# Multi-Source Weighted Localization Based on Cascaded DOA-TDOA

## TL;DR

This paper introduces a new method for accurately locating multiple sources by combining direction and time-based techniques to improve signal separation and positioning accuracy.

## Contribution

A cascaded DOA-TDOA algorithm is proposed to enhance multi-source localization by integrating signal separation and weighted estimation.

## Key findings

- The method improves signal separation and localization accuracy in multi-source scenarios.
- Simulation results show the proposed algorithm outperforms existing approaches in estimation accuracy and robustness.

## Abstract

Time Difference of Arrival (TDOA)-based localization is widely used for its stability and high accuracy. However, in multi-source scenarios, TDOA measurements from multiple sources become entangled, making it difficult to separate and correctly associate them for accurate localization. To address this challenge, this paper proposes a cascaded DOA-TDOA-based multi-source weighted localization algorithm that leverages the strengths of Direction of Arrival (DOA)-based methods for separating multi-source signals and the high precision of TDOA-based methods for single-source localization. The proposed method first estimates the DOAs of multiple sources and performs DOA matching based on geometric consistency to obtain initial coarse position estimates. Subsequently, it applies wideband spatial filtering to wideband signals using the Minimum Variance Distortionless Response (MVDR) to separate multi-source signals, enhance the signal-to-noise ratio (SNR), and thereby guide the selection of the reference station and the performance of TDOA estimation. Then, TDOA estimation is performed, while the weights are assigned based on the difference in GDOP (D-GDOP), computed from the initial coarse estimate, and a weighted least-squares (WLS) method is applied to obtain the refined estimate. Finally, the D-GDOP of the refined estimate can be computed and used to reassign weights, yielding more accurate position estimate. Simulation results validate the effectiveness of the proposed method, showing superior estimation accuracy and robustness relative to existing approaches.