Optimal Weight Scheme for Fusion-Assisted Cooperative Multi-Monostatic Object Localization in 6G Networks

TL;DR

This paper introduces an optimal fusion algorithm for cooperative multi-monostatic sensing in 6G networks, improving passive target localization accuracy by optimally combining ToA and AoA PDFs from multiple base stations.

Contribution

It proposes a novel weighted geometric average fusion method with an iterative Monte Carlo algorithm to find optimal weights, enhancing positioning precision.

Findings

Outperforms existing benchmarks in accuracy

Effective in both LOS and multipath environments

Demonstrates the benefit of optimal weight fusion

Abstract

Cooperative multi-monostatic sensing enables accurate positioning of passive targets by combining the sensed environment of multiple base stations (BS). In this work, we propose a novel fusion algorithm that optimally finds the weight to combine the time-of-arrival (ToA) and angle-of-arrival (AoA) likelihood probability density function (PDF) of multiple BSs. In particular, we employ a log-linear pooling function that fuses all BSs' PDFs using a weighted geometric average. We formulated an optimization problem that minimizes the Reverse Kullback Leibler Divergence (RKLD) and proposed an iterative algorithm based on the Monte Carlo importance sampling (MCIS) approach to obtain the optimal fusion weights. Numerical results verify that our proposed fusion scheme with optimal weights outperforms the existing benchmark in terms of positioning accuracy in both unbiased (line-of-sight only) and biased (multipath-rich environment) scenarios.