Fusion of Sensors Data in Automotive Radar Systems: A Spectral Estimation Approach

TL;DR

This paper introduces a spectral estimation approach for fusing data from multiple automotive radar sensors to enhance the accuracy and robustness of target location and velocity estimates in driver assistance systems.

Contribution

It formulates the sensor fusion problem as a multivariate spectral estimation task and demonstrates the benefits of using cross-spectra magnitude for improved target estimation accuracy.

Findings

Magnitude of cross-spectrum improves accuracy

Phase lag compensation yields marginal gains

First application of high-resolution spectral methods for sensor fusion

Abstract

To accurately estimate locations and velocities of surrounding targets (cars) is crucial for advanced driver assistance systems based on radar sensors. In this paper we derive methods for fusing data from multiple radar sensors in order to improve the accuracy and robustness of such estimates. First we pose the target estimation problem as a multivariate multidimensional spectral estimation problem. The problem is multivariate since each radar sensor gives rise to a measurement channel. Then we investigate how the use of the cross-spectra affects target estimates. We see that the use of the magnitude of the cross-spectrum significantly improves the accuracy of the target estimates, whereas an attempt to compensate the phase lag of the cross-spectrum only gives marginal improvement. This paper may be viewed as a first step towards applying high-resolution methods that builds on multidimensional multivariate spectral estimation for sensor fusion.