The “defocused transmit-focused receive” (DTFR) mode in the distributed multiple-input multiple-output (MIMO) radar network is very effective in multitarget tracking. In this mode, a completely defocused beam is transmitted and a focused receive beam is synthesized so that the MIMO radar is capable of tracking targets independently. A joint target assignment and power allocation (TAPA) strategy is developed for multiple distributed MIMO radar networks in cluttered environment using the DTFR mode. Our aim is to achieve the better system tracking accuracy under the constraints of receive beam direction capability and power budget. We derive the posterior Cramer-Rao lower bound (PCRLB) and adopt it as the objective function, since it quantifies the precision of target state estimates. It is shown that the TAPA problem is a mixed integer programming and NP-hard problem, where two involved parameters, i.e., the target-radar assignment and power allocation, are both coupled in the objective and in the constraints. By introducing an intermediate variable, we propose an efficient two-step-based solution for solving this problem. The simulation results show the superior performance and adaptivity compared with existing algorithms.

A collocated multiple-input multiple-output (MIMO) radar has the ability to track multiple targets under the simultaneous multi-beam mode, where each transmit beam is defocused to search the entire surveillance space, and then; a focused beam is formed at the receiver to achieve a higher resolution. A joint power and bandwidth allocation strategy is developed in this mode. The optimization model is established with the aim of improving the sum of weighted posterior Cramer-Rao lower bounds (PCRLBs) of multiple targets, which is predicted based on the feedback information in the tracking recursive cycle, subject to the limited power and bandwidth budget. By exploiting the convex relaxation technique and the cycle minimizer, this problem is transformed into a two-stage convex problem, and an efficient solution is provided using the cyclical optimization framework. The effectiveness of the proposed strategy is demonstrated by comparison with three uniform resource allocation methods in many cases.

How to utilize the limited antenna s for tracking multiple targets plays a critical role in the collocated multiple input multiple output MIMO radar. A dynamic antenna selection strategy is proposed to address this problem . The basis of our strategy is to achieve the optimal antenna selection under the constraint of limited active antennas using the feedback information in the tracking recursion cycle, improving the worst case of estimate accuracy among multiple targets . Since the posterior Cramer Rao lower bound (PCRLB) quantifies the target tracking performance, it is derived and utilized as the optimization criterion. W e then propose an efficient algorithm which integrates the convex relaxation technique with the local search to solve the problem. S imulation results show its superior performance compared with the random antenna selection strategy and the heuristic search algorithm . Moreover, the proposed strategy can provide the performance close to the exhaustive search method while maintaining reasonable runtime.

In this paper, with the aim of performing joint multi-target localization and discrimination tasks, a performance-driven resource allocation scheme is proposed. In the first, by establishing the signal model under deception jamming and utilizing the maximum likelihood (ML) estimator, the estimation information of targets can be obtained. Secondly, the Cramer–Rao lower bound (CRLB) for the transmit antenna selection and power allocation is derived. Then, to fully utilize the difference in spatial distribution between true and false targets, a false target discriminator based on the CRLB of the distance deception parameter is utilized. By introducing the nondimensionalization mechanism, we build an optimal objective function of target localization error and discrimination probability. Subsequently, a joint multi-target localization and discrimination optimization model has been established, which is mathematically a non-smooth and non-convex problem. By introducing an auxiliary variable, we propose a three-step solution strategy for solving this problem. Simulation results demonstrate that the proposed algorithm can improve the performance of joint localization accuracy and discrimination ability (JLADA) by more than 30% compared with the algorithms only for localization or discrimination. Meanwhile, by utilizing the proposed algorithm, the composite indicators of JLADA can decrease more than 70% compared with the uniform allocation scheme.