Optimal Sensor Placement for Hybrid Source Localization Using Fused TOA-RSS-AOA Measurements

TL;DR

This paper proposes an optimal sensor placement method for hybrid source localization using fused TOA, RSS, and AOA measurements, leveraging majorization-minimization to improve accuracy under various noise conditions.

Contribution

It introduces a novel MM-based optimization framework for sensor placement that handles correlated noise and extends to D and E optimal criteria.

Findings

The method achieves improved localization accuracy in simulations.

It effectively handles both uncorrelated and correlated measurement noise.

The approach is adaptable to different optimal design criteria.

Abstract

Source localization techniques incorporating hybrid measurements improve the reliability and accuracy of the location estimate. Given a set of hybrid sensors that can collect combined time of arrival (TOA), received signal strength (RSS) and angle of arrival (AOA) measurements, the localization accuracy can be enhanced further by optimally designing the placements of the hybrid sensors. In this paper, we present an optimal sensor placement methodology, which is based on the principle of majorization-minimization (MM), for hybrid localization technique. We first derive the Cramer-Rao lower bound (CRLB) of the hybrid measurement model, and formulate the design problem using the A-optimal criterion. Next, we introduce an auxiliary variable to reformulate the design problem into an equivalent saddle-point problem, and then construct simple surrogate functions (having closed form solutions) over both primal and dual variables. The application of MM in this paper is distinct from the conventional MM (that is usually developed only over the primal variable), and we believe that the MM framework developed in this paper can be employed to solve many optimization problems. The main advantage of our method over most of the existing state-of-the-art algorithms (which are mostly analytical in nature) is its ability to work for both uncorrelated and correlated noise in the measurements. We also discuss the extension of the proposed algorithm for the optimal placement designs based on D and E optimal criteria. Finally, the performance of the proposed method is studied under different noise conditions and different design parameters.