Xinliang Chen

Improved Probabilistic Multi-Hypothesis Tracker for Multiple Target Tracking With Switching Attribute States

The probabilistic multi-hypothesis tracker (PMHT) is an effective multiple target tracking (MTT) method based on the expectation maximization (EM) algorithm. The PMHT only uses the kinematic information to solve the problem of measurement to target association. However, in some applications, other information such as attribute measurements of targets may be available, which has potential to reduce misassociations and improve the tracking performance. Integrating attributes into the PMHT may suffer from the switch of attribute states and the instability of attribute measurements. In this paper, an attribute-aided association structure for the PMHT is proposed to consider the uncertainty in both attribute states and attribute measurements. The attribute characteristics are described by the hidden Markov model (HMM), and the joint probabilistic model of kinematic and attribute properties is derived. The attribute states are estimated by the Viterbi algorithm and the data association is improved by the extracted attribute information. Simulation results show that the proposed algorithm has better performance when the attributes of targets are available.

Offline Performance Prediction of PDAF With Bayesian Detection for Tracking in Clutter

Conventional detectors in tracking system generally work according to the Neyman-Pearson criterion. Recently, Bayesian detection has become an alternative for many trackers such as probabilistic data association filter (PDAF). However, a critical problem of the tracking system with Bayesian detection is to predict the tracking performance without simulations. As the Bayesian detection is introduced, clutters are nonuniformly distributed and the detection threshold varies with time, which increases the difficulty of the analysis. In this paper, an offline method is developed to predict the performance of the PDAF with Bayesian detection (PDAF-BD). In the approach, the information reduction factor (IRF) of the PDAF-BD is derived, describing the influence of measurement origin uncertainty. Unlike the IRF of PDAF, the IRF of PDAF-BD has analytical expression which is efficient in computation. On this basis, the offline recursion of the error covariance and the quantification of track loss are achieved. The experiments show that the nonsimulated result generated by the proposed algorithm is reasonably close to the simulated one.

Fresnel based frequency domain adaptive beamforming for large aperture distributed array radar

Distributed array radar is a significant radar system, which can not only improve radar detection capability and measurement accuracy, but also suppress mainlobe interference effectively. However, due to its equivalent large aperture, the plane wave assumption fails. Whats worse, target signals arriving at each array may have the envelope migration problem, thus the traditional adaptive beamforming method becomes invalid. This paper proposes a Fresnel based frequency domain adaptive beamforming method for large aperture distributed array radar. Instead of plane wave model, it uses the Fresnel model based steering vector to improve accuracy. Meanwhile, it transforms the time domain signal to frequency domain, and using the traditional adaptive beamforming method for each frequency point respectively. Finally, it accomplishes the frequency domain adaptive beamforming by an inverse Fourier transform (IFFT). In result, this method could eliminate the envelope migration problem effectively. Simulations illustrate the effectiveness of the proposed method.

Whitening filter for mainlobe interference suppression in distributed array radar

This paper proposes a mainlobe interference suppression method based on whitening filter for distributed array radar (DAR). Due to the equivalent large aperture of DAR, it is possible to cancel the mainlobe interference without target signal suppression. For narrowband signal, the time domain whitening filter (TD-WF) is used. As for the wideband signal or large aperture DAR, the target signals arriving at each antenna may have the envelope migration problem, thus the frequency domain whitening filter (FD-WF) is utilized in order to avoid this problem. In addition, a mainlobe interference suppression experiment is carried out in the scenario involving an S-band experimental radar system and an S-band jammer. The measured data is processed employing the proposed method, and the result verifies the effectiveness of this algorithm.

Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar

Distributed array radar can improve radar detection capability and measurement accuracy. However, it will suffer cyclic ambiguity in its angle estimates according to the spatial Nyquist sampling theorem since the large sparse array is undersampling. Consequently, the state estimation accuracy and track validity probability degrades when the ambiguous angles are directly used for target tracking. This paper proposes a second probability data association filter (SePDAF)-based tracking method for distributed array radar. Firstly, the target motion model and radar measurement model is built. Secondly, the fusion result of each radar’s estimation is employed to the extended Kalman filter (EKF) to finish the first filtering. Thirdly, taking this result as prior knowledge, and associating with the array-processed ambiguous angles, the SePDAF is applied to accomplish the second filtering, and then achieving a high accuracy and stable trajectory with relatively low computational complexity. Moreover, the azimuth filtering accuracy will be promoted dramatically and the position filtering accuracy will also improve. Finally, simulations illustrate the effectiveness of the proposed method.