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

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 4; p. 981
• Main Authors: Han, Qinghua, Pan, Minghai, Long, Weijun, Liang, Zhiheng, Shan, Chenggang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 12.02.2020; MDPI AG
• Subjects: best-fitting gaussian (bfg); chance-constraint programming (ccp); cramér-rao lower bound like (crlb-like); joint adaptive sampling interval and power allocation (jasipa); maneuvering target tracking (mtt); chance-constraint programming (CCP); Cramér-Rao lower bound like (CRLB-like); joint adaptive sampling interval and power allocation (JASIPA); maneuvering target tracking (MTT); best-fitting Gaussian (BFG)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20040981

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.