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In the coprime planar array (CPA), the existing tensor DOA estimation has the problem that the statistics are not fully utilized. We propose a two-dimensional DOA estimation method based on tensor self-correlation, which realizes the high-resolution and high-precision joint estimation of elevation angle and azimuth angle. Firstly, we represent the received signals of two subarrays with tensors and then obtain the self-correlation covariance tensor of the subarrays themselves and the cross-correlation covariance tensor of the two subarrays. Then, we extract the covariance tensor corresponding to the maximum continuous virtual array and prove the expression of the maximum continuous virtual array aperture of the proposed method. Compared with the existing methods, the proposed method effectively improves the maximum aperture of the continuous virtual array. Finally, the signal subspace is solved by tensor expansion and tensor decomposition. Simulation results show that under the same conditions, the proposed method has higher estimation accuracy and degree of freedom than the cross-correlation tensor method, and the resolution is also improved significantly.

As we all know, the model mismatch, primarily when the desired signal exists in the training data, or when the sample data is used for training, will seriously affect algorithm performance. This paper combines the subspace algorithm based on direction of arrival (DOA) estimation with the adaptive beamforming. It proposes a reconstruction algorithm based on the interference plus noise covariance matrix (INCM). Firstly, the eigenvector of the desired signal is obtained according to the eigenvalue decomposition of the subspace algorithm, and the eigenvector is used as the estimated value of the desired signal steering vector (SV). Then the INCM is reconstructed according to the estimated parameters to remove the adverse effect of the desired signal component on the beamformer. Finally, the estimated desired signal SV and the reconstructed INCM are used to calculate the weight. Compared with the previous work, the proposed algorithm not only improves the performance of the adaptive beamformer but also dramatically reduces the complexity. Simulation experiment results show the effectiveness and robustness of the proposed beamforming algorithm.

In non-cooperative communications, blind identification of the widely used LDPC codes is an emerging research hot spot. This is different from and more difficult than blind identification of channel coding in cooperative communications. Common disadvantages of the existing algorithms are poor fault tolerance, high number of iterations, and only being applicable to short codes. Our proposed algorithm is based on the idea presented by Sicot et al. (2009) for binary phase-shift keying (BPSK) signals over the additive white Gaussian noise (AWGN) channel. Firstly, to sort the codewords according to a proper threshold, we present a threshold function and derive its zero point, which is verified to be the optimal threshold through experiments. This threshold improves the fault tolerance of our algorithm. Secondly, an operation called bidirectional Gaussian column elimination (BGCE) is proposed to replace Gaussian column elimination (GCE). This operation speeds up the process of deriving parity-checks, and considerably reduces the number of iterations. Thirdly, we use an existing technique for finding low-weight codewords as proposed by Canteaut and Chabaud (1998) to make the linearly independent parity-checks sparse, thereby realizing reconstruction of the sparse parity-check matrix of the LDPC code. The results of comparative experiments demonstrate that our algorithm outperforms existing algorithms.

Source number estimation is one of the key technologies of wireless location, and it is also the basis of parameter estimation and location solution. In order to improve the accuracy of existing non-circular source number estimation and realize underdetermined source number estimation, this paper presents a non-circular source number estimation method based on sparse array. This method firstly uses the non-circular characteristics of received sources to expand the array aperture, then combines the structural characteristics of sparse array to construct a virtual array model, and extracts the response parts of continuous virtual array elements for spatial smoothing processing. Finally, the minimum description length (MDL) method is used to realize the effective estimation of non-circular source number. The complexity analysis and simulation experiments are given, and the results of Monte Carlo experiments show that compared with the traditional source number estimation method, this method can not only improve the accuracy of the source number estimation but also realize the effective estimation of the source number in undetermined conditions.

In order to improve the low estimation accuracy and low resolution of the existing Direction of Arrival (DOA) estimation algorithms for cyclostationary signals, a high precision DOA estimation algorithm for cyclostationary signals using a coprime array combined with common spectral peak search is proposed in this paper. The main idea of the algorithm is to use the sparse characteristics of the coprime array subarrays and solve the DOA estimates from the subarrays respectively. Among them, the blurred values of the subarrays have coprime characteristic, and the common value of the two subarrays DOA solution sets is the true angle of target signal. Firstly, the algorithm constructed the array receiving model of each subarray, and obtained the subarray cyclic autocorrelation matrix. Then, according to the characteristics of each subarray receiving model, we use the subspace method to solve the DOA value of the subarray. Finally, the real DOA value of the target signal can be obtained by comparing the solutions of the two subarrays with the idea of common spectral peaks. The proposed algorithm is based on a coprime array with sparse characteristic, and the common spectral peak is used to eliminate the problem of blurred values caused by sparsity, thus improving the estimation accuracy and resolution of the algorithm. The simulation results show that the proposed algorithm can realize effective estimation of cyclostationary signals. Compared with most cyclostationary signals DOA estimation algorithms, the estimation accuracy of the proposed algorithm is further improved.

