We consider the problem of joint tracking and classification using the information from radar and electronic support measure. For each target class, a separate filter is operated in parallel, and each class-dependent filter is implemented by interacting multiple model regularized particle filter. The speed likelihood for each class is defined using a priori information about speed constraint and combined with the likelihoods from two sensors to improve tracking and classification. Moreover, the output of classifier is also used for particle reassignment of different classes, which might lead to better performance. Simulations show that our proposed method can provide reliable tracking and correct classification. joint tracking and classification point-target-motion-model based JTC rigid-target-motion-model based JTC electronic support measure transferable belief model particle filter regularized particle filter interacting multiple model interacting multiple model regularized particle filter IMMRPF based JTC probability density mass function root-mean squared errors posterior joint state-class probability density mass function (pdmf) target state vector ith target type known number of target classes measurement sequences from radar and ESM measurement at time k from radar and ESM flight envelope constraints for class i set of maneuver models for class i target Markovian maneuver transition matrix for class i target Gaussian white process noise vector with zero mean and variance Q state transition matrix gain matrix additive Gaussian white noise with zero mean and variance R set of all possible emitters, where N is the total number of emitter types set of on-board emitters of class i target number of the on-board emitters of class i target ESM measurement space, where m = 2 N ESM confusion matrix usage transition probability matrix for emitter j overall emitter usage transition matrix for class i target with emitter set ε i posterior target class probability using ESM data only emitter usage status vector of class i target posterior target class probability using radar data only jth model probability of class i target at time k likelihood for jth model probability of class i target at time k speed likelihood functions speed feature measurement initial class probabilities number of particles for class i target at time k initial number of particles for class i indicator function effective sample size for class i target preset re-sampling threshold parameter hybrid particle weights of particles 
Joint tracking and classification with constraints and reassignment by radar and ESM