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In order to solve the problem of severely decreased performance under the situation of rapid moving sources and unstable array platforms, a null broadening robust adaptive beamforming algorithm based on power estimation is proposed in this paper. First of all, we estimate the interference signal power according to the characteristic subspace theory. Then, the correspondence between the signal power and steering vector (SV) is obtained based on the orthogonal property, and the interference covariance matrix (ICM) is reconstructed. Finally, with the aim of setting virtual interference sources, null broadening can be carried out. The proposed algorithm results in a deeper null, lower side lobes and higher tolerance of the desired signal steering vector mismatch under the condition of low complexity. The simulation results show that the algorithm also has stronger robustness. [ABSTRACT FROM AUTHOR]

Aiming at the problems that the chaotic sequence is periodic due to the influence of calculation accuracy and the pseudorandomness of the sequence is not ideal, this paper proposes a dynamic multimapping compound (DMMC) chaotic sequence based on time of days (TOD) and several improved logistic mappings, which not only increases the key space, flexibility, and complexity of sequence generation but also improves pseudorandom performance. The sequence is composed of two new logistic maps with different initial values, which are then scrambled by the 2D-LICM sequence. In the whole process, the TOD is used to determine the system parameter and the selection of the scrambling sequence. After certain processing, it is connected in series with the original generated sequence to obtain the target chaotic sequence. The simulation analysis proves that the sequence generated by the algorithm proposed in this paper has good run length, correlation, and ideal balance and greatly improves the confidentiality of the system, which proves the effectiveness and reliability of the designed spread spectrum sequence. [ABSTRACT FROM AUTHOR]

When the array structure of the sparse arrays (SA) cannot be determined, the existing beamforming algorithms designed according to specific formations such as coprime arrays (CA), nested arrays (NA), etc. will fail. To solve this problem, we propose two algorithms that are suitable for a variety of SA. In the first method, assuming that the desired signal is a non-Gaussian signal, the desired signal direction vector (DSDV) is estimated using the fourth-order cumulant, and then the interference plus noise covariance matrix (INCM) is reconstructed by integrating the area outside the desired signal. When the desired signal is a Gaussian signal, we propose the second method. The second method estimates the power and direction of arrival (DOA) of the signals by performing eigenvalue decomposition on the sampled covariance matrix (SCM) and finally calculates the weight vector. However, this method needs to estimate the DOA of the signals, so it has certain requirements for the SA structure design. The simulation results show that the proposed method has good performance and strong robustness under different SA. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]