Improving PA-MIMO Radar Detection Performance Through Transceiver Subarray Configuration Optimization Under QoS-Based Model

As a tradeoff between the conventional phased-array radar and multiple-input multiple-output radar, the phased array multiple-input multiple-output (PA-MIMO) radar has attracted widespread attention. To better manage the coherent processing gain and diversity gain within the system, this article introduces a transceiver subarray configuration strategy. Its essence lies in adjusting the ratio of these two gains through subarray configuration. Initially, we develop a likelihood ratio detector that incorporates channel reciprocity and pulse accumulation, while accounting for diversity gain from subarray configurations. This subsequently leads to the derivation of an implicit radar effective range expression. Leveraging this, we formulate a quality of service-based subarray configuration optimization model, which hinges on the number of elements per subarray. The utility function of the model strikes a balance between fulfilling the task objective and possessing a certain level of low probability of intercept capability. To address this problem, we first design a relaxation and fine-tuning process, and propose an efficient elite social learning-based particle swarm optimization algorithm to find an approximate optimal solution. This algorithm circumvents local optima and inefficient search by emulating the strong uncertainty of particle state superposition. Simulation outcomes underscore the efficacy of our proposed PA-MIMO radar subarray configuration strategy and the enhanced particle swarm optimization algorithm.