This paper examines the direction of arrival (DOA) estimation for polarized signals impinging on a sparse vector sensor array which is based on the maximum interelement spacing constraint (MISC). The vector array effectively utilizes the polarization domain information of incident signals, and the quaternion model is adopted for signals polarization characteristic maintenance and computational burden reduction. The features of MISC arrays are crucial to the mutual coupling effects reduction and higher degrees of freedom (DOFs). The quaternion data model based on vector MISC arrays is established, which extends the scalar MISC array into the vector MISC array. Based on the model, a quaternion multiple signal classification (MUSIC) algorithm based on vector MISC arrays is proposed for DOA estimation. The algorithm combines the advantages of the quaternion model and the vector MISC array to enhance the DOA estimation performance. Analytical simulations are performed to certify the capability of the algorithm.

As a hot research topic, the gain-phase error self-calibration in MIMO radar systems has been investigated for many years. In this paper, we proposed a novel array error self-calibration method, termed online errors self-calibration based on feature learning (OES-FL). This method regards the statistical characteristics of the detected targets’ DOA as a prior knowledge and does not require the calibrated antenna subarray or external reference source to correct the array disturbances in real time. First, we analyse the monostatic MIMO signal model suffering gain-phase error. Then, we exploit the statistical characteristics of DOA of many targets for correcting gain-phase error of antenna array. Next, the gain-phase error estimation scheme based on LMS and the DOA deviation estimation method based on LSTM are proposed, respectively. Using real-life radar data collected at the integrated transportation hubs to generate simulation data, the proposed approach is shown to be effective in correcting gain-phase errors and, therefore, provides a promising model for online error self-calibration in monostatic MIMO radars.

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