Target Capacity Based Resource Optimization for Multiple Target Tracking in Radar Network

In this paper, a target capacity based resource optimization (TC-RO) scheme is developed for multiple target tracking (MTT) application in radar networks. The key idea of this scheme is to coordinate the transmit power and dell time resource usage of multiple radars in order to increase the number of the targets that can be tracked with predetermined accuracy requirements. We adopt the Bayesian Cramer-Rao lower bound as a metric function to quantify the MTT accuracies, and build the TC-RO scheme as a non-smooth and non-convex optimization problem. To deal with this problem, we design an efficient three-step solution technique which incorporates relaxation and fine-tuning process. Specifically, we first relax the resulting optimization problem as a smooth one by applying sigmoid-type transformation to its objective, and then develop an appropriate method to find a local minimum to the relaxed non-convex problem with guaranteed convergence. After that, the local minimum of the relaxed problem is used as an initial point and a fine-tuning process is performed to search for a reasonable feasible solution to the original non-smooth optimization problem. Simulation results demonstrate that the proposed TC-RO scheme can greatly increase the target capacity of the radar network when compared with the traditional uniform allocation scheme.

Automated summary

This paper presents a target capacity based resource optimization scheme for multiple target tracking in radar networks, aiming to increase the number of tracked targets by coordinating the resource usage of multiple radars. The scheme uses a Bayesian Cramer-Rao lower bound as a metric function and is designed as a non-smooth and non-convex optimization problem. An efficient three-step solution technique is proposed to deal with this problem, incorporating relaxation and fine-tuning processes.