Resource saving based dwell time allocation and detection threshold optimization in an asynchronous distributed phased array radar network

The resource optimization plays an important role in an asynchronous Phased Array Radar Network (PARN) tracking multiple targets with Measurement Origin Uncertainty (MOU), i.e., considering the false alarms and missed detections. A Joint Dwell Time Allocation and Detection Threshold Optimization (JDTADTO) strategy is proposed for resource saving in this case. The Predicted Conditional Cramér-Rao Lower Bound (PC-CRLB) with Bayesian Detector and Amplitude Information (BD-AI) is derived and adopted as the tracking performance metric. The optimization model is formulated as minimizing the difference between the PC-CRLBs and the tracking precision thresholds under the constraints of upper and lower bounds of dwell time and false alarm ratio. It is shown that the objective function is nonconvex due to the Information Reduction Factor (IRF) brought by the MOU. A cyclic minimizer-based solution is proposed for problem solving. Simulation results confirm the flexibility and robustness of the JDTADTO strategy in both sufficient and insufficient resource scenarios. The results also reveal the effectiveness of the proposed strategy compared with the strategies adopting the BD without detection threshold optimization and amplitude information.