Fast Angle Estimation and Sensor Self-Calibration in Bistatic MIMO Radar With Gain-Phase Errors and Spatially Colored Noise

Multiple-input multiple-output (MIMO) radars are essential in many Internet-of-Things (IoT) applications. Gain-phase error calibration is an important branch in MIMO radar. Existing calibration algorithms are only suitable for white Gaussian noise scenario, however, spatially colored noise is more practical in engineering implementation. In this paper, we stress the problem of direction finding and sensor self-calibration in a bistatic MIMO radar in the co-exist of unknown gain-phase error and spatially colored noise, and a covariance tensor-based parallel factor analysis (PARAFAC) estimator is proposed. To suppress the spatially colored noise and exploit the multi-dimensional structure of the array measurement, a covariance tensor is firstly established and the temporal cross-correlation operation is followed. Then the PARAFAC decomposition is carried out to obtain the factor matrices. Thereafter, automatically paired direction estimation is achieved via least squares. Finally, the element-wise multiply/divide technique and the Lagrange multipliers are adopted to obtain the gain-phase error vectors. The algorithm is analyzed in terms of identifiability and Cramer-Rao bound (CRB). Numerical simulations results show the effectiveness and improvement of the proposed estimator.