Joint Spatio-Temporal Bias Estimation and Tracking for GNSS-Denied Sensor Networks

Sensor parameter estimation is a key process that must be considered when performing data fusion in a multi-sensor object tracking scenario. For example, significant relative time delays in sensor data arriving at a fusion centre can result in a reduction of track accuracy, false tracks, or early termination of a true object track. The same issues may arise in the presence of some relative angular bias between sensors. This article presents a technique for simultaneous target tracking and estimation of relative time delays and angular biases in data for a multi-sensor system with no access to a global frame of reference. The proposed technique makes use of a hierarchical Bayesian model and couples a grid-based search method with an array of augmented state Kalman filters to accomplish this. Results are provided comparing the root-mean-squared error in a simulated single object tracking scenario. The performance of a single sensor, two sensors with correct registration, two sensors with incorrect registration, and two sensors with registration correction are compared. The results demonstrate a significant improvement in tracking performance when registration errors are corrected with the proposed method, as well as an increase in accuracy over object tracking with only a single sensor.

I. Introduction

Sensor data fusion is a valuable technique in sensor networks. Use of multiple sensors to carry out a task - such as behaviour analysis - has advantages over the same task performed by only a single sensor [1]. Calibration of these sensors is key to their reliable performance. Consider the rapid deployment of radars on ships where there may be no time for calibration ahead of time - impromptu registration becomes a necessary capability. Sensors with unknown relative registration biases are likely to return data which can lead to, in the case of object tracking, false tracks or complete loss of tracks. In a crisis situation, this could be disastrous.