Recently, the design of sparse linear array for direction of arrival (DOA) estimation of non-Gaussian signals has attracted considerable interest due to the fact that the fourth-order difference coarray offered by non-Gaussian significantly increases the aperture of a virtual linear array, which improves the performance of DOA estimation. In this paper, a super four-level nested array (S-FL-NA) configuration based on fourth-order cumulants (FOC) is proposed. The S-FL-NA consists of uniform linear arrays which have different interelement spacing. The proposed array configuration is designed based on interelement spacing, which, for a given number of sensors, is uniquely determined by a closed-form expression. We also derive the closed-form expression for the degrees of freedom (DOFs) of the proposed array. The optimal distribution of the number of sensors in each uniform linear array of the proposed array is given for an arbitrary number of sensors. Compared with the existing sparse arrays, the proposed array can provide a higher number of degrees of freedom and a larger physical array aperture. In addition, to improve the calculation speed of the fourth-order cumulant matrix, we simplify the FOC matrix by removing some redundancy. Numerical simulations are conducted to verify the superiority of the S-FL-NA over other sparse arrays.

When there is coexistence of uncorrelated and coherent signals in sparse arrays, the conventional algorithms for direction-of-arrival (DOA) estimation using difference coarray fail. In order to solve the problems, this paper analyzes the feasibility of using spatial smoothing in sparse arrays. Firstly, we summarize the two types of sparse arrays, one consisting of identical sparse subarrays and the other consisting of several uniform linear subarrays. Then, we give the feasibility analysis and the processes of applying spatial smoothing. At last, we discuss the performance of the number of detectable coherent signals in different sparse arrays. Numerical experiments prove the conclusions proposed by the paper.

Most of the existing DOA estimation algorithms are based on uniform array, whose estimation performance is limited by the shortcoming of small aperture of uniform array, and it is difficult to apply sparse array to DOA estimation of distributed sources. In order to solve the above problems, this paper introduces interqualitative array into DOA estimation of distributed sources, and proposes a DOA estimation algorithm for coherent distributed sources. Because the array element spacing is larger than half wavelength, the direct estimation will produce false peak. In order to eliminate the ambiguity, this paper proves that DOA estimation of two submatrices is performed separately, and its common value is unique, that is, the true incidence Angle. According to this proof, DOA estimation of two submatrices is carried out separately for the one-dimensional DOA estimation scenario of distributed sources, and the common part of the two sets of estimation results is taken as the final estimation result. The complexity analysis and performance verification of this method are also given. Simulation experiments show that the performance of the proposed method is better than that of the DSPE algorithm and TLS-ESPRIT algorithm under the same array number in the one-dimensional DOA estimation scenario.

Underwater wireless sensor networks nodes deployment optimization problem is studied and underwater wireless sensor nodes deployment determines its capability and lifetime. If no underwater wireless sensor node is available in the monitoring area of underwater wireless sensor networks due to used up energy or any other reasons, the monitoring area where is not detected by any underwater wireless sensor node forms coverage holes. In order to improve the coverage of the underwater wireless sensor networks and prolong the lifetime of the underwater wireless sensor networks, based on the perception model, establish nodes detection model, combining with the data fusion. Because the underwater wireless sensor networks nodes coverage holes appear when the initial randomly deployment, a nodes deployment algorithm based on perception model of underwater wireless sensor networks is designed in this article. The simulation results show that this algorithm can effectively reduce the number of deployment underwater wireless sensor networks nodes, improve the efficiency of underwater wireless sensor networks coverage, reduce the underwater wireless sensor networks nodes energy consumption, prolong the lifetime of the underwater wireless sensor networks.

Underwater wireless sensor network nodes deployment optimization problem is studied and underwater wireless sensor nodes deployment determines its capability and lifetime. Underwater wireless sensor network if no wireless sensor node is available in the area due to used up energy or any other reasons, the area which is not detected by any wireless sensor node forms coverage holes. The coverage holes recovery algorithm aiming at the coverage holes in wireless sensor network is designed in this article. The nodes movement is divided into several processes, in each movement process according to the balance distance and location relations move nodes to separate the aggregate nodes and achieve the maximum coverage of the monitoring area. Because of gradually increasing the balance distance between nodes, in each movement process the nodes movement distance is small and reduce the sum of the nodes movement distance. The simulation results show that this recovery algorithm achieves the goal of the nodes reasonable distribution with improving the network coverage and reducing the nodes movement distance thus extends the lifetime of the underwater wireless sensor network in the initial deployment phase and coverage holes recovery phase.

Underwater wireless sensor networks nodes deployment optimization problem is studied and underwater wireless sensor nodes deployment determines its capability and lifetime. If no underwater wireless sensor node is available in the monitoring area of underwater wireless sensor networks due to used up energy or any other reasons, the monitoring area where is not detected by any underwater wireless sensor node forms coverage holes. In order to improve the coverage of the underwater wireless sensor networks and prolong the lifetime of the underwater wireless sensor networks, based on the perception model, establish nodes detection model, combining with the data fusion. Because the underwater wireless sensor networks nodes coverage holes appear when the initial randomly deployment, a nodes deployment algorithm based on perception model of underwater wireless sensor networks is designed in this article. The simulation results show that this algorithm can effectively reduce the number of deployment underwater wireless sensor networks nodes, improve the efficiency of underwater wireless sensor networks coverage, reduce the underwater wireless sensor networks nodes energy consumption, prolong the lifetime of the underwater wireless sensor networks.