Security has always been an important aspect of wireless communications. Aiming at further improve the security of wireless communication and how to prevent eavesdropping, this paper proposes a constellation encryption design based on chaotic sequence and the RSA algorithm. The core idea of this method is to effectively combine both chaotic sequences and RSA, and then to generate large number of encrypted sequences with high security. The specific method is to first use the asymmetric RSA algorithm to transmit the system parameters, then to use the initial value sensitivity of the chaotic sequence to generate the secret sequence, and finally to use the secret sequence to encrypt the original sequence. Simulations show that the proposed algorithm further improves the security of satellite communications without increasing the complexity of the original system.

Nowadays, sparse arrays have been a focus for direction‐of‐arrival (DOA). The existing arrays can achieve high degree of freedom (DOF) bigger than the number of sensors when using the spatial smoothing methods. However, the small inter‐element spacing degrades the DOA estimation accuracy when facing severe mutual coupling. In order to alleviate mutual coupling, a coprime linear array using three uniform linear arrays (CLA‐U3) is proposed. The expression of sensor locations is given, and the analysis of DOF is considered. The minimum subarray inter‐element spacing in CLA‐U3 is bigger than that of the existing arrays, which means that CLA‐U3 can be less sensitive to mutual coupling. Moreover, the difference co‐subarrays can fill each other's holes, so CLA‐U3 and other sparse arrays composed of three subarrays have close DOF. Simulation experiments prove the favourable performance of DOA estimation.

Direction-of-arrival (DOA) estimation using nested linear array ignores the information from repeated sensors, thus involves the problem of losing accuracy. Moreover, non-circular feature of signals is rarely considered and it results in discontinuity of the virtual array, namely, holes appear. This paper first provide an improved data averaging method to increase the accuracy of DOA estimation by fusing data from covariance and elliptic covariance. And then an algorithm based on matrix completion theory in nested array that greatly extends the degrees of freedom (DOF) and is able to find more sources than number of physical sensors is presented. The algorithm reconstructs the covariance matrix of the virtual linear array, which exactly has the same shift invariance as the uniform linear array but a higher aperture. Considering about the missing elements of the virtual array covariance matrix, we apply matrix completion theory to solve the problem. Finally, true DOAs of multiple signals can be obtained through subspace algorithms. Numerical results demonstrate that the proposed algorithms can obtain high accuracy while underdetermined DOA estimation is realized.

Nowadays, sparse arrays have been a hotspot for research in the direction of arrival (DOA). In order to achieve a big value for degrees of freedom (DOFs) using spatial smoothing methods, researchers try to use multiple uniform linear arrays (ULAs) to construct sparse arrays. But, with the number of subarrays increasing, the complexity also increases. Hence, in this paper, a design method, named as the cross-coarray consecutive-connected (4C) criterion, and the sparse array using Q ULAs (SA-UQ) are proposed. We first analyze the virtual sensor distribution of SA-U2 and extend the conclusions to SA-UQ, which is the 4C criterion. Then, we give an algorithm to solve the displacement between subarrays under the given Q ULAs. At last, we consider a special case, SA-U3. Through the analysis of DOFs, SA-UQ can find underdetermined signals. Moreover, SA-U3 can obtain DOFs close to other sparse arrays using three ULAs. The simulation experiments prove the performance of SA-UQ.

In the area of direction-of-arrival (DOA) estimate research, Unknown mutual coupling (UMC) causes significant interference with the array’s localization performance. Although the sparsity of the structure of the sparse array can weaken the mutual coupling (MC) to some extent, the residual MC still has a substantial effect on the localization performance. In this paper, a sparsity-based localization method for mixed near-field (NF) and far-field (FF) sources with UMC is developed. On the basis of the rank reduction (RARE) principle, multiple parameters of the source are decoupled. Therefore the one-dimensional (1-D) search is necessary to estimate the aforementioned factors. The sparse array localization performance is greatly improved by the offsetting of MC parameters using proposed method. Simulation experiments show that this method significantly improves the performance of mixed NF and FF sources localization under the UMC environment.