A Multi-Layer Perceptron Applied To Number Of Target Indication For Direction-Of-Arrival Estimation In Automotive Radar Sensors

Abstract

A multi-layer perceptron for indicating the number of targets present in a range-velocity cell of automotive radar sensors is examined and compared with a state-of-the-art approach based on a Generalized Likelihood Ratio Test. The multi-target indication is typically used for direction-of-arrival es- timation to decide whether resolution in the angular domain is necessary. We focus on the practically relevant challenge of deciding between a single-target and a two-target scenario. Compared to the state-of-the-art approach which requires a preceding maximum likelihood DoA estimate and a precise array model, the proposed multi-layer perceptron directly operates on the single-snapshot spatial covariance matrix estimate. The array model inherently is learned by the network during the training process. The evaluation of the MLP in terms of classification accuracy shows that a performance similar to the Generalized Likelihood Ratio Test is achieved.

Publication
In International Workshop on Machine Learning for Signal Processing (MLSP), IEEE.

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