Dekang Liu

This paper investigates joint deployment of unmanned aerial vehicle (UAV)-borne linear-array angle-of-arrival (AOA) sensors for localizing a target UAV in three-dimensional space. Since each sensing UAV carries a lightweight one-dimensional (1-D) AOA array, each measurement provides only one angular constraint, and its information contribution depends jointly on the UAV waypoint and array pointing direction. This leads to a coupled coordinate–orientation design problem that differs from conventional full-AOA deployment. We formulate a Cramér–Rao lower bound (CRLB)-based framework under A- and D-optimality criteria, covering both free-flight and constrained hovering regions. By exploiting the structure of the 1-D AOA Fisher information matrix, we show that, for fixed UAV coordinates, the orientation block can be exactly eliminated through a low-dimensional eigenproblem. The resulting reduced coordinate problem is then solved by a geometry-structured sequential quadratic programming (SQP) method, whose curvature model captures the radial and tangential sensitivities induced by line-of-sight geometry. Numerical simulations further validate the effectiveness of the proposed approach.

To strike a balance between complexity and performance in Minimum Shift Keying (MSK) systems for underwater electromagnetic communication, we propose a low-complexity maximum-likelihood (ML) noncoherent demodulation method. By integrating a resource reuse mechanism with a confidence-driven adaptive extension strategy, the proposed method significantly reduces computational resource consumption while maintaining near-optimal demodulation performance. Simulation results demonstrate that the bit-error-rate (BER) performance of the proposed method approaches that of the traditional fixed length ML receiver when the confidence threshold is set to 0.1. Meanwhile, the proposed method reduces complex correlation operations by 96.2% and complex addition operations by 87.1%, achieving minimal average computational overhead. Furthermore, we evaluate the method under frequency-flat Rayleigh fading channels, and the results confirm that the proposed method retains its performance advantage and complexity reduction under fading, supporting its potential for reliable underwater communication.

This letter addresses joint localization of 3D mobile networks with asynchronous clocks, where both relative distance and relative velocity measurements are used. Clock offsets and drifts are explicitly modeled, and an anchor-based compensation term is introduced to fully exploit anchor information and resolve translation ambiguity. A block majorization–minimization algorithm, termed vBMM, is proposed to jointly estimate node positions and clock parameters. Simulation results show that vBMM achieves position accuracy close to the Cramér–Rao lower bound and remains stable under different clock conditions. Compared with the state-of-the-art algorithm, vBMM achieves higher localization accuracy when clock asynchrony is significant and maintains robustness across clock conditions and measurement noise levels.

Relative positioning is critical for collaborative operations among unmanned aerial vehicles(UAVs). This study proposes a relative positioning algorithm for Dual-UAV that utilizes inertial navigation systems(INS) and opportunistic communication signals to obtain angle-of-arrival (AOA) measurements in Global Navigation Satellite System(GNSS)-denied environments. Both AOA measurements and INS-derived attitude and displacement measurements are corrupted by noise, introducing statistical bias relative to the Cramér-Rao lower bound(CRLB). To mitigate this bias, we calibrate weighting matrix parameters using relative position estimates, enhancing accuracy through iterative refinement. Theoretical analysis and simulations demonstrate the algorithm asymptotically attains CRLB performance.

This paper addresses the 3D relative localization problem for two unmanned aerial vehicles (UAVs) using a combination of time-of-arrival (TOA) and angle-of-arrival (AOA) measurements across varied flight trajectories. We commenced by examining the problem of relative attitude estimation using only time-of-arrival (TOA) measurements, taking into account a distance-dependent noise model. To address this issue, we constructed a constrained weighted least squares (CWLS) problem and applied semidefinite relaxation (SDR) techniques for its resolution. Furthermore, we extended our analysis to incorporate AOA measurements and scrutinize the Cramer–Rao Lower Bound (CRLB) to illustrate enhanced localization accuracy through TOA-AOA integration compared to TOA alone under stable trajectory conditions. Ultimately, numerical simulations substantiate the efficacy of the proposed methodologies.

This paper focuses on the problem of utilizing millimeter wave (mmWave) and Terahertz (THz) massive hybrid arrays to serve multiple users simultaneously. The mmWave and THz massive arrays are characterized by wide bandwidth and high gain, leading to extensive application prospects. Moreover, a hybrid structure array can combine multiple antenna signals through the phase shifter network. Compared with the full digital array, it is a cost-effective technique that can be functional with fewer radio frequency (RF) chains. However, owing to the adoption of a discrete Fourier transform (DFT) structure, most traditional massive hybrid arrays, which allocate one chain to each user, are restricted to scenarios where the number of RF chains is more than that of users. Otherwise, even users with ideal channel conditions and short distances are inherently difficult to assign an independent chain. Thus, it will limit the scale of users that the base station (BS) can support. Inspired by the above analysis, this paper develops a method to provide service for more users with limited RF chains. Firstly, an analog matrix designing method based on the minimax criterion, which enables arrays access to multiple users, is proposed to guarantee each user a good array gain. Secondly, we establish a receiver designing scheme by the GAMP algorithm to receive signals from multiple users at the same time. Additionally, good bit error rate (BER) performance can be obtained under the condition that the observation matrix is not of full rank. Finally, numerical simulations demonstrate the effectiveness of our proposed method.