Multi-Dimensional Resource Management Optimization Strategy for Multi-Group Target Tracking

Objective Although existing resource management strategies can improve the performance of Multiple Target Tracking (MTT), they generally assume that targets are well separated. With the development of swarm intelligence, multiple maneuvering targets can adopt group merging and splitting tactics to enhance task efficiency. This often results in the presence of several closely spaced targets within a single tracking beam, making them difficult to distinguish. In such cases, networked radars must coordinate the allocation of signal bandwidth and pulse width to increase both the resolution of adjacent targets and overall MTT performance. However, previous studies have not accounted for the effect of signal resolution capability on MTT performance. When adjacent targets emerge in the surveillance region, resource allocation schemes derived from existing methods become insufficient. To address this limitation, this study proposes a joint Beam Selection and Multi-dimensional Resource Management (BSMRM) strategy. By jointly optimizing the transmit beam, power, signal bandwidth, and pulse width, the proposed approach markedly improves MTT performance for multiple groups of clustered maneuvering targets and enhances the resolution of adjacent targets within the surveillance area. Methods This study proposes a BSMRM strategy for netted radar systems to improve target resolution capability and MTT accuracy. First, the Bayesian Cramér-Rao Lower Bound (BCRLB) is derived under the Probabilistic Data Association (PDA) fusion rule and the Interacting Multiple Model-Extended Kalman Filter (IMM-EKF) framework. Resolution performance is quantified using the normalized magnitude of the ambiguity function for both observed and unobserved targets located within the beam. A utility function is then formulated using the logarithmic barrier method to quantify global performance. Multi-dimensional resource management is modeled as a constrained optimization problem, with the objective of maximizing global performance under system resource constraints. A three-stage solver is developed to address the optimization problem. Simulation results show that the proposed BSMRM strategy achieves comparable target resolution and MTT accuracy to state-of-the-art algorithms, while demonstrating superior efficiency in spectrum usage and power allocation. Results and Discussions As shown in (Table 1) and (Fig. 1), this study evaluates the effectiveness and robustness of the proposed BSMRM strategy in a complex scenario where four groups of swarm targets maneuver within the surveillance region of three radar nodes. The performance of the BSMRM strategy is assessed through four simulation experiments. Experiment 1 presents the allocation of transmission resources, including beam selection and power distribution. The results are shown in (Fig. 2), where color-coded regions represent normalized transmit power levels determined by the BSMRM strategy. Experiment 2 reports the allocation of waveform resources—specifically, signal bandwidth and pulse width. In this experiment, resolution thresholds corresponding to various target states are first established. The BSMRM strategy is then used to adjust the signal bandwidth and pulse width of each transmit beam to enable effective discrimination of swarm targets. Experiment 3 analyzes tracking performance through numerical simulations, comparing the proposed strategy with benchmark algorithms. A sequence of 60 tracking frames with a time interval of T = 2.5 s is simulated, with 300 Monte Carlo runs. Root Mean Square Error (RMSE) is used as the evaluation metric. (Fig. 4) displays the BCRLB and RMSE curves for each target group under the BSMRM strategy. The RMSE converges toward the corresponding BCRLBs, indicating that the proposed method meets tracking accuracy requirements across all target groups. When sufficient transmit power is available, the BSMRM strategy drives the accuracy to the desired thresholds, thereby maximizing utility. (Fig. 5) compares the normalized average power consumption of three strategies. (Figs. 6 and 7) show resolution performance curves for the BSMRM and JBPAWLS strategies, respectively. (Fig. 8) compares spectrum occupancy across two operational phases to assess spectral efficiency under resolution constraints. Experiment 4 evaluates the computational complexity of the BSMRM strategy. Results show that, with parallel computing, the strategy satisfies the real-time requirements of resource scheduling. Conclusions This study proposes a BSMRM strategy for MTT in a C-MIMO radar network. The strategy jointly optimizes beam selection, transmit power, signal bandwidth, and pulse length to improve both target resolvability and tracking accuracy. An efficient three-stage solver is developed to address the resulting optimization problem. Simulation results show that the proposed strategy achieves high-resolution MTT while reducing spectral occupancy. Under sufficient power conditions, tracking accuracy is comparable to that of state-of-the-art algorithms. In the same simulation environment, the average CPU runtime per frame is 2.8617 s, which is reduced to 0.357 3 s with GPU-based parallel processing—meeting the real-time requirements for resource allocation. Moreover, the BSMRM strategy demonstrates flexibility and robustness under limited power constraints.