Deployment Optimization for Integrated Search and Tracking Tasks in Netted Radar System Based on Pareto Theory

This article proposes a joint deployment optimization method for integrated search and tracking (ISAT) tasks in netted radar systems. The biobjective optimization model is developed with the aim to improve the integrated performance of the two tasks by optimizing the positions of radars. The first objective function aims to maximize the integrated sensing area for the search task. The second objective function aims to minimize the weighted sum of the localization errors w.r.t. multiple sampling points when the tracking task is simulated by localizing some sampling points from a potential trajectory in this article. A modified nondominated sorting genetic algorithm method based on the Pareto theory is proposed in this article to solve the biobjective optimization problem and get the Pareto optimal curve. With the Pareto optimal curve, we can choose an optimized deployment scheme conveniently by the table lookup method and find a suitable tradeoff between ISAT tasks. Finally, simulation results are given to demonstrate the effectiveness and practicality of the joint deployment optimization method.