In this paper, we propose a novel sparse signal representation (SSR) based algorithm called the ℓ1-PSRCM with cocentered orthogonal loop and dipole (COLD) array to estimate two-dimensional (2D) direction of arrival (DOA) and polarization parameters. Considering the characteristics of polarization sensors, a polarized sparse representation model of covariance matrix is constructed, whose overcomplete dictionary and sparse coefficient matrix only depend on DOA and polarization parameters, respectively. In so doing, the proposed algorithm can make full use of the spatial and polarized information contained in the received data, thereby improving the estimation accuracy. In addition, to reduce the computational complexity and suppress the effect of noise, the modified ℓ1-PSRCM algorithm with the real-valued sparse coefficient matrix and noise-free sparse representation model is proposed. Finally, we present the twodimensional multiresolution grid refinement (2D-MGR) method to reduce the heavy computation burden when the spatial grid is dense. Simulation results validate the superiority of the proposed algorithms.

Polynomial phase signals (PPSs) have numerous applications in many fields including radar, sonar, geophysics, and radio communication systems. Therefore, estimation of PPS coefficients is very important. In this paper, a novel approach for PPS parameters estimation based on adaptive short-time Fourier transform (ASTFT), called the PPS-ASTFT estimator, is proposed. Using the PPS-ASTFT estimator, both one-dimensional and multi-dimensional searches and error propagation problems, which widely exist in PPSs field, are avoided. In the proposed algorithm, the instantaneous frequency (IF) is estimated by S-transform (ST), which can preserve information on signal phase and provide a variable resolution similar to the wavelet transform (WT). The width of the ASTFT analysis window is equal to the local stationary length, which is measured by the instantaneous frequency gradient (IFG). The IFG is calculated by the principal component analysis (PCA), which is robust to the noise. Moreover, to improve estimation accuracy, a refinement strategy is presented to estimate signal parameters. Since the PPS-ASTFT avoids parameter search, the proposed algorithm can be computed in a reasonable amount of time. The estimation performance, computational cost, and implementation of the PPS-ASTFT are also analyzed. The conducted numerical simulations support our theoretical results and demonstrate an excellent statistical performance of the proposed algorithm.

In bearings-only localization, clustering-based methods have been widely used to remove spurious intersections by fusing multiple bearing measurements from different observation stations. Existing clustering methods, including fuzzy C-mean (FCM) clustering and density-based spatial clustering of applications with noise (DBSCAN), must specify the number of clusters and the threshold for defining the neighborhood density, respectively, which are always unknown and difficult to estimate. Moreover, in dense radiation source scenes, existing clustering methods for removal of spurious intersections all deteriorate significantly. Therefore, we propose a novel density-based clustering method called K-M-DBSCAN, which combines the minimum K distance algorithm with Mahalanobis distance-based DBSCAN clustering. Firstly, K-M-DBSCAN uses minimum K distance algorithm for preprocessing to remove most of the spurious intersections and reduce the computational complexity of clustering. Mahalanobis distance-based DBSCAN is used for clustering and spurious intersections recognition. In order to adapt the large variations of sample density in clustering, we use Mahalanobis distance to define an explicit neighborhood of DBSCAN instead of traditional Euclidean distance. Simulation results show that the proposed K-M-DBSCAN performs better than FCM and DBSCAN in removing of spurious intersections.

In the complex battlefield environment, stealth radar target recognition has been paid increasing attentions. Previous studies have demonstrated that the stealth target can be identified well by pole extraction based on matrix pencil method (MPM). However, MPM suffers from the difficulty in setting model order and the time-domain resonant response aliasing problem. This paper proposes a new sliding-window matrix pencil method (SW-MPM) based on dynamic order setting and sliding window. Dynamic order setting scheme is used to overcome the difficulty of setting the order in matrix pencil method, while sliding window can avoid the aliasing problem of time-domain resonant response to some extent. The time-domain scattering data used in SW-MPM are obtained by inverse Fourier transform of the frequency domain scattering data in the resonance region. Taking typical stealth aircraft identification as example, the simulation results verify that the proposed method may extract more number of poles with better azimuth consistency, which is beneficial to improve the accuracy of pole-based target identification.