Multi-target elliptic positioning via difference of convex functions programming

Abstract

Multi-target localization in a distributed multiple-input multiple-output radar is quite challenging as the correct measurement-target associations in each transmitter–receiver pair are unknown. In this paper, we address this difficult problem from a joint optimization perspective. The measurement-target association and multi-target localization are jointly formulated as an intractable mixed-integer optimization problem, which contains both discrete and continuous variables. We first develop an equivalent Difference of Convex functions (DC) representation for the non-convex Boolean constraint imposed on the association variables, making the problem tractable. Then, a DC algorithm is derived to efficiently solve the resulting optimization problem. Simulation results demonstrate that the proposed DC method is numerically accurate when compared to state-of-the-art methods.