This paper proposes a reduced-dimension polarization-space-time adaptive processing (RD-PSTAP) algorithm for airborne conformal array (CFA) radar to achieve better clutter suppression performance at lower computational complexities. Firstly, we take polarization information into signal modeling. Subsequently, the dimension reduction matrix is derived based on the generalized sidelobe cancellation (GSC) structure, which incorporates the polarization information of the target and clutter into constructing the main channel and auxiliary channels, respectively. Simulation results demonstrate the effectiveness of the proposed algorithm compared with the conventional RD algorithm.

In this paper, for the application of multi-target tracking (MT-T), a joint node and resource scheduling (JNRS) strategy based on convex programming is proposed for the distributed multiple-input multiple-output (MIMO) radar network. To be more precise, the JNRS strategy is first formulated as a mathematic optimization problem, which is NP-hard. Then, an iterative and efficient solution technique incorporating the sequential convex programming (SCP) and the semi-definite programming (SDP) is proposed to tackle the NP-hard problem. Finally, numerical simulation results are provided to verify the effectiveness of the proposed JNRS strategy. Furthermore, the proposed JNRS strategy can achieve better MTT performance than other existing benchmark strategies.

In this paper, we propose a collaborative resource allocation and beampattern optimization (CRABO) strategy for multiple maneuvering targets tracking (MMTT) in the network of multiple colocated MIMO radar (CMR) nodes. Specifically, the CRABO strategy is formulated as a nonconvex optimization problem, where the node-target assignment, beampattern synthesis and bandwidth allocation are jointly optimized for the first time. To tackle the nonconvex problem efficiently, a fast sequential and decoupling-based three-stage solution algorithm that fully meets real-time requirement is developed. Numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed CRABO strategy.

If the pointing directions of antenna elements are inconsis-tent, the received signal of each sensor will vary due to the polarization effect, and this will influence the target detection performance. To analyze this problem, we establish an irreg-ular array structure and give a signal model for this structure. Then we propose a polarimetric detector based on two-step GLRT in Gaussian clutter background. By conducting ex-periments based on simulated clutter data, we prove that the proposed fully adaptive detector has constant false alarm rate (CFAR) property, and the detector has a better performance for low-speed target detection compared with regular array structures.

In this paper, we address the polarization diversity detection problem in the non-Gaussian sea clutter environment. Considering the heterogeneous characteristic of the sea clutter, we model it as the compound Gaussian distribution. Based on the two-step generalized likelihood ratio test (GLRT), Rao test, and Wald test, we propose three persymmetric and polarimetric detectors. In detail, we first assume that the polarimetric clutter covariance matrix (PCCM) is known, and we develop three test statistics of the proposed detectors. In addition, we use persymmetric property to estimate the PCCM, and obtain the adaptive persymmetric detectors by inserting the estimate of PCCM into first-step test statistics. The experiments are conducted by using the simulated data and the real sea clutter data to verify the performance of the proposed persymmetric polarimetric detectors.

This letter presents the derivation of the generalized likelihood ratio test (GLRT)-based polarimetric detector in the non-Gaussian clutter. We model the non-Gaussian sea clutter as compound-Gaussian distribution with inverse-Gaussian texture (IG-CG), which has better goodness-of-fit for the high-resolution sea clutter. Based on the two-step GLRT criterion, we develop the test statistic of the proposed detector by assuming the texture component is known in the first step. In the second step, with the texture of the secondary data estimated as the power of the clutter, we insert the maximuma posterioriestimate (MAPE) of the texture of the primary data into the test statistic to achieve the fully adaptive detection. Furthermore, the model-based polarimetric detector, which exploits the independence between the co-polarized and the cross-polarized component, is derived. Finally, we conduct experiments with simulated clutter and real sea clutter to evaluate the performance of the proposed detector. The simulation results verify that the proposed detectors have better detection performance and the MAPE under different hypotheses leads to different levels of robustness to the mismatched signal.