Yunxiang Li

A new multiple extended target tracking algorithm using PHD filter

Abstract A new multiple extended target tracking algorithm using the probability hypothesis density (PHD) filter is proposed in our study, to solve problems on tracking performance degradation of the extended target PHD (ET-PHD) filter under the nonlinear conditions and its intolerable computational requirement. It is noted that with the current Gaussian mixture implement of ET-PHD filter satisfying tracking performance could only be obtained under linear and Gaussian conditions. To extend the application of ET-PHD filter for nonlinear models, our study has derived a particle implement of ET-PHD (ET-P-PHD) filter. Our study finds that the main factors influencing the computational complexity of the ET-P-PHD filter are the partition number of measurement set and the calculation of non-negative coefficients of cells in partitions. With the pretreatment of measurements and application of a new K -means clustering based measurement set partition method, we have successfully decreased the partition number. In addition, a gating method for target state space, which is based on likelihood relationship between target state and measurement, is proposed to simplify the calculation of non-negative coefficients. Simulation results show that the algorithms proposed by our study could satisfyingly deal with multiple extended target tracking issues under nonlinear conditions, and lead to significantly lower computational complexity with tiny effect on tracking performance.

Joint multi-target filtering and track maintenance using improved labeled particle PHD filter

As is known, the most prominent advantage of Finite Set Statistics (FISST) based multi-target tracking algorithms is it could cope with complicated tracking problems arising from special events such as target birth, target death and tracks crossing without complicated data association. Through improving the existing labeled particle Probability Hypothesis Density (L-P-PHD) filter, an improved labeled particle PHD (IL-P-PHD) filter is proposed in this paper. Simulation experiment shows that the tracking performance of IL-P-PHD filter is much better than L-P-PHD filter on complicated multi-target tracking problems, IL-P-PHD filter could extract target track information while efficiently detecting target birth and disappearance and stably estimating target state.

Joint tracking and identification of the unresolved towed decoy and aircraft using the labeled particle probability hypothesis density filter

For the distance and velocity deception from the new towed decoy, echo signal from the target and decoy appear as one target on time domain and frequency domain because of aliasing. Therefore, independent measurements for the decoy and aircraft are unavailable for conventional algorithm, neither identification and tracking. In this paper is proposed a new algorithm for joint identification and tracking of the decoy and aircraft which are unresolved within radar beam, innovations for which include: First, construction of echo signal model in the three interfering stages. Once stage decided with the decoy presence detection algorithm, the proposed particle filter based measurement generating algorithm sequentially estimates the aircraft and decoy character parameters on different stages, separating the aircraft signal and decoy signal. Secondly, based on the improved labeled particle probability hypothesis density (IL-P-PHD) filter, an algorithm for joint identification and tracking of vertically the unresolved aircraft and decoy is proposed, realizing real time identification and sequential estimation of movement state. Simulation experiment demonstrates that the proposed algorithm behaves in a manner consistent with our expectations.

Particle implementation of the multi-group multi-target probability hypothesis density filter for multi-group target tracking

We propose a particle implementation for the multi-group multi-target probability hypothesis density (MGMT-PHD) filter in this paper. It provides estimates of motion state of multi-group target centers as well as its components. The algorithm models multi-group centers as parent process, components as daughter processes related to centers. With separation of the two interacting point processes, the huge computational complexity arising from high-dimensional joint estimation is decreased. In the simulation scenario, we set a typical complicated multi-group target scene with target appearance and disappearance and tracks crossing to test the performance of the proposed algorithm.

A fast algorithm for designing the transmitted waveform and receive filter of MIMO radar

The authors study the problem of the transmitted waveform and the receiving filter design for multiple-input multiple-output (MIMO) radar in signal-dependent interference environment. The mean-square error of point target scattering coefficient estimate is considered to represent the measurement of the system. A fast computational approach is proposed to obtain optimal pairs for the transmitted waveform and receive filter. The proposed algorithm basing on fractional programming and power method-like iterations is efficient. Furthermore, this algorithm can address constant modulus constraint and peak-to-average-power ratio (PAR) constraint on the transmitted waveform. The effectiveness of this approach is demonstrated by numerical simulations.

Modified labeled particle probability hypothesis density filter for joint multi-target tracking and classification

Unification of the detection, tracking and classification for multiple targets is an object for random finite sets based filters developing. Introduction of target attribute information can improve tracking performance. Then, as the improved labeled particle probability hypothesis density (IL-P-PHD) filter is capable of joint detection and tracking, we will fuse obtained target attribute information into IL-P-PHD filter to propose a joint tracking and classification particle PHD (JTC-P-PHD) algorithm. We are in the expectation that the proposed JTC-P-PHD algorithm is capable of joint detection, tracking as well as classification of multiple targets. Numerical examples demonstrate that the proposed JTC-P-PHD algorithm behaves in a manner consistent with our expectations.

Reducing the waveform autocorrelation and cross correlation for MIMO radar with multiple pulse train coding

Reducing the correlation level lies at the core of the waveform design for multiple-input multiple-output (MIMO) radar system. In this paper, a new approach to the design of waveform for MIMO radar is proposed. The multiple pulse train coding is employed and the compression accumulation is introduced. In this manner, the waveform correlation can be almost eliminated and the optimal compression accumulation results with low range sidelobe is obtained. Numerical simulations demonstrate that the performance of the proposed approach is superior to that of previous state-of-the-art algorithms.

Identification and tracking of towed decoy and aircraft using multiple-model improved labeled P-PHD filter

Target identification is essential for the interception endgame of an aircraft that is protected by a towed radar active decoy (TRAD). In this paper, we analyze the joint rapid detection, the stability of tracking and the identification of highly maneuvering aircraft tied to a decoy. These aspects are resolved in terms of the range dimension using Finite Set Statistics (FISST) theory. In the introduction part, we propose an improved labeled particle probability hypothesis density (IL-P-PHD) filter that improves traditional L-P-PHD filter. Then, using the multiple-model (MM) method, an MM-IL-P-PHD filter for the interception of highly maneuvering target is developed. Finally, based on the proposed MM-IL-P-PHD filter and echo amplitude fluctuation characteristic based interference detection method, we establish a comprehensive frame for joint rapid detection, stable track and reliable identification of aircraft and decoy which are resolved on range dimension within radar beam. Simulation results are presented to prove the effectiveness of the proposed frame.