In this paper, a high-accuracy localization problem under a complex non-line-of-sight (NLOS) condition is addressed by a new method that utilizes multiple NLOS paths to improve localization accuracy, as opposed to the traditional method of suppressing them. The spatial multi-path array fusion localization model is presented and analyzed, followed by an angle-of-arrival (AOA) and time-of-arrival (TOA) algorithm based on spatial multi-information fusion that seeks to improve localization accuracy. Multi-path of spatial signals, measurement of the multi-element antenna, and geographic environment information are integrated into the proposed method for localization optimization. Simulation experiments were carried out, and the results revealed that the proposed algorithm is capable of making full use of spatial multi-location information for localization, thus improving the accuracy of localization in a the NLOS environment effectively and increasing the locatable probability of complex environment localization applications.

Unmanned Aerial Vehicle (UAV) technology is increasingly gaining attention in localization systems due to its flexibility and mobility. However, traditional localization techniques often fail in complex environments where line-of-sight paths are obstructed. To address this challenge, this paper presents an innovative UAV-assisted high-precision multi-target localization system. The system utilizes UAVs equipped with Reconfigurable Intelligent Surfaces to create a reflective signal path, allowing a receiver sensor to capture these signals, creating favorable conditions for multi-target localization. Exploiting the sparsity of signals, we introduce a direct positioning algorithm that leverages Atomic Norm Minimization (ANM) to estimate the target’s location. To address the high complexity of traditional ANM methods, we propose a novel Coyote-ANM-based direct localization (CADL) approach. This method combines the coyote optimization algorithm with the alternating direction method of multipliers to achieve high-accuracy positioning with reduced computational complexity. Simulation results across various signal-to-noise ratio scenarios demonstrate that the proposed algorithm significantly improves localization accuracy, achieving lower root mean square error values and faster execution times compared to traditional methods.

In passive localization techniques, as the scale of the array of the sensors used increases, the source distribution may be a coexistence of near-field (NF) and far-field (FF) sources. Most of the existing algorithms dedicated to the localization of mixed-field sources are based on a simplified model, which has model errors and cannot make good use of non-circular properties when non-circular signals are present in the sources. In this paper, we present a mixed-field circular and non-circular source localization algorithm based on exact spatial propagation geometry. First, we make an initial estimate of the source parameters using exact spatial geometry relations. The MUSIC algorithm is then used in combination with the non-circular properties of the signal to achieve an accurate estimate. The algorithm does not lose performance due to model mismatch and is able to make good use of the non-circular properties of the sources to improve the estimation accuracy. The simulation results show that the proposed algorithm can effectively distinguish between sources and that the algorithm performs satisfactorily.

Mutual coupling distorts the array manifold and can severely degrade direction-of-arrival (DOA) estimation by subspace-based methods. This paper develops a blind DOA-estimation framework for uniform circular arrays with unknown mutual coupling. By exploiting the complex symmetric circulant structure of the coupling matrix, the ambiguity mechanisms associated with conventional one-dimensional searches are first characterized, and practical criteria are established to reject spurious solutions. It is then shown that existing one-dimensional formulations estimate source directions separately and fail to fully exploit the coupling vector shared by all true DOAs. To address this limitation, two joint multidimensional optimization models are formulated to incorporate the common coupling structure. Newton-based and alternating-optimization procedures are derived, using the one-dimensional estimates for initialization. Simulation studies involving two to four sources demonstrate that the proposed joint formulations improve DOA-estimation accuracy over their one-dimensional counterparts under representative signal-to-noise ratios, array configurations, and coupling conditions, without requiring calibration sources or prior knowledge of the coupling coefficients. These results show that exploiting the shared coupling structure provides an effective approach to blind DOA estimation for uniform circular arrays.

Malware authors increasingly exploit API Hashing to create “invisible” system calls, replacing explicit function names with dynamically computed hashes that evade detection systems. This sophisticated obfuscation technique poses three critical challenges: accurately identifying hash functions within obfuscated code, linking computed hashes to their corresponding API calls, and detecting the growing diversity of hash algorithm variants. Existing rule-based approaches fail against these adaptive threats and cannot identify modern hash variants. We propose GAEDM , a novel framework that combines deep learning with program analysis to address these challenges. Our key innovation integrates static taint analysis with a genetic algorithm-enhanced assembly language model that generates diverse training variants, enabling robust detection of previously unseen obfuscation patterns. Experimental evaluation demonstrates that GAEDM achieves 91.9% MRR and 94.6% Recall@k in hash function identification, representing improvements of 18.4% and 8.2% respectively over state-of-the-art methods. GAEDM detects sophisticated obfuscation patterns that completely evade existing approaches, enabling security analysts to uncover previously undetectable threats and significantly advancing malware defense capabilities.

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