Joint Dwell Time and Bandwidth Optimization for Multi-Target Tracking in Radar Network Based on Low Probability of Intercept

Abstract

Radar network systems have been demonstrated to offer numerous advantages for target tracking. In this paper, a low probability of intercept (LPI)-based joint dwell time and bandwidth optimization strategy is proposed for multi-target tracking in a radar network. Since the Bayesian Cramer–Rao lower bound (BCRLB) provides a lower bound on parameter estimation, it can be utilized as the accuracy metric for target tracking. In this strategy, in order to improve the LPI performance of the radar network, the total dwell time consumption of the underlying system is minimized, while guaranteeing a predetermined tracking accuracy. There are two adaptable parameters in the optimization problem: one for dwell time, and the other for bandwidth allocation. Since the nonlinear programming-based genetic algorithm (NPGA) can solve the nonlinear problem well, we develop a method based upon NPGA to solve the resulting problem. The simulation results demonstrate that the proposed strategy has superiority over traditional algorithms, and can achieve a better LPI performance of this radar network.

Low probability of intercept‐based cooperative node selection and transmit resource allocation for multi‐target tracking in multiple radars architecture

“…Moreover, in order to better illustrate the superiority of the proposed CNS‐TRA strategy, its LPI performance and target tracking performance are compared with those of the following three benchmarks: Collaborative Power and Bandwidth Allocation (CPBA) strategy [26]: This method optimises the allocation of transmit power and effective bandwidth, whereas the dwell time of each PAR is fixed to 0.005 s. Joint Dwell Time and Bandwidth Allocation (JDTBA) strategy [18]: This method optimises the allocation of dwell time and effective bandwidth, whereas the transmit power of each PAR is fixed to 300W. Uniform Transmit Resource Allocation (UTRA) strategy: In this method, the transmit resource, i.e., transmit power, dwell time and effective bandwidth, are uniformly allocated to the designated radar nodes. …”

“…Ding et al. [18] extend this work to joint dwell time and bandwidth optimisation case, which further improves LPI performance of radar network system. In the scenario of insufficient available power, Yuan et al.…”

Designing adaptive time resource management cost function for cognitive radar

“…The time resource allocation is performed using kinematic estimation and prediction of target state parameters. Resource management solutions found in the literature mainly use the predicted states covariance matrix [3, 4], its expectation bound – Bayesian Cramer‐Rao lower bound (BCRLB) [5], the posterior CRLB (PCRLB) [6, 7] and predicted conditional CRLB (PC‐CRLB) [8].…”

Range-Doppler domain spatial alignment for networked radars

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Networked radars can share different views of data from multiple stand-alone radars for fusion so as to improve the target tracking accuracy. The information fusion of multiple radars has the advantages of increasing the surveillance systems accuracy and enhancing the reliability [1][2][3][4].Before fusion, the data measured by local radars should be transformed to a common spatial coordinate system to acquire the reliable information of targets [5]. However, due to the deviation and measurement errors of radars, it is difficult to ensure the accuracy for tracking.

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“…Networked radars can share different views of data from multiple stand-alone radars for fusion so as to improve the target tracking accuracy. The information fusion of multiple radars has the advantages of increasing the surveillance systems accuracy and enhancing the reliability [1][2][3][4].…”