In this paper, the virtual antenna technique is applied to a single input multiple output (SIMO) radar system to enhance the performance of the conventional beamforming direction of arrival (DOA) estimation method. Combining the virtual array generated by the interpolated array technique and the real array, the angular resolution of the DOA estimation algorithm is improved owing to the extended number of antennas and aperture size. Based on the proposed interpolation technique, we transform the position of the antenna elements in a uniform linear array (ULA) to the arbitrary positions to suppress the grating lobe and side lobe levels. In simulations, the pseudo spectrum of the Bartlett algorithm and the root mean square error (RMSE) of the DOA estimation with the signal-to-noise ratio (SNR) are analyzed for the real array and the proposed virtually extended array. Simulation results show that the angular resolution of the proposed array is better than that of the real array using the same aperture size of array and the number of antennas. The proposed technique is verified with the practical data from commercialized radar system.

This paper presents an interference suppression and signal restoration technique that can create the clean signals required by automotive frequency-modulated continuous wave radar systems. When a radar signal from another radar system interferes with own transmitted radar signal, the target detection performance is degraded. This is because the beat frequency corresponding to the target cannot be estimated owing to the increase in the noise floor. In this case, advanced weighted-envelope normalization or wavelet denoising can be used to mitigate the effect of the interference; however, these methods can also lead to the loss of the desired signal containing the range and velocity information of the target. Therefore, we propose a method based on an autoregressive model to restore a signal damaged by mutual interference. The method uses signals that are not influenced by the interference to restore the signal. In experiments conducted using two different automotive radar systems, our proposed method is demonstrated to effectively suppress the interference and restore the desired signal. As a result, the noise floor resulting from the mutual interference was lowered and the beat frequency corresponding to the desired target was accurately estimated.

In this paper, we propose a received signal interpolation method for enhancing the performance of multiple signal classification (MUSIC) algorithm. In general, the performance of the conventional MUSIC algorithm is very sensitive to signal-to-noise ratio (SNR) of the received signal. When array elements receive the signals with nonuniform SNR values, the resolution performance is degraded compared to elements receiving the signals with uniform SNR values. Hence, we propose a signal calibration technique for improving the resolution of the algorithm. First, based on original signals, rough direction of arrival (DOA) estimation is conducted. In this stage, using frequency-domain received signals, SNR values of each antenna element in the array are estimated. Then, a deteriorated element that has a relatively lower SNR value than those of the other elements is selected by our proposed scheme. Next, the received signal of the selected element is spatially interpolated based on the signals received from the neighboring elements and the DOA information extracted from the rough estimation. Finally, fine DOA estimation is performed again with the calibrated signal. Simulation results show that the angular resolution of the proposed method is better than that of the conventional MUSIC algorithm. Also, we apply the proposed scheme to actual data measured in the testing ground, and it gives us more enhanced DOA estimation result.