In this study, a low probability of intercept (LPI)-based robust node selection and power allocation (RNS-PA) scheme is proposed for multiple targets tracking (MTT) within a colocated multiple-input multiple-output (C-MIMO) radar network. The core of the RNS-PA scheme is to adaptively coordinate the radar-to-target assignment and transmit power to improve the LPI performance of the radar network, subject to the MTT accuracy requirements and system resource budgets. Unlike the existing resource-aware design algorithms that prioritize total resource consumption as the optimization objective (commonly resulting in decreased resource utilization and suboptimal solutions), we employ the closed-form expression for the worst-case probability of the C-MIMO radar network being intercepted by hostile targets as the optimization criterion. It is shown that the RNS-PA scheme is formulated as a nonlinear and nonconvex optimization problem. By evaluating whether all targets can satisfy the predefined tracking accuracy demands, the original problem is transformed into two subproblems: first, accelerating the reduction of MTT error under a given interception probability threshold (when the original problem is infeasible), and second, minimizing the worst-case interception probability subject to the MTT accuracy threshold constraints (when the original problem is feasible). We verify that the interception probability constraint forms a convex set and then propose a fast, comprehensive solution method to tackle the optimization problem efficiently. Simulation results verify the robustness and effectiveness of the proposed RNS-PA scheme.


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    Title :

    LPI-Based Robust Node Selection and Power Allocation Scheme for Multiple Targets Tracking in Colocated MIMO Radar Network


    Contributors:
    Ding, Lintao (author) / Long, Fei (author) / Shi, Chenguang (author) / Wang, Jian (author) / Zhou, Jianjiang (author)

    Published in:

    Publication date :

    2025-08-01


    Size :

    3359412 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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