In small-aperture high frequency surface wave radar, the main-lobe clutter all can be seen as a more severe space spread clutter under the influence of the smaller array aperture. It compromises the detection performance of moving vessels, especially when the target is submerged in the clutter. To tackle this issue, an improved spread clutter estimated canceller, combining spread clutter estimated canceller, adaptive selection strategy of the optimal training samples and rotating spatial beam method, is presented to suppress main-lobe clutter in both angle domain and range domain. According to the experimental results, the proposed algorithm is shown to have far superior clutter suppression performance based on the real data.

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.

The design and performance evaluation is presented of a partially adaptive array that suppresses clutter from low elevation angles in atmospheric radar observations. The norm-constrained and directionally constrained minimization of power (NC-DCMP) algorithm has been widely used to suppress clutter in atmospheric radars, because it can limit the signal-to-noise ratio (SNR) loss to a designated amount, which is the most important design factor for atmospheric radars. To suppress clutter from low elevation angles, adding supplemental antennas that have high response to the incoming directions of clutter has been considered to be more efficient than to divide uniformly the high-gain main array. However, the proper handling of the gain differences of main and sub-arrays has not been well studied. We performed numerical simulations to show that using the proper gain weighting, the sub-array configuration has better clutter suppression capability per unit SNR loss than the uniformly divided arrays of the same size. The method developed is also applied to an actual observation dataset from the MU radar at Shigaraki, Japan. The properly gain-weighted NC-DCMP algorithm suppresses the ground clutter sufficiently with an average SNR loss of about 1 dB less than that of the uniform-gain configuration.

Recently, many neural networks have been proposed for radar sea clutter suppression. However, they have poor performance under the condition of low signal to interference plus noise ratio (SINR). In this letter, we put forward a novel method to detect a small target embedded in sea clutter based on an optimal filter. The proposed method keeps the energy in the frequency cell under test (FCUT) invariant, at the same time, it minimizes other frequency signals. Finally, detect target by judging the output SINR of every frequency cell. Compared with the neural networks, the algorithm proposed can detect under lower SINR. Using real-life radar data, we show that our method can detect the target effectively when the SINR is higher than -39dB which is 23dB lower than that needed by the neural networks.