High Frequency Surface Wave Radar (HFSWR) can achieve over-the-horizon detection, which can effectively detect and track the ships and ultra-low altitude aircrafts, as well as the acquisition of sea state information such as icebergs and ocean currents and so on. However, HFSWR is seriously affected by the clutters, especially sea clutter and ionospheric clutter. In this paper, we propose a deep learning image semantic segmentation method based on optimized Deeplabv3+ network to achieve the automatic detection of sea clutter and ionospheric clutter using the measured R-D spectrum images of HFSWR during the typhoon as experimental data, which avoids the disadvantage of traditional detection methods that require a large amount of a priori knowledge and provides a basis for subsequent the clutter suppression or the clutter characteristics research.

A generalized covariance matrix taper (GCMT) model is proposed to enhance the performance of knowledge-aided space-time adaptive processing (KA-STAP) under sea clutter environments. In KA-STAP, improving the accuracy degree of the a priori clutter covariance matrix is a fundamental issue. As a crucial component in the a priori clutter covariance matrix, the taper matrix is employed to describe the internal clutter motion (ICM) or other subspace leakage effects, and commonly constructed by the classical covariance matrix taper (CMT) model. This work extents the CMT model into a generalized CMT (GCMT) model with a greater degree of freedom. Comparing it with the CMT model, the proposed GCMT model is more suitable for sea clutter background applications for its improved flexibility. Simulation results illustrate the efficiency of the GCMT model under different sea clutter environments.

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.

Accurate modeling of sea clutter and detection of low observable targets within sea clutter are the major goals of radar signal processing applications. Recently, fractal geometry has been applied to the analysis of high range resolution radar sea clutters. The box-counting method is widely used to estimate fractal dimension but it has some drawbacks. We explain the drawbacks and propose a new fractal dimension based detector to increase detection performance in comparison with traditional detectors. Both statistically generated and real data samples are used to compare detector performance.

Estimating the parameters of a statistical distribution from measured sample values forms an essential part of many signal processing tasks. K-distribution has been proven to be an appropriate model for characterising the amplitude of sea clutter. In this paper, a new method for estimating the parameters of K-Distribution is proposed. The method greatly lowers the computational requirement and variance of parameter estimates when compared with the existing non-maximum likelihood methods.

We observed the log normal, log-Weibull and K-distributed sea-clutter from high sea state 7 with an X-band radar for grazing angles between 3.1 and 17.5. To determine the sea-clutter amplitude statistics, we introduced the Akaike Information Criterion (AIC), which is more rigorous fit of the distribution to the data than the least-squares method.

A novel algorithm associated with fractal preprocessors, wavelet feature extractors and unsupervised neural classifiers is proposed for detecting radar targets embedded in sea ice and sea clutter. Utilizing the advantages of fractals, wavelets and neural networks, the algorithm is suitable for real-time and automatic applications. Fractal preprocessor can increase 10 dB signal-to-clutter ratios (S/C) for radar images by using fractal error. Fractal error will make easy to detect radar targets embedded in high clutter environments. Wavelet feature extractors with a high speed computing architecture, can extract enough information for classifying radar targets and clutter, and improve signal-to-clutter ratios. Wavelet feature extractors can also provide flexible combinations for feature vectors at different clutter environments. The unsupervised neural classifier has a parallel operation architecture easily applied to hardware, and a low computational load algorithm without manual interventions during learning stage. We modified the unsupervised competitive learning algorithm to be applicable for detecting small radar targets by introducing an asymmetry neighborhood factor. The asymmetry neighborhood factor can provide a protective learning to prevent interference from clutter and improve the learning effects of radar targets. The small radar targets in Millimeter wave (MMW) and X-band radar images have been successfully discriminated by our proposed algorithm. The effective, efficient, high noise immunity characteristics for our proposed algorithm have been demonstrated to be suitable for automatic and real time applications.

Sea clutter was measured using an X-band radar at very high grazing angles between 8.2 and 17.5. The sea state was 7 with the wave height of 6 to 9m. The wind velocity was 25m/s. It is shown that sea clutter amplitudes obey the log-normal and K distributions using the Akaike Information Criterion (AIC) , which is more rigorous fit to the distribution to the data than the least squares method.