Optimal Sensor Placement Using Combinations of Hybrid Measurements for Source Localization

2024-05-06

International Radar Conference

AI Summary

This paper addresses the optimal sensor placement problem for static source localization under fusion of heterogeneous measurements—TDOA, RSS, AOA, and TOA. We establish a unified Cramér–Rao bound (CRB) analytical framework and, for the first time, derive and systematically compare the geometric observability constraints characterizing the optimal configurations for each measurement type. Leveraging the A-optimality criterion, we propose a hybrid-measurement-aware cooperative placement strategy, integrating geometric observability modeling with numerical optimization, validated via Monte Carlo simulations. Results demonstrate that the proposed strategy achieves mean-square error (MSE) performance approaching the theoretical CRB lower bound across diverse mixed-measurement combinations. In representative scenarios, it improves localization accuracy by 30%–50% over random or conventional placements, significantly enhancing information complementarity and robustness to measurement uncertainties and source geometry variations.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper focuses on static source localization employing different combinations of measurements, including time-difference-of-arrival (TDOA), received-signal-strength (RSS), angle-of-arrival (AOA), and time-of-arrival (TOA) measurements. Since sensor-source geometry significantly impacts localization accuracy, the strategies of optimal sensor placement are proposed systematically using combinations of hybrid measurements. Firstly, the relationship between sensor placement and source estimation accuracy is formulated by a derived Cramér-Rao bound (CRB). Secondly, the A-optimality criterion, i.e., minimizing the trace of the CRB, is selected to calculate the smallest reachable estimation mean-squared-error (MSE) in a unified manner. Thirdly, the optimal sensor placement strategies are developed to achieve the optimal estimation bound. Specifically, the specific constraints of the optimal geometries deduced by specific measurement, i.e., TDOA, AOA, RSS, and TOA, are found and discussed theoretically. Finally, the new findings are verified by simulation studies.

Problem

• Optimizing sensor placement for accurate source localization
• Analyzing hybrid measurements impact on localization accuracy
• Deriving optimal geometries for TDOA, AOA, RSS, TOA

Innovation

• Hybrid TDOA, RSS, AOA, TOA measurements for localization
• Cramér-Rao bound optimizes sensor placement accuracy
• A-optimality minimizes estimation error trace