Geometric Optimization of Distributed MIMO Radar Systems With Spatial Distance Constraints

We consider a geometric optimization problem of distributed multi-input multi-output (MIMO) radar systems with widely separated radar nodes in this article. The aim is to maximize the radar surveillance performance in a given area of interest by adjusting the node positions, while satisfying practical spatial distance constraints among radar nodes. Typical constraints can be, for example, the maximum distance constraints between nodes and fusion centers (FCs) due to limited communication and the minimum distance constraints to ensure a better system spatial diversity. To achieve this goal, we first derive an analytical expression for a weighted coverage ratio (WCR) metric to evaluate the system surveillance performance. Then, using the WCR metric as the objective function, we formulate a spatial constrained geometric optimization problem for three typical MIMO radar system architectures, each of which has a unique expression of distance constraints. However, the formulated optimization problem is computationally intractable for practical scenarios due to its high dimensionality, non-convexity and especially the complex spatial constraints. To solve this problem, we propose an enhanced particle swarm optimization (PSO) algorithm, and different from the standard PSO, the particles of the proposed enhanced PSO can properly consider the distance constraints within themselves during swarm optimization process. Finally, various numerical studies show that the proposed method can effectively maximize the surveillance performance while satisfying the complex distance constraints.