This letter deals with the problem of robust target localization in distributed multiple-input multiple-output (MIMO) radar using bistatic range measurements contaminated by outliers. Motivated by the robustness of nonconvex ℓ _p -norm for outlier rejection, in this letter, we reformulate the target localization problem as a nonconvex ℓ _p -norm minimization of residual matrix with nonconvex quadratic constraints. However, the resulted problem is very challenging. We consider the use of iterative reweighting algorithms, which approximates the nonconvex problem by a sequence of tractable subproblems. In particular, a new weight update method is proposed to accommodate the solving algorithm of the subproblem and avoid the selection of a regularization parameter, leading to an improved iterative reweighting (ℓ _p -IIRW) solution. Numerical results demonstrate substantially enhanced robustness and improved positioning accuracy of the proposed method in both cases of low signal-to-interference-plus-noise ratio (SINR) outliers and non-line-of-sight (NLOS) outliers.

Interference mitigation is crucial for ensuring the reliability of Global Navigation Satellite System (GNSS) in complex electromagnetic environments. However, GNSS receivers frequently encounter various types of interference from radar, communication systems, airfields, and unmanned aerial systems. While existing interference mitigation methods for single-antenna GNSS receivers are only suppress specific types of interference. This paper proposes a multi-interference mitigation method based on the time-frequency Universal Network(TF-Unet) network. The TF-Unet method enables fine-grained segmentation based on high-resolution TF images, sensing and mitigating multidimensional interference features within TF domain. This method employs a signal fusion mechanism that optimizes interference mitigation by minimizing the TF Equivalent Mean Square Error (TFEMSE), thereby enhancing interference edge detection probability and reducing spectral impacts on GNSS signals. Leveraging high-resolution TF analysis, this approach constructs the first GNSS TF signal dataset encompassing multiple interference types and enables phase-aligned GNSS signal reconstruction. Simulations on both conventional and power-enhanced GNSS signals demonstrate that the proposed method effectively suppresses narrow-band interference (NBI), linear frequency sweeping interference (LFSI), and pulse interference (PI) by maximum 10dB·Hz carrier-to-noise ratio(C/N₀) gain compared to conventional methods. Practical experimental further validated that the positioning accuracy can be improved by 27%.

Analog neural networks are highly effective to solve some optimization problems, and they have been used for target localization in distributed multiple-input multiple-output (MIMO) radar. In this work, we design a new relaxed energy function based neural network (RNFNN) for target localization in distributed MIMO radar. We start with the maximum likelihood (ML) target localization with a complicated objective function, which can be transformed to a more tractable one with equality constraints by introducing some auxiliary variables. Different from the existing Lagrangian programming neural network (LPNN) methods, we further relax the optimization problem formulated for target localization, so that the Lagrangian multiplier terms are no longer needed, leading to a relaxed energy function with better convexity. Based on the relaxed energy function, a RNFNN is implemented with much simpler structure and faster convergence speed. Furthermore, the RNFNN method is extended to localization in the presence of transmitter and receiver location errors. It is shown that the performance of the proposed localization approach achieves the Cramér-Rao lower bound (CRLB) within a wider range of signal-to-noise ratios (SNRs). Extensive comparisons with the state-of-the-art approaches are provided, which demonstrate the advantages of the proposed approach in terms of performance improvement and computational complexity (or convergence speed).

Unmanned aerial vehicles (UAVs) have become indispensable in both civilian and military applications. However, their reliance on Global Navigation Satellite System (GNSS)-based navigation exposes them to escalating threats from sophisticated jamming and spoofing attacks. These threats severely compromise operational safety and mission integrity, necessitating the development of effective countermeasures. This survey provides a comprehensive overview of GNSS interference threats specific to UAV navigation, with a focus on the analysis of jamming and spoofing attacks, and summarizes state-of-the-art techniques for interference detection and mitigation. We first review the principles of satellite navigation for UAVs and identify the inherent vulnerability of GNSS signals to interference. Then, we systematically examine existing countermeasures, including signal processing-based, array antenna-based, and artificial intelligence-based approaches, highlighting their effectiveness against jamming and spoofing. Despite these advances, significant challenges remain in ensuring robust UAV navigation in adversarial electromagnetic environments, particularly those arising from resource constraints, limited algorithmic flexibility, and the lack of UAV-specific countermeasures. To address these issues, we propose a hierarchical framework that integrates robust signal reception, intelligent algorithm optimization, and system-level collaboration. This framework provides practical strategies for enhancing the resilience of next-generation UAV navigation systems to complex interference.

Satellite communications are pivotal to global Internet access, connectivity, and the advancement of information warfare. Despite these importance, the open nature of satellite channels makes them vulnerable to eavesdropping, making the enhancement of interception resistance in satellite communications a critical issue in both academic and industrial circles. Within the realm of satellite communications, polarization modulation and quadrature techniques are essential for information transmission and interference suppression. To boost electromagnetic countermeasures in complex battlefield scenarios, this paper integrates multi-parameter weighted-type fractional Fourier transform (MP-WFRFT) with directional modulation (DM) algorithms, building upon polarization techniques. Initially, the operational mechanisms of the polarization-amplitude-phase modulation (PAPM), MP-WFRFT, and DM algorithms are elucidated. Secondly, it introduces a novel variable polarization-amplitude-phase modulation (VPAPM) scheme that integrates variable polarization with amplitude-phase modulation. Subsequently, leveraging the VPAPM modulation scheme, an exploration of the anti-interception capabilities of MP-WFRFT through parameter adjustment is presented. Rooted in an in-depth analysis of simulation data, the anti-scanning capabilities of MP-WFRFT are assessed in terms of scale vectors in the horizontal and vertical direction. Finally, exploiting the potential of the robust anti-scanning capabilities of MP-WFRFT and the directional property of antenna arrays in DM, the paper proposes a secure transmission technique employing directional variable polarization modulation with MP-WFRFT. The performance simulation analysis demonstrates that the integration of MP-WFRFT and DM significantly outperforms individual secure transmission methods, improving anti-interception performance by at least an order of magnitude at signal-to-noise ratios above 10 dB. Consequently, this approach exhibits considerable potential and engineering significance for its application within satellite communication systems.

As a range-based localization approach, elliptic localization based on bistatic ranges (BRs) has wide range of applications in multistatic systems. This paper addresses the problem of robust elliptic localization using the minimum $\ell _{p}$ -norm criterion in scenarios affected by impulsive $\alpha$ -stable noise. To solve the $ \ell _{p}$ -norm formulation, we develop an asymptotically efficient two-stage iteratively reweighted least squares (TSIRLS) estimator via incorporating pseudolinear estimation and multi-stage weighted least squares estimation into the framework of iteratively reweighted least squares (IRLS) methods. The estimator consists of two cascaded $ \ell _{p}$ -norm minimization estimators. To obtain a closed-form solution at each iteration, we transform nonlinear BR measurement equations into pseudolinear forms through introducing auxiliary variables, thus resulting in an iteratively reweighted pseudolinear least squares (IRPLS) estimator in the first stage. The dependencies of unknown parameters and the estimate error derived from the IRPLS are utilized in the second stage to enhance the performance of target location estimate. The analytical derivation demonstrates that, under the assumptions of a sufficiently large number of measurements and small noise, the final position estimate obtained from the TSIRLS achieves the theoretical covariance of the general $ \ell _{p}$ -norm minimization estimation. Extensive numerical simulations highlight the advantages of TSIRLS over existing least squares and robust estimators in terms of positioning performance and runtime. The TSIRLS is also observed to generate approximately unbiased estimates with mean square errors that closely approach the Cramér-Rao lower bound.