Enhanced Target Localization with Deployable Multiplatform Radar Nodes Based on Non-Convex Constrained Least Squares Optimization

TL;DR

This paper introduces a novel 3D target localization algorithm for multiplatform radar networks that uses non-convex constrained least squares optimization, achieving lower error rates especially in low SNR conditions.

Contribution

It formulates a non-convex constrained LS problem for target localization and provides a quasi-closed-form solution leveraging KKT conditions, improving accuracy over existing methods.

Findings

Lower RMSE than benchmark methods in low SNR regimes

Effective utilization of monostatic sensor radiation patterns

Solution leverages KKT conditions for efficiency

Abstract

A new algorithm for 3D localization in multiplatform radar networks, comprising one transmitter and multiple receivers, is proposed. To take advantage of the monostatic sensor radiation pattern features, ad-hoc constraints are imposed in the target localization process. Therefore, the localization problem is formulated as a non-convex constrained Least Squares (LS) optimization problem which is globally solved in a quasi-closed-form leveraging Karush-Kuhn-Tucker (KKT) conditions. The performance of the new algorithm is assessed in terms of Root Mean Square Error (RMSE) in comparison with the benchmark Cramer Rao Lower Bound (CRLB) and some competitors from the open literature. The results corroborate the effectiveness of the new strategy which is capable of ensuring a lower RMSE than the counterpart methodologies especially in the low Signal to Noise Ratio (SNR) regime.