A Novel Numerical Method for Relaxing the Minimal Configurations of TOA-Based Joint Sensors and Sources Localization

TL;DR

This paper presents a new numerical approach that relaxes the minimal sensor and source configuration requirements for 3D joint localization using TOA measurements, enabling localization with fewer sensors and sources.

Contribution

It introduces a numerical method that reduces the minimal configuration for joint sensors and sources localization in 3D space, expanding practical applicability.

Findings

Localization feasible with only four sensors and four sources.

The method accurately estimates unknown distances using triangle inequalities.

Simulation results confirm the method's effectiveness and robustness.

Abstract

This work introduces a novel numerical method that relaxes the minimal configuration requirements for joint sensors and sources localization (JSSL) in 3D space using time of arrival (TOA) measurements. Traditionally, the principle requires that the number of valid equations (TOA measurements) must be equal to or greater than the number of unknown variables (sensor and source locations). State-of-the-art literature suggests that the minimum numbers of sensors and sources needed for localization are four to six and six to four, respectively. However, these stringent configurations limit the application of JSSL in scenarios with an insufficient number of sensors and sources. To overcome this limitation, we propose a numerical method that reduces the required number of sensors and sources, enabling more flexible JSSL configurations. First, we formulate the JSSL task as a series of triangles and apply the law of cosines to determine four unknown distances associated with one pair of sensors and three pairs of sources. Next, by utilizing triangle inequalities, we establish the lower and upper boundaries for these unknowns based on the known TOA measurements. The numerical method then searches within these boundaries to find the global optimal solutions, demonstrating that JSSL in 3D space is achievable with only four sensors and four sources, thus significantly relaxing the minimal configuration requirements. Theoretical proofs and simulation results confirm the feasibility and effectiveness of the proposed method.