Fast Optimal Joint Tracking-Registration for Multi-Sensor Systems
This addresses sensor fusion accuracy for vehicular systems, but appears incremental as it builds on existing joint registration-tracking methods with a focus on speed and stability.
The paper tackles the problem of correcting systematic sensor errors in multi-sensor fusion for vehicular systems by presenting a fast maximum a posteriori algorithm for joint registration-tracking, achieving asymptotical optimality and O(n) complexity with n targets.
Sensor fusion of multiple sources plays an important role in vehicular systems to achieve refined target position and velocity estimates. In this article, we address the general registration problem, which is a key module for a fusion system to accurately correct systematic errors of sensors. A fast maximum a posteriori (FMAP) algorithm for joint registration-tracking (JRT) is presented. The algorithm uses a recursive two-step optimization that involves orthogonal factorization to ensure numerically stability. Statistical efficiency analysis based on Cramèr-Rao lower bound theory is presented to show asymptotical optimality of FMAP. Also, Givens rotation is used to derive a fast implementation with complexity O(n) with $n$ the number of tracked targets. Simulations and experiments are presented to demonstrate the promise and effectiveness of FMAP.