scholarly journals Adaptive Sample-Size Unscented Particle Filter with Partitioned Sampling for Three-Dimensional High-Maneuvering Target Tracking

2019 ◽  
Vol 9 (20) ◽  
pp. 4278 ◽  
Author(s):  
Qi Deng ◽  
Gang Chen ◽  
Huaxiang Lu
Keyword(s):  
Particle Filter ◽  
Sample Size ◽  
Target Tracking ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Maneuvering Target Tracking ◽  
Time Step ◽  
Maneuvering Target ◽  
Unscented Particle Filter ◽  
Partitioned Sampling

High-maneuvering target tracking is a focused application area in radar positioning and military defense systems, especially in three-dimensional space. However, using a traditional motion model and techniques expanded from general two-dimensional maneuvering target tracking may be inaccurate and impractical in some mission-critical systems. This paper proposes an adaptive sample-size unscented particle filter with partitioned sampling (PS-AUPF), which is used to track a three-dimensional, high-maneuvering target, combined with the CS-jerk model. In PS-AUPF, the partitioned sampling is introduced to improve the resampling and predicting process by decomposing motion space. At the same time, the adaptive sample size strategy is used to adjust the sample size adaptively in the tracking process, according to the initial parameters and the estimated state variance of each time step. Finally, the effectiveness of this method is validated by simulations, in which the sample size of each algorithm is set to the minimum required for the optimal accuracy, thus ensuring the reliability of the tracking results. The results have shown that the proposed PS-AUPF, with higher accuracy and lower computational complexity, performs better than other existing tracking methods in three-dimensional high-maneuvering target tracking scenarios.

scholarly journals Joint Adaptive Sampling Interval and Power Allocation for Maneuvering Target Tracking in a Multiple Opportunistic Array Radar System

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 981 ◽  
Author(s):  
Qinghua Han ◽  
Minghai Pan ◽  
Weijun Long ◽  
Zhiheng Liang ◽  
Chenggang Shan
Keyword(s):  
Resource Allocation ◽  
Target Tracking ◽  
Power Allocation ◽  
Adaptive Sampling ◽  
Sampling Interval ◽  
Radar System ◽  
Chance Constraint ◽  
Maneuvering Target Tracking ◽  
Time Step ◽  
Maneuvering Target

In this paper, a joint adaptive sampling interval and power allocation (JASIPA) scheme based on chance-constraint programming (CCP) is proposed for maneuvering target tracking (MTT) in a multiple opportunistic array radar (OAR) system. In order to conveniently predict the maneuvering target state of the next sampling instant, the best-fitting Gaussian (BFG) approximation is introduced and used to replace the multimodal prior target probability density function (PDF) at each time step. Since the mean and covariance of the BFG approximation can be computed by a recursive formula, we can utilize an existing Riccati-like recursion to accomplish effective resource allocation. The prior Cramér-Rao lower boundary (prior CRLB-like) is compared with the upper boundary of the desired tracking error range to determine the adaptive sampling interval, and the Bayesian CRLB-like (BCRLB-like) gives a criterion used for measuring power allocation. In addition, considering the randomness of target radar cross section (RCS), we adopt the CCP to package the deterministic resource management model, which minimizes the total transmitted power by effective resource allocation. Lastly, the stochastic simulation is embedded into a genetic algorithm (GA) to produce a hybrid intelligent optimization algorithm (HIOA) to solve the CCP optimization problem. Simulation results show that the global performance of the radar system can be improved effectively by the resource allocation scheme.

scholarly journals Multiple Maneuvering Target Tracking by Improved Particle Filter Based on Multiscan JPDA

2012 ◽  
Vol 2012 ◽  
pp. 1-25 ◽  
Author(s):  
Jing Liu ◽  
ChongZhao Han ◽  
Feng Han ◽  
Yu Hu
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Posterior Probability ◽  
Data Association ◽  
Joint Probability ◽  
Nonstationary Process ◽  
Maneuvering Target Tracking ◽  
Process Noise ◽  
Maneuvering Target ◽  
Novel Method

The multiple maneuvering target tracking algorithm based on a particle filter is addressed. The equivalent-noise approach is adopted, which uses a simple dynamic model consisting of target state and equivalent noise which accounts for the combined effects of the process noise and maneuvers. The equivalent-noise approach converts the problem of maneuvering target tracking to that of state estimation in the presence of nonstationary process noise with unknown statistics. A novel method for identifying the nonstationary process noise is proposed in the particle filter framework. Furthermore, a particle filter based multiscan Joint Probability Data Association (JPDA) filter is proposed to deal with the data association problem in a multiple maneuvering target tracking. In the proposed multiscan JPDA algorithm, the distributions of interest are the marginal filtering distributions for each of the targets, and these distributions are approximated with particles. The multiscan JPDA algorithm examines the joint association events in a multiscan sliding window and calculates the marginal posterior probability based on the multiscan joint association events. The proposed algorithm is illustrated via an example involving the tracking of two highly maneuvering, at times closely spaced and crossed, targets, based on resolved measurements.

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