Existing direction of arrival (DOA) estimation approaches are often only considering Gaussian noise or impulsive noise, leading to the performance degradation in the scenario that both noises exist simultaneously. Considering that ambient noise of underwater acoustic array may have different variances due to the large aperture, this paper proposes a robust sparse recovery method based on variational Bayesian inference (VBI) that considers the “heavy-tailed” characteristics of impulsive noise, and the non-uniformity of ambient noise. Student-t distribution and Bernoulli distribution are modeled as impulsive noise in the measurement, and then, the array observed signal is created as a mixture of desired signal, impulsive noise and non-uniform noise. A VBI scheme is constructed to estimate the desired sparse signal to implement DOAs. Results obtained from the numerical simulation and experimental data processing verify the superior performance of the proposed VBI promoting DOA estimation for dealing with impulsive noise and non-uniform noise.

Due to mobility of unmanned aerial vehicles (UAVs), many UAVs are used to expand the communication range and relay data communication in military and civil scenarios. UAV network will have a new impact on network performance due to its unique three-dimensional space operation and its own high-speed mobility. In this paper, we propose a three-dimensional grid based virtual routing (3D-GVR) algorithm to improve network performance. We determine the size of the three-dimensional grid based on the effective communication range of the UAV and the heartbeat time of the UAV, and then transmit it as a whole through virtual routing. Finally, the proposed 3D-GVR algorithm is evaluated by simulation. Simulation results show that this method is superior to DSDV and GPSR in packet overhead, packet transmission rate and average time.

This Letter solves steering vector estimation under mismatch for adaptive beamforming. The proposed beamformer implements a stepwise estimation of steering vector, and zone orthogonal constraint is added first based on adaptive constraint framework from Khabbazibasmenj [IEEE Trans. Signal Process. 60(6), 2974–2987 (2012)], which ensures that the estimated steering vector does not converge to an interference steering vector outside the predefined sector, even if the sector deviates from the central observation area of arrays. Then uncertainty set error constraint is used to improve the estimation accuracy. The numerical simulation and experimental results verify the effectiveness of the proposed method.

The current self-calibration approaches based on sparse Bayesian learning (SBL) demonstrate robust performance under uniform white noise conditions. However, their efficacy degrades significantly in non-uniform noise environments due to acute sensitivity to noise power estimation inaccuracies. To address this limitation, this paper proposes an orientation estimation method based on variational Bayesian inference to combat non-uniform noise and gain/phase error. The gain and phase errors of the array are modeled separately for calibration purposes, with the objective of improving the accuracy of the fit during the iterative process. Subsequently, the noise of each element of the array is characterized via independent Gaussian distributions, and the correlation between the array gain deviation and the noise power is incorporated to enhance the robustness of this method when operating in non-uniform noise environments. Furthermore, the Cramér–Rao Lower Bound (CRLB) under non-uniform noise and gain-phase deviation is presented. Numerical simulations and experimental results are provided to validate the superiority of this proposed method.