Due to low complexity, Received Signal Strength (RSS) source localization in Wireless Sensor Network (WSN) has gained upsurge of interests in recent years. In this letter, we consider the Path Loss Exponent (PLE) as a random variable in the lognormal shadowing model. Assuming a general distribution function for the PLE, the nonlinear equation for finding the Maximum Likelihood (ML) distance estimator has been derived. Then, an approximated closedform formula for the ML distance estimator is derived for uniform distribution of the PLE. Moreover, a Bayesian Minimum Mean Square Error (MMSE) estimator has been calculated for the PLE. Finally, localization is performed by a classical linear least square approach. Simulation results show the efficacy of the proposed algorithm in comparison to other methods.

This brief paper proposes a robust distributed estimation algorithm in presence of impulsive noise. Impulsive noises are present both in the measurements and in the communication links in a sensor network. The proposed method is essentially a diffusion LMS algorithm with optimized variable coefficients in the adaptation and combination steps. The optimized coefficients are obtained based on the minimum disturbance principle. Moreover, it is shown that the optimized coefficients of the adaptation step are found by solving a linear system of equations, while the optimized coefficients of the combination step are calculated by an eigenvector of a particular matrix. Moreover, the minimum disturbances calculated theoretically and their upper bounds are derived mathematically. Simulation results show the better performance of the proposed minimum disturbance diffusion LMS algorithm over some state-of-the-art algorithms.

This paper investigates the problem of sparse signal recovery in the presence of additive impulsive noise. The heavy-tailed impulsive noise is well modeled with stable distributions. Since there is no explicit formulation for the probability density function of SaS distribution, alternative approximations are used, such as, Generalized Gaussian Distribution (GGD) which imposes lp-norm fidelity on the residual error. In this paper, we exploit a Continuous Mixed Norm (CMN) for robust sparse recovery instead of \lp-norm. We show that in blind conditions, i.e., in case where the parameters of noise distribution are unknown, incorporating CMN can lead to near optimal recovery. We apply Alternating Direction Method of Multipliers (ADMM) for solving the problem induced by utilizing CMN for robust sparse recovery. In this approach, CMN is replaced with a surrogate function and Majorization-Minimization technique is incorporated to solve the problem. Simulation results confirm the efficiency of the proposed method compared to some recent algorithms for impulsive noise robust sparse recovery.

The problem of received signal strength (RSS) source localization in wireless sensor networks has raised attention in recent years due to its low complexity. However, the performance of the RSS localization algorithms is degraded due to malicious phenomena such as multipath and fading. Hence, machine learning and especially deep neural networks (DNNs) are applied to solve this problem. So far, convolutional the neural network (CNN), the recurrent neural network (RNN), and multilayer perceptron (MLP) are used to this end. In this letter, neural networks such as MLP, CNN, and RNN are suggested to be combined pairwise. In this regard, we propose to use a fusion of two general neural network localization estimators. To design the combination coefficients, a heuristic approach based on the errors of the training step of two neural networks are suggested. Moreover, two optimized approaches for selecting the combination coefficients are provided based on mean and variances and also based on the mean square error of the combined estimator. The simulation results show improvement of the localization accuracy by fusion of two neural networks.

This letter presents the sparse vector signal detection from one bit compressed sensing measurements, in contrast to the previous works that deal with scalar signal detection. Available results are extended to the vector case and the generalized likelihood ratio test (GLRT) detector and the optimal quantizer design are obtained. A double-detector scheme is introduced, in which a sensor level threshold detector is integrated into network level GLRT to improve the performance. The detection criteria of oracle and clairvoyant detectors are also derived. Simulation results show that with careful design of the threshold detector, the overall detection performance of double-detector scheme would be better than the sign-GLRT proposed in [J. Fang et al., “One-bit quantizer design for multisensor GLRT fusion,” IEEE Signal Process. Lett., vol. 20, no. 3, pp. 257-260, Mar. 2013] and close to oracle and clairvoyant detectors. The proposed detector is applied to spectrum sensing and the results are near the well-known energy detector, which uses the real valued data, while the proposed detector only uses the sign of the data.