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.

The recently proposed distributed adaptive direct position determination (D-ADPD) algorithm provides an efficient way to locating a radio emitter using a sensor network. However, this algorithm may be suboptimal in the situation of colored emitted signals. We propose an enhanced distributed adaptive direct position determination (EDA-DPD) algorithm. Simulations validate that the proposed EDA-DPD outperforms the D-ADPD in colored emitted signals scenarios and has the similar performance with the D-ADPD in white emitted signal scenarios.

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.