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In conventional sparse Space-Time Adaptive Processing (STAP) algorithms, the l 0 -norm is often used instead of the l 1 -norm to relax convexity constraints. The approach presented in this paper can be mathematically reformulated as a linear problem, which can be efficiently solved using various convex optimization techniques. While this method effectively circumvents the NP-Hard complexity associated with the l 0 -norm, it does come with certain potential challenges. For example, the observation matrix must meet the criteria of the Restricted Isometry Property (RIP) and other stringent conditions. In this research, a novel STAP algorithm is introduced, which is based on a non-convex penalty function. This innovative algorithm substitutes the traditional l 0 -norm with a custom-designed non-convex penalty function and is applied to the Direct Filter Processor (DFP), solved using the recursive least squares (RLS) algorithm. The resulting algorithm, known as g p -RLS, is demonstrated through Monte Carlo simulations to outperform other l 1 -based and reduced-rank STAP algorithms in terms of faster convergence, improved signal-to-clutter-plus-noise ratio (SCNR), and enhanced target detection performance. [ABSTRACT FROM AUTHOR]

Passive coherent location (PCL) has attracted widespread attention due to its low cost, good concealment, and strong anti-stealth. When multiple illuminators operating in a single frequency network are used in a PCL system for multitarget tracking, the three-dimensional (3D) data association uncertainty among the illuminators, measurements and targets has to be dealt with because it will significantly increase computational complexity and reduce scalability. To solve this problem, a scalable multi-association belief propagation (MA-BP) approach for multitarget tracking is proposed. By factorizing the joint probability density function, the tracking problem is described by a factor graph, in which two new illuminator-target oriented and measurement oriented association variables are defined to describe the 3D data association uncertainty. Furthermore, under the Gaussian assumption, a Gaussian version of MA-BP, named as multi-association Gaussian belief propagation (MA-GaBP), is implemented with a generalized virtual measurement model. The MA-GaBP approach can further reduce memory requirements and computational complexity. Simulation results show that the proposed approach effectively solves the 3D data association uncertainty, and has satisfied tracking accuracy and scalability. [ABSTRACT FROM AUTHOR]

The paper takes an in-depth analysis of the channel estimate model (CEM) in passive radar using the orthogonal frequency division multiplexing (OFDM) waveforms. The CEM has been intensively exploited in OFDM passive radar, where the channel estimates (CE) are obtained from the original digitized received signal (ODRS), and target detection is performed based on CEs. However, traditional CEM is derived neglecting the inter-carriers interference (ICI). The influence of the ICI on target detection has rarely been discussed previously. In fact, target with large power and Doppler frequency can induce strong ICI, which increases the noise floor, and thus imposes significant influence on the detection of the other targets, especially the weak targets. In this paper, we rederive the CEM taking the ICI into consideration, and obtain a new CEM. In the new CEM, a specific target has two components, i.e., the useful signal part, and ICI. We derive the useful signal to noise ratio (SNR) and ICI to noise ratio (ICINR) theoretically, and provide compact expressions. We reveal the relationship between the SNR and ICINR in the CE, and the SNR in the ODRS. Based on the theoretical derivation, the influence of ICI is analysed. We also discuss the elimination of the ICI. The influence of ICI can be eliminated by cancelling the target signal that induces the ICI from the CEM. A target signal cancellation method is developed based on the new CEM. Simulations demonstrate the effectiveness of the theoretical analysis of the CEM and the proposed cancellation method. [ABSTRACT FROM AUTHOR]

We propose a novel intelligent reference signal extraction system for DRM-based passive radar. By fully using prior knowledge of the received signal, a pilot conditional reference signal extraction network (PCRSENet) is designed to replace both conventional channel estimation and signal equalization. The PCRSENet estimates the channel state information (CSI) implicitly and outputs reference signals directly. Meanwhile, what makes it challenging to utilize deep learning in reference signal extraction of passive radar is not only the limited measured data but also the truth transmission signals and CSI being unknown. So, the least square (LS) estimator-aided training sets generation method is employed, which obtains the CSI from measured data by LS estimator as prior knowledge and then simulates DRM signals as accurate labels of training sets. We evaluate the proposed method on measured data, and the experimental results show that the proposed method works effectively for target detection in DRM-based passive radar with different working parameters when the training and test sets are in the same transmission mode. [ABSTRACT FROM AUTHOR]

In this paper, we investigate low probability interception (LPI) in passive phased array radars. Power control and transmit beamforming (BF) are used to minimize the received power by the interceptor. Meanwhile, desired target detection performances and the limited power budget of radar are guaranteed. The LPI problem is solved analytically and some theorems are derived for the design of optimum BF weights. Two LPI optimization problems from different perspectives are formulated and solved analytically in preparing closed-form solutions for designing transmit BF weights. LPI performance and computation time of the proposed algorithms are compared with that of the benchmark algorithms. Simulation results show that proposed algorithms not only minimize the interception probability of radar in comparison with benchmark algorithms but also satisfy desired detection performance and power budget constraints simultaneously. The proposed algorithms outperform the standard numerical Barrier algorithm in complexity and optimality. [ABSTRACT FROM AUTHOR]