text: 1. Field of the Invention
  The invention concerns radar systems, in particular, radar systems which seek to identify targets in clutter, especially where the targets are of low radar cross section.
  2. Related Art
  Airborne long range radar surveillance or fire control requires a radar system to acquire, track and identify targets. These targets can severely stress radar detection, tracking and identification capabilities if the target has a small radar cross section (RCS) and if the target return competes with land or sea clutter. Previous approaches for upgrading such radars have applied increased power aperture, waveform design and adaptive array strategies. Although other efforts have investigated advanced signal processing techniques such as space-time processing, such approaches have extremely high signal processing demands.
  Small RCS targets flying near the earth's surface are the stressing case for airborne and shipborne radars because such targets are easily obscured by land and sea clutter. A target's low RCS makes its radar return much weaker than the background clutter and comparable to returns from false targets such as birds and ground moving targets. Mainbeam and sidelobe clutter returns conceal low RCS targets. Thus, increased target-to-clutter ratio is required for detection. An associated problem is the need to suppress ground clutter sufficiently without attenuating target signal. To successfully detect and track targets, an airborne or shipborne surveillance radar typically employs such techniques as Doppler filtering moving target indicator (MTI) approaches, analog or digital bandpass filtering, amplitude weighting and range gating to suppress the clutter.
  FIG. 1 illustrates one scenario. An airborne platform 101 carries a radar operating in a look-down mode. In this mode the radar experiences severe clutter returns, for example from the ground, ground moving target 102 and other sources. These clutter returns increase the difficulty in detecting a small RCS target 103.
  FIG. 2a is a block diagram illustrating conventional processing in a moving target indicator (MTI) radar. The output of a phase detector 201 is sampled sequentially by a plurality of range gates 203. Each range gate opens in sequence just long enough to sample the voltage of the video waveform corresponding to a different range interval in space. The range gate acts as a switch which opens and closes at the proper time and is activated by a controller (not shown) once each pulse repetition interval (PRI). An echo from a moving target produces a series of pulses which vary in amplitude according to the doppler frequency. The output of the range gate may be stretched in a boxcar generator circuit 205 to aid in the filtering and detection process by emphasizing the fundamental of the modulation frequency and eliminating harmonics of the pulse repetition frequency. A clutter rejection filter 207 is a bandpass filter whose bandpass depends on the extent of the clutter spectrum, but is typically less than one half the pulse repetition frequency. Full wave linear detector 209 and integrator 211 provide unipolar video to threshold detector 213. Only signals exceeding the threshold are reported as targets.
  FIG. 2b is a more detailed illustration of typical signal processing elements in a conventional medium pulse repetition frequency (PRF) radar system. The elements shown can be implemented as individually assembled circuits, as combinations of hardware and software executed and controlled by a processor carrying out a program or by a system fully implemented in software. In phase and quadrature information is applied to analog detector 220 to generate a digital signal, for example a ten bit plus sign signal, to a range gate 222. The output of range gate 222 is applied to clutter suppression filter 224, such as a three pulse delay line canceler with binary weights. The output of the delay canceler is applied to filter 226, such as the 40 dB Dolph Chebchev weighting filter shown to reduce clutter residue. A fast Fourier transform is then performed, as shown in block 228 and the sum of the squares of the in phase and quadrature signals formed as shown in block 230. As shown in block 232, using a moving average of range cells, for example eight range cells, two guard cells and the cell of interest, an estimate of the background noise is formed. Circuitry to perform doppler and range cell blanking 234 is used to form a doppler notch to remove ground moving targets and a range notch to eliminate sidelobe discretes. Threshold comparison circuitry 236 is set for a desired false alarm rate. For systems with multiple PRFs, circuitry in block 240 is used to determine if a requisite number of thresholding crossings, e.g. three crossings in eight PRFs, have occurred. If so, a detection is indicated.
  FIG. 2c illustrates typical signal processing elements in a high PRF radar. These elements are similar to those of a medium PRF radar with parametric differences as shown. In addition, another difference is the substitution of a digital high pass filter for the MTI filter used in the medium PRF radar of FIG. 2b. FIG. 2c also includes a doppler cell blanking processing element in which only selected doppler cells are examined for the presence of target returns.
  Conventional airborne medium pulse repetition interval (PRF) radars typically process a radar return as depicted in FIG. 3a. Mainbeam clutter must be reduced or eliminated prior to signal detection. In an airborne radar, the mainlobe clutter must be centered at zero doppler frequency by clutter tracking circuitry 301 before being processed by the Moving Target Indicator 302.
  Amplitude weighting circuitry 303, which is also used as part of the estimation and detection process, reduces clutter residue effects and reduces the interference caused by large returns spilling over into adjacent doppler cells containing small returns. The amplitude weighting circuitry typically employs a Taylor, Kaiser or Dolph-Chebychev filter. Weighting increases processing losses, lowering the target signal to noise ratio, and correlates background interference across all Doppler filters. Spectral estimation circuitry 304 then typically employs FFT techniques to transform time domain information into the frequency domain in order to further discriminate targets and clutter using Doppler frequency differences. In a radar system target detection is performed by forming the ratio of a signal estimate to a background estimate and then comparing this ratio to a threshold. The signal estimate is formed by taking the power output from range doppler cells under consideration. The background estimate may be obtained by averaging the power from cells which are within W Hz of the target doppler and R nmi of the target range. Nonhomogeneous clutter present cells close to, but outside of W Hz and R nmi, bias the estimate of the background, i.e., cause a non-random inaccuracy. This phenomenon occurs because of the inevitable existence of frequency or range sidelobes in the response of the radar processor. The sidelobes may be reduced in magnitude by applying a window to the data before Fourier transformation, and thereby lessening the bias. This is well known to those of ordinary skill in the art.
  In addition to the above, however, it is also well known that such windows lead to a loss of energy that increases the variance of both the signal estimate and the noise estimate. This degrades the probability of detection and/or increases the probability of a false alarm. The prior art discloses that this trade-off between lessening the bias or decreasing the variance of a signal estimate may be avoided through the computation of K eigenspectra, where K is an application specific number. Each of the K eigenspectra are computed by applying one of K windows to the data and then computing the Fourier transform. The windows that are employed are drawn from the family of prolate spheroidal sequences. Prolate spheroidal window sequences have been applied to radar processing in the past, but only the first order window has been used, as opposed to windows of order 1 through K that are employed by other prior art sources. The optimal spectral estimate of the signal and its variance are computed by a formula disclosed in the prior art that involves weighted sums of the eigenspectra.