Ship target detection using radar is an important application in military and civilian fields. For the polarization estimation of scattering waves in the underdetermined case, i.e., the number of scattering waves from ships is larger than the number of sensors, this paper proposes two estimation methods with different measurement models. 1) For the single-vector-sensor model, this paper proposes the polarization-invariant ESPRIT-based method. This method can estimate the polarization of signals containing target echo, interference, and noise, which can cure the problem that the accuracy of existing method is poor under low interference signal ratio. 2) For the multi-vector-sensor model, this paper proposes an improved ESPRIT method based on the spatial-invariant and time-invariant simultaneously, which can increase the degree of freedom without increasing hardware cost. As for another problem of multi-vector-sensor, i.e., almost all existing methods assume that the number of scattering waves is known, this paper introduces the polarization spectrum for the first time, which can estimate the polarization parameters when the number of scattering waves is unknown. Finally, we analyze the two proposed ESPRIT-based methods comparing with some existing methods through Monte Carlo simulation, which results demonstrate the efficience of the proposed methods.

Owing to the shortcomings of single view angle and insufficient information in the monostatic radar, it is difficult to effectively suppress the deception-false-target (FT) jamming. To counter the range-velocity compound deception jamming, a suppression method based on the radar position error (RPE) is proposed for active $/$ passive netted radar on different moving platforms. In this algorithm, FTs are discriminated in the two stages of track initiation and target tracking. Firstly, in the track initiation stage, the RPE is taken into consideration to construct the error covariance matrix of the Mahalanobis distance. Then, the chi-square test is used to make the measurement correlation based on the azimuth and pitch angle information, which eliminates some FTs. Secondly, the measurements in active radar, which has been successfully correlated, are regarded as the supplementary for track initiation of passive radar. The FTs are discriminated again based on the radial velocity information. Finally, in the target tracking stage, the targets are tracked independently in active radar and passive radar. The sequential track correlation method is adapted to eliminate the remaining FTs that have been maintained in active radar. Simulation results verify that the proposed method improves the discrimination probability of FTs under the condition of ensuring the high discrimination probability of the physical target (PT).

Compared with the multisite radar, the monostatic radar has the disadvantages of a single viewing angle and less available information. In the presence of main-lobe jamming, the monostatic radar has the problems of poor anti-jamming effect and difficulty in achieving target detection. To solve the above problems, the radar signal processing method and data fusion method are combined in this paper. A main-lobe jamming suppression method for phased array (PA) netted radar based on the maximum signal-to-noise ratio blind source separation (MSNR-BSS) is proposed. Firstly, the received signals of a monostatic PA radar are separated into target signals and jamming signals by the MSNR-BSS method. Secondly, channel accumulation, pulse compression, and constant false-alarm rate (CFAR) target detection are performed on the separated signal channels to obtain the target radial distance. Finally, the data fusion theory of three global orientations is employed to determine the target spatial position. Simulation experiments evaluate the performance of main-lobe jamming suppression and verify the effectiveness of the proposed method. In the presence of noise amplitude modulation (NAM) jamming or smeared spectrum (SMSP) jamming (JNR=100dB), the target positioning accuracy of the proposed method is 6.613m or 6.596m. And the peak sidelobe level ratios (PSLR) in the two jamming environments are approximately equal to - 8dB.

Mainlobe suppression jamming significantly degrades radar detection performance. The conventional blind source separation (BSS) algorithms often fail under high-jamming-to-signal-ratio (JSR) and low-signal-to-noise-ratio (SNR) conditions. To overcome this limitation, we propose an enhanced BSS method combining variational mode decomposition (VMD) and wavelet packet decomposition (WPD), termed VMD-WPD-JADE. The proposed approach first applies VMD-WPD for noise reduction in radar signals and then utilizes the JADE algorithm to compute the separation matrix of the denoised signals, effectively achieving blind source separation of radar echoes for interference suppression. We evaluate the method using noise-amplitude modulation and noise-frequency modulation jamming scenarios. The experimental results show that at a JSR = 50 dB and an SNR = −5 dB, our method successfully separates the target signals. Compared with the conventional blind source separation (BSS) algorithms, the proposed technique demonstrates superior robustness, achieving a 4–11% improvement in the target detection probability under noise-amplitude modulation (NAM) jamming and a 4–16% enhancement under noise-frequency modulation (NFM) jamming within a signal-to-noise ratio (SNR) range of −5 dB to 5 dB.

Uniform circular array electromagnetic vector sensor (UCA-EVS) can provide omni-directional and high-resolution azimuth information and polarization information, which has been widely concerned in the field of radar signal processing. Aiming at the joint estimation of direction of arrival (DOA) and polarization parameters of coherent sources via UCA-EVS, a multi-dimensional parameter estimation method with low complexity—polarization-DOA matrix method—is proposed. First, the rank of array covariance matrix is recovered by axial virtual translation, and then the direction and polarization parameters of the signal are estimated by using the eigenvalues and eigenvectors of the matrix based on constructing polarization-DOA matrix. Different from the traditional DOA matrix method, the proposed algorithm can not only estimate the azimuth information of the signals but also provide the polarization information of the targets, and the estimated parameters can be matched automatically. At the same time, it can estimate the parameters only by using the information of three elements, which can save hardware resources. In addition, the proposed method does not need to search for spectral peaks, which not only greatly reduces the computational complexity but also loses the estimation accuracy. Simulation results verify the feasibility of the proposed algorithm.

Research on sea clutter modeling is meaningful for the sea clutter jamming rejection in radar detection. The previous methods cannot fit the characteristics such as the peak value and amplitude width of the sea clutter amplitude distribution curves. Thus, an estimation method of sea clutter based on the multi-feature-point model validation is proposed. First, the amplitude distribution characteristics and temporal correlation of sea clutter are analyzed, and the spherically invariant random process is used to simulate the K-distribution sea clutter model. Then, six feature points are constructed, (i.e., the maximum probability density, the amplitude value of maximum probability density, the 3 dB amplitude width, the amplitude widths corresponding to 1/3, and 2/3 of the maximum probability density, and the amplitude critical value corresponding to probability density lower than 0.01). Based on the multigroup feature points of the amplitude distribution curves, the radial basis function (RBF) neural network is trained to derive the relationship between the multiple features and the shape parameter and predict the shape parameter of the measured data. Finally, the simulation results show that the proposed method can fit the characteristics of the actual sea clutter amplitude distribution more accurately than the previous estimation methods.