The non-collocated dual-polarized array alleviates the mutual coupling between the orthogonally polarized antennas. Nevertheless, the enlarged inter-element spacing may bring ambiguity issue, and the conventional direction-of-arrival (DOA) estimation algorithms may require varied processing approaches for different signal environments. For these problems, this paper first introduces two types of non-collocated array configurations called the interleaved array and the side-by-side array. The former helps to improve the aperture efficiency, and the latter can enable distributed applications. Subsequently, a polarization-agnostic DOA estimation method is proposed. In this method, firstly the low-dimensional array interpolation is performed via the joint minimization of the covariance matrix rank and a whitened residual error term. Then, both the auto- and cross-covariance matrices of the interpolated subarrays are employed to extract the signal subspace unrelated to the polarization parameters, based on which the fast direction finding algorithm is exploited. An alternating direction method of multipliers (ADMM) based solver is proposed to accelerate the array interpolation. The proposed method can handle the type of completely polarized (CP) signals, the type of partially polarized (PP) signals, the type of a spatial mixture of CP and PP signals, and the type of a temporal mixture of CP and PP signals without additional polarization-dependent processings. It is also compatible with the classical sparse array like the coprime array. Additionally, the extension to the 2D case is introduced. Numerical simulations are provided to demonstrate its superior performance in comparison to other representative approaches.

Due to the complexity of underwater environments, underwater target recognition based on radiated noise has always been challenging. This paper proposes a multi-scale frequency-adaptive network for underwater target recognition. Based on the different distribution densities of Mel filters in the low-frequency band, a three-channel improved Mel energy spectrum feature is designed first. Second, by combining a frequency-adaptive module, an attention mechanism, and a multi-scale fusion module, a multi-scale frequency-adaptive network is proposed to enhance the model’s learning ability. Then, the model training is optimized by introducing a time–frequency mask, a data augmentation strategy involving data confounding, and a focal loss function. Finally, systematic experiments were conducted based on the ShipsEar dataset. The results showed that the recognition accuracy for five categories reached 98.4%, and the accuracy for nine categories in fine-grained recognition was 88.6%. Compared with existing methods, the proposed multi-scale frequency-adaptive network for underwater target recognition has achieved significant performance improvement.

Orthogonal Time Frequency Space (OTFS) modulation offers a promising solution for Vehicle To Everything (V2X) scenarios. In this letter, we present an algorithm that reduces pilot overhead and improves link reliability, which enhances spectral efficiency in IRS (Intelligent Reflecting Surface)-assisted OTFS systems. The method projects received signals onto orthogonal subspaces and performs channel matching within each subspace to minimize pilot overhead. And we utilize the Grey Wolf Optimization (GWO) Algorithm to calculate the channel matching parameters. Additionally, an adaptive pilot overhead reduction(APOR) scheme is presented to further enhance spectral efficiency. Simulation results verify that the proposed methods significantly improve system reliability and throughput rate.

Recent research on uncrewed aerial vehicle (UAV) localization has focused on enhancing accuracy by incorporating angle information. However, this requires array antennas on UAVs, which increases costs and reduces flight endurance. To address this, we propose a distributed, collaborative localization method for UAV swarms, integrating reconfigurable intelligent surface (RIS) with multidimensional scaling (MDS). This method relies solely on inter-UAV ranging and a subset of UAVs equipped with global positioning system (GPS). The proposed algorithm improves localization by leveraging inter-UAV ranging, RIS, and GPS-equipped UAVs. After swarm patching, an improved node selection algorithm, based on the Nyström approximation, directs RIS-equipped UAVs to designated areas. The UAV distance matrix is refined using the $3\sigma $ Gaussian principle and Rice distribution criteria, while MDS computes relative coordinates within each patch. Simulations show that the algorithm reduces localization error, achieving over 10% improvement using multiple RIS-equipped UAVs. Furthermore, the proposed algorithm reduces GPS-equipped UAV requirements by deploying passive RIS-equipped UAVs, extending swarm operation duration and enhancing localization accuracy.

In non-orthogonal multiple access (NOMA) systems, the overall bit error rate (BER) is highly influenced by power allocation. This letter investigates the relationship between the optimal power allocation factor and user path losses in a downlink Rician fading channel, with the objective of minimizing the overall BER. To achieve this, the exact BER expressions for a downlink NOMA system under imperfect successive interference cancellation (SIC) are derived. An analytical relationship between the power allocation factor and path loss is then established by applying an exponential $Q$ -function approximation, performing analytical averaging over Rician fading, and employing a piecewise Taylor expansion. Numerical and Monte Carlo simulations validate the obtained relationship.

In order to address the challenges of unknown initial positions and accumulated long-distance positioning errors in Pedestrian Dead Reckoning (PDR), as well as significant ranging errors caused by non-line-of-sight (NLOS) conditions in Ultra-Wideband (UWB) localization, this paper proposed a PDR and UWB fusion positioning algorithm based on adaptive gradient descent, briefly as AGD-PDR-UWB, for the indoor positioning scenarios where embedded terminals have limited resources. Firstly, the initial pedestrian position is estimated using the least-squares method based on UWB-ranging information from three base stations to solve the problem of unknown initial positions in PDR. Secondly, a novel method, which evaluates the degree of NLOS based on PDR positioning results and UWB-ranging information from two consecutive steps, is proposed to identify NLOS conditions in UWB signals. Finally, the proposed NLOS recognition method is used to adjust the weight of the loss function in the fusion positioning process, and the optimal pedestrian position that minimizes the loss function is obtained by the adaptive gradient descent method. Experimental results demonstrate that the proposed AGD-PDR-UWB algorithm outperforms four other comparative algorithms in two different positioning scenarios. Under the condition of three UWB base stations, the positioning error is within 0.5 m, effectively mitigating the cumulative errors of PDR and the positioning errors caused by NLOS conditions in UWB.