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.

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.

In this paper, we propose an efficient clutter suppression algorithm for automotive radar systems in iron-tunnel environments. In general, the clutters in iron tunnels makes it highly likely that automotive radar systems will fail to detect targets. In order to overcome this drawback, we first analyze the cepstral characteristic of the iron tunnel clutter to determine the periodic properties of the clutters in the frequency domain. Based on this observation, we suggest for removing the periodic components induced by the clutters in iron tunnels in the cepstral domain by using the cepstrum editing process. To verify the clutter suppression of the proposed method experimentally, we performed measurements by using 77GHz frequency modulated continuous waveform radar sensors for an adaptive cruise control (ACC) system. Experimental results show that the proposed method is effective to suppress the clutters in iron-tunnel environments in the sense that it improves the early target detection performance for ACC significantly.