the German Federal Parliament within the projects CANARIA (FKZ: 20D1931C) and INTACT (FKZ: 20D2128D). |
---|---

## Dataset

The dataset consist of three .csv files containing the visibility analysis
from ray launching, the anchor states and the results of the probabilistic
range sampling. The file `visibility.csv` contain visibility states inside a
grid with resolution of $10\text{\,}\mathrm{cm}$ of the two simulated heights
in the cabin. The columns of the file include the following data:

  * •

`x`: position of the predicted grid cell in x.

  * •

`y`: position of the predicted grid cell in y.

  * •

`height`: height of the simulated prediction plane. Is used to investigate the
viability of the tag for two different height in the cabin.

  * •

`anchor_id`: ID of the anchor for which the visibility is specified.

  * •

`visibility`: computed visibility state (`LOS=1`/`OLOS=2`/`NLOS=3`) based on
the radio propagation simulation with ray launching.

The second .csv file includes the sampled ranges based on the observability
and is named `ranges.csv`. Each line of the file contains the following data:

  * •

`ref_pos_x`: static reference position of the PAX in x.

  * •

`ref_pos_y`: static reference position of the PAX in y.

  * •

`height`: height of the used prediction plane.

  * •

`anchor_id`: ID of the anchor for which the range is sampled.

  * •

`range`: sampled range between the anchor and the tag at the PAX. This range
is used in the localization module to estimate the position of the PAX based
on the simulated radio channel characteristics and visibility state from the
radio propagation simulation.

Additionally the 3D position of the anchors in the cabin are provided in
separate file called `anchors.txt`. The dataset is open-access available to
download on the research data repository OpARA of Dresden University of
Technology: The submitted data is still under review at the research data
management platform. The data has temporarily been stored at:
https://github.com/PaulSchwarzbach/ewgt_2023.git.

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