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.

In this paper, an efficient power allocation (PA) strategy is developed for maneuvering target tracking (MTT) in the collocated MIMO radar. The mechanism of our strategy is to implement the optimal PA based on the prior target maneuvering information in the tracking cycle. The predicted conditional Cramer-Rao lower bound (PC-CRLB) is derived, normalized and adopted as the optimization criterion, since the PC-CRLB is based on the most recently realized measurement and provides a more accurate lower bound than the standard posterior CRLB (PCRLB). We fully demonstrate that the established optimization model is convex. By exploiting the monotonic decreasing property of the objective function, an efficient sequential relaxation-based solver is proposed for the solution, where the PA for one target is identified that whether should be fixed on the minimum level at each iteration. Simulation results show better tracking performance compared with the uniform allocation, and improved efficiency, compared with convex optimization tools.

With the increasing of commercial communication applications, the scarcity of spectrum resources is becoming obvious, and the joint radar and communication (JRC) systems have been attracted much attention. The resource allocation technique is crucial for mitigating mutual interference and enhancing radar sensing and communication performance in JRC systems. A strategy for joint target and user assignment, power allocation, and subchannel allocation (JCAPASA) in an unmanned aerial vehicle (UAV)-based radar and communication coexistence (RCC) network is proposed. The predicted conditional Cramér-Rao lower bound (PC-CRLB) incorporating uncertainty of sensor location (USL) is derived and utilized as the optimization metric. The optimization model aims to minimize the sum of weighted PC-CRLBs for multiple targets while adhering to constraints, such as communication data ratio (CDR) requirements, beam assignment, power budget, and subchannel nonoverlap. The optimization model is nonconvex and falls into the NP-hard class. An iterative descent-based optimization method (IDBOM) is proposed for its solution. The simulation results confirm the efficiency and effectiveness of the proposed strategy compared to state-of-the-art methods. The results also imply that the proposed strategy comprehensively considers spatial diversity and power budget to maximize tracking performance while meeting the CDR requirements.

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.