High Angular Resolution for 77GHz FMCW Radar via a Sparse Weighted Quadratic Minimization

Abstract

Combining the covariance matching criterion with sparse representation, much effort was devoted to improve the angular resolution. However, the requirement of hyperparameters or the high sidelobe makes them unsatisfactory in practical radar applications. In this paper, we propose a Sparse Capon-like Weighted Quadratic Estimator (SCWQE) with hyperparameter-free and apply it to a 77GHz Frequency Modulated Continuous Wave (FMCW) radar for the high-resolution Direction Of Arrival (DOA) estimation. Under the assumption of the uncorrelated sources, SCWQE is formulated by converting the covariance matching criterion to a quadratic function with respect to the unknown power spectrum, in which a symmetrical and non-diagonal matrix called as the Capon-like spectrum is embedded into the quadratic form. The consequent multiple weighting operation would further promote the sparsity because multiple and different weighting values are exerted on each element of the spatial power spectrum. This is fundamentally different from the traditional weighted approach that employs the diagonal weighting matrix and assigns a single weighting value to each element. Therefore, SCWQE could further eliminate the spurious peaks and sharpen the desired peaks. Numerical examples including simulations and actual data collected from a 77GHz FMCW radar sensor demonstrate that, the proposed SCWQE algorithm produces better accuracy of DOA estimation and higher angular resolution compared to some typical sparse recovery methods.