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

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.

On the Integration of Enabling Wireless Technologies and Sensor Fusion for Next-Generation Connected and Autonomous Vehicles

“…RADAR sensors have low precision than cameras in the human interpretation of the measured data [93]. Moreover, cameras need training data and a machine learning model to predict and classify the target of interest [94]. Artificial Intelligence (AI)-based algorithms can covert the RADAR sensor data to valuable images, which require fusion of information collected by all sensors mounted on the vehicular platform [95].…”

M$^2$-Spectral Estimation: A Relative Entropy Approach

“…In this section, we apply our theory to the problem of target parameter estimation in an integrated system of two automotive modules, see Figure 1. The setting of the problem is the same as that in [34] which we will recall briefly. For details of the radar signal (the waveform, filtering, sampling, etc.…”

“…However, there are situations in which the model is multidimensional and multivariate, say M 2 . An example is given by the integrated system of automotive modules proposed in [34]: the latter is composed by a certain number of uniform linear arrays (ULAs) of receive antennas sharing one common transmitter.…”

A Fast Robust Numerical Continuation Solver to a Two-Dimensional Spectral Estimation Problem

“…The aforementioned theories are established for random processes and one-dimensional systems, i.e., random fields and dynamical systems that depend on one index, in most cases the time. Motivated by many practical applications involving multidimensional systems and random fields such as image processing (Ekstrom, 1984) and parameter estimation in automotive radar systems (Rohling and Kronauge, 2012;Engels, 2014;Zhu et al, 2019), the research in rational covariance extension has also been ex-However, we want to point out that while theoretical developments in this area are significant, algorithmic studies seem scarce. We mention the works Ferrante et al (2011); Baggio (2018) on an integral-form iterative algorithm, Carli et al (2013) on block-Toeplitz matrix completion, on a fast Newton solver, and Enqvist (2001); Zhu (2020) on numerical continuation methods, all in the 1-d case.…”