Joint Route Planning and Resource Allocation Algorithm for Airborne Radar Network with Imperfect Detection

To addresses the challenge of multiple target tracking (MTT) under imperfect detection conditions, a joint route planning and resource allocation (JRPRA) strategy for airborne radar network (ARN) is developed. The key mechanism of the proposed strategy involves using optimization techniques to collaboratively plan flight routes and allocate power and bandwidth for each airborne radar, thereby enhancing MTT accuracy. Initially, we derive the posterior Cramér-Rao lower bound (PCRLB) expression, which accounts for factors such as the radar’s kinematic velocity, heading angle, power and bandwidth under imperfect detection conditions. This expression serves as a measure of tracking accuracy. Building on this, we formulate a joint optimization model for ARN route planning and resource allocation, aiming to minimize the PCRLB, which represents the target estimation error. To solve this optimization problem, we propose a two-stage solution method that combines the crested porcupine optimizer (CPO) and the tent-mapping-based chaotic biogeography-based optimization (CBBO) algorithm. Simulation results demonstrate that, compared to existing algorithms, the proposed strategy can significantly improve tracking accuracy while adhering to kinematic constraints and maintaining a predetermined resource budgets.