The virtual array generation process based on typical sparse arrays is studied for a mixture of circular and non-circular impinging signals. It consists of two sub-arrays: one is the traditional difference co-array and the other one is the new sum co-array. The number of consecutive virtual array sensors is analysed for the nested array case, but it is difficult to give a closed-form result for a general sparse array. Based on the extended covariance matrix of the physical array, two classes of direction of arrival (DOA) estimation algorithms are then developed, with one based on the subspace method and one based on sparse representation or the compressive sensing (CS) concept. Both the consecutive and non-consecutive parts of the virtual array can be exploited by the CS-based method, while only the consecutive part can be exploited by the subspace-based one. As a result, the CS-based solution can have a better performance than the subspace-based one, though at the cost of significantly increased computational complexity. The two classes of algorithms can also deal with the special case when all the signals are noncircular. Simulation results are provided to verify the performance of the proposed algorithms.
An expanding and shift (EAS) scheme for efficient fourth-order difference co-array construction is proposed. It consists of two sparse sub-arrays, where one of them is modified and shifted according to the analysis provided. The number of consecutive lags of the proposed structure at the fourth order is consistently larger than two previously proposed methods. Two effective construction examples are provided with the second sparse sub-array chosen to be a two-level nested array, as such a choice can increase the number of consecutive lags further. Simulations are performed to show the improved performance by the proposed method in comparison with existing structures.
The High Altitude Platforms (HAPs) have been considered as a promising technique for providing wireless communication and other services. They operate at an altitude from 17km to 22km, which exploit both advantages of satellites and terrestrial wireless communication networks. It is possible to deploy HAPs network rapidly and adjust them dynamically to cover specific regions requiring communication services. In this paper, the problem of deploying HAPs network to provide wireless communication for terrestrial users is investigated. The aim of deployment is to providing communication services to ground users as many as possible with quality of service (QoS) guaranteed. To find an optimal solution, we model the problem as a potential game. And a restricted spatial adaptive play (RSAP) learning algorithm is introduced for the game. Using this algorithm, the HAPs can be deployed to cover the user areas in a self-organized manner with high QoS achieved. Finally, the simulation results also demonstrate that the HAPs network can achieve the optimal deployment.
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