Recently, deep unfolding networks with interpretable parameters have been widely utilized in direction of arrival (DOA) estimation due to the faster convergence speed and better generalization ability. However, few consider the nested array for gridless DOA estimation. In this letter, we propose a deep alternating projection network to address the problem. We first convert the covariance matrix into a measurement vector in the form of atomic norm, which can reduce the matrix dimension during projection. We then train the proposed network to alternately obtain the positive semi-definite matrix and the corresponding irregular Hermitian Toeplitz matrix, where the loss function is derived by employing the trace of network output. Finally, we apply the irregular root Multiple Signal Classification (MUSIC) method to obtain gridless DOA via nested array. We demonstrate that the proposed networks can accelerate the convergence rate and reduce computational cost. Simulations verify the performance of proposed networks in comparison with the existing methods.

We present the convolution neural networks (CNNs) to achieve the localization of near‐field sources via the symmetric double‐nested array (SDNA). Considering that the incoherent near‐field sources can be separated in the frequency spectrum, we first calculate the phase difference matrices and consider the typical elements as the inputs of the networks. In order to guarantee the precision of the angle‐of‐arrival (AOA) estimation, we implement the autoencoders to divide the AOA subregions and construct the corresponding classification CNNs to obtain the AOAs of near‐field sources. Then, we construct a particular range vector without the estimated AOAs and utilize the regression CNN to obtain the range parameters of near‐field sources. The proposed algorithm is robust to the off‐grid parameters and suitable for the scenarios with the different number of near‐field sources. Moreover, the proposed method outperforms the existing method for near‐field source localization.

Localization is an important application in wireless sensor network for mobile computing. Range-based and range-free localization technologies are discussed in this paper. As one of most famous range-free location technologies, DV-Hop algorithm is introduced in detail and is simulated in AlgoSenSim. The simulation results show the influence of distribution of sensor nodes on network connectivity in DV-Hop localization. Moreover, mean error, standard deviation and median error generated by DV-Hop localization are compared respectively in the simulation experiment. It suggests that reduce the localization errors by optimizing the layout and the number of beacon nodes in network, and is simulated in AlgoSenSim.