KEYWORDS: Polarization, Matrices, Covariance matrices, Monte Carlo methods, Signal to noise ratio, Error analysis, Computer simulations, Eigenvectors, Detector arrays, Chemical elements
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.
KEYWORDS: Polarization, Signal processing, Covariance matrices, Signal to noise ratio, Monte Carlo methods, Statistical analysis, Smoothing, Interference (communication), Detection and tracking algorithms, Radar signal processing
Aiming at the problem of accurate estimation of direction-of-arrival(DOA) and polarization parameters of coherent sources by anti-radiation seeker under polarization sensitive circular array, this paper proposes a DOA and polarization parameter estimation method based on peak comparison(PC) and oblique projection(OP) transformation. At first, the polarization forward smoothing(PFS) method is used to realize the decoherence of array received signal under uniform circular array(UCA). Then, based on the PC strategy, the spectrum peak of the two-dimensional spatial spectrum is searched to achieve the direction finding of the target. Finally, using the estimated DOA information of the signal source, multiple coherent signals are decomposed into single signal units based on the OP transformation, and the polarization parameters of each signal unit are estimated. On the one hand, the proposed method speeds up the two-dimensional spectral peak search, on the other hand, it solves the problem that the PFS algorithm cannot estimate the polarization parameters, realizes the automatic pairing of DOA and polarization parameters, and has high estimation accuracy. The effectiveness of the proposed method is verified by simulation.
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.
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