Networking Radar-Communication Integrated System (RCIS) promises an improved performance of radar and communication system platform by expanding scanning range and sharing information with each other. But equipping RCIS with directional antennas, in return, will worsen network's connectivity. In this paper, we aim to address the problem of neighbor discovery stage in networking process. Then, we are to present a directional neighbor discovery algorithm called scan based networking radar algorithm, or, SBA-RA. The difference between SBA-RA and previous researches which assume no prior knowledge of neighbors, is that SBA-RA makes full use of the approximate location information of neighbors obtained from RCIS platform to accelerate neighbor discovery. What is more, the performance of SBA-RA in RCIS is evaluated, and then compared with Scan-Based Algorithm (SBA) and Completed Random Algorithm (CRA) in terms of time consumption. And the experiments indicate SBA-RA outweighs the others. In the end, future possibilities to improve SBA-RA are discussed.

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 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 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.