Optimal Estimation of Sensor Biases for Asynchronous Multi-Sensor Data Fusion

TL;DR

This paper introduces a novel nonlinear least squares approach and an efficient block coordinate descent algorithm for estimating sensor biases in asynchronous multi-sensor data fusion, achieving high accuracy and outperforming existing methods.

Contribution

The paper presents a new LS formulation and a BCD algorithm with guaranteed global convergence in noise-free scenarios for sensor bias estimation.

Findings

Significantly reduces root mean square error compared to existing methods.

Guarantees global optimality in noise-free conditions.

Efficiently handles measurement asynchrony and nonlinear transformations.

Abstract

An important step in a multi-sensor surveillance system is to estimate sensor biases from their noisy asynchronous measurements. This estimation problem is computationally challenging due to the highly nonlinear transformation between the global and local coordinate systems as well as the measurement asynchrony from different sensors. In this paper, we propose a novel nonlinear least squares (LS) formulation for the problem by assuming the existence of a reference target moving with an (unknown) constant velocity. We also propose an efficient block coordinate decent (BCD) optimization algorithm, with a judicious initialization, to solve the problem. The proposed BCD algorithm alternately updates the range and azimuth bias estimates by solving linear least squares problems and semidefinite programs (SDPs). In the absence of measurement noise, the proposed algorithm is guaranteed to find the global solution of the problem and the true biases. Simulation results show that the proposed algorithm significantly outperforms the existing approaches in terms of the root mean square error (RMSE).