In this paper, we combine the compressed sensing theory with the parallel factor (PARAFAC) model to present a two-dimensional direction of arrival (2D-DOA) and frequency estimation algorithm for L-shaped array. We first build the multi-delay outputs data as PARAFAC model, then compress it with partitioning and perform the PARAFAC decomposition through trilinear alternating least square (TALS) algorithm. Finally, we reconstruct the received data with sparsity to obtain the automatically paired 2D-DOA and frequency. The proposed algorithm is effective for both uniform and non-uniform L-shaped array, and owing to the compression process, it holds the propert- ies of lower computational complexity and smaller capacity for data storage, compared with traditional PARAFAC algorithm. The angle and frequency estimation performance of the proposed algorithm is close to the traditional PARAFAC method, and outperforms the estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm and propagator method (PM). Simulation results verify the effectiveness and superiority of our approach.

we address the two-dimensional direction-of-arrival (2-D DOA) estimation problem for L-shaped ULA by developing an algorithm represented by the convex optimal method. In this paper, the generalized conjugate symmetry property of L-shaped ULA is fully developed to increase not only virtual array aperture but virtual snapshots, which not only increases the maximum resolvable sources but yields better 2-D DOA estimation performance. Specifically, we first formulate the cost function as a quadratically constrained complex quadratic programming (QCCQP) problem via the subspace theory. The QCCQP problem can then be relaxed to a series of semidefinite programming (SDP) problems, which can be solved via the CVX solvers in polynomial time complexity per iteration. To avoid complex 2-D global iterations during the implementation of SDP problems, the PM-ESPRIT-like method is first applied to estimate azimuths, based on which, the proposed method can then be transformed to 1-D local iterations with no additional angles pairings needed. Furthermore, the superb performance of the proposed method still holds whether the spatial angles are very close or separate apart. Performances evaluations are confirmed based on multiple simulations examples and some criteria.

In this article, we propose two computationally efficient approaches to jointly estimate direction of arrival (DOA) and carrier for coherent signals using a given single snapshot with uniform linear array. First, the proposed methods construct the Khatri-Rao product-like data vector by introducing P -level delays from the array output. Second, a row elementary transformation is applied to the structured data vector, the rotational invariance structure of the carrier vector is exploited and thereby the carrier is derived. To be able to proceed, the special structure of the data vector is also exploited to estimate the coherent waveform. As a result, the DOA estimation problem can be recast into two Frobenius-norm-based optimization problems: one is referred to as linear search-based covariance-like matrix fitting method, which is suitable for estimating spaced angles that are well separated. The other one is called subspace separation-based covariance-like matrix fitting method, which is suitable for estimating closely spaced angles. The proposed methods develop one-dimensional (1-D) search and K 1-D search procedures, respectively, and hence they are computationally efficient compared to the classical methods. The simulation results illustrate that the performance of the proposed methods is better than the spatial smoothing-based multiple signal classification method under different scenarios.

A dual parallel factor (PARAFAC)-based approach to jointly estimating the two-dimensional direction of arrival (2-D DOA) and Doppler is proposed in this paper, where an L-shaped array consisting of acoustic vector-sensor is used. First, we apply the PARAFAC decomposition to the data model formed by concatenating the outputs of multi-level delays of the observations, and we get the parameter matrix H, which accomplish the 2-D DOA estimation and pairing automatically, then the dual PARAFAC decomposition is applied to the achieved composite steering matrix from the first PARAFAC decomposition, and thus, the same permutation matrices link the estimates of steering matrices and delay matrices from X-subarray and Y-subarray, respectively. Following this, the Doppler and 2-D DOA matching information are obtained via triple matching implementation, e.g. 2-D DOA and frequency matching. Finally, Doppler is estimated by delay matrices. The proposed algorithm is computationally effective for both uniform and non-uniform L-shaped array as SNR exceeds 15dB, and its performance outperforms the joint angle and Doppler shift ESPRIT (JAD-ESPRIT) algorithm and the joint angle and Doppler shift PM (JAD-PM) algorithm. The simulation results justified the effectiveness of the proposed algorithm.

With the advancement of MEMS techniques, mobile sensors with low cost and high capability are being deployed widely to carry out all kinds of complicated, risky tasks in large scale networks, especially in the areas are inaccessible to the human. Mobile sensors, which have the function of self-detection and self-repair, are desirable to solve the problem of failure of sensors, which causes costly maintenance expenditure and fatal loss. In this paper, we propose a novel heuristic self-repair algorithm to fix coverage holes in sensor networks using mobile nodes. The algorithm works successfully and efficiently to cover holes using reasonable numbers of mobile sensors. Numerical simulation results show our algorithm is near optimal for small coverage holes in terms of the metrics in number of mobile sensors, time and space complexity compared to optimal scheme. Our scheme is especially suitable for repairing multiple irregular areas of coverage holes simultaneously.

This paper addresses the issue of joint two-dimensional direction of arrival (2-D DOA) and frequency estimation via reduced-dimensional propagator method (RD-PM) with L-shaped array. The proposed algorithm has no need for eigenvalue decomposition of the sample covariance matrix and simplifies three-dimensional global spectral search within the three-dimensional propagator method (3-D PM) to one-dimensional local search, which greatly reduces computational complexity. Furthermore, the proposed algorithm can work under both uniform and non-uniform L-shaped array and can achieve paired 2-D DOA and frequency estimates automatically. In addition, the 2-D DOA and frequency estimation performance for the proposed method is approximate 3-D PM algorithm and parallel factor (PARAFAC) method but exceeds the estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm and improved PM algorithm. The detailed derivation of Cram´er-Rao bound (CRB) is provided and the simulation results demonstrate the effectiveness and superiority of the proposed approach.