This study proposes an asymmetric mapping method to design sparse planar arrays with multiple constraints including the aperture, number of elements and minimum spacing between adjacent elements. Benefiting from two different mapping functions, which are established in this paper, this approach allows one to overcome the limitations of the existing matrix mapping method in terms of flexibility and performances. The numerical validation points out that the proposed method outperforms the matrix mapping method and modified real genetic algorithm in the design of array arrangements.

Integrated sensing and communication (ISAC) is recognized as a key technology in the multi-UAV-enabled 6G network. In this paper, we propose a simultaneous sensing and communication method for the multi-UAV network with multiple users and targets. Firstly, we investigate the ISAC channel model for the multiple users and targets, which can be determined by the physical parameters between the UAVs and the users or the targets. Then, we design the ISAC signal for the multi-UAV-enabled network and analyze the interference of the downlink signal for the multi-users and the echo signal for the multi-targets, respectively. On this basis, we further propose a multi-target sensing method with multi-UAV cooperation, where angle division multiple access (ADMA) is exploited to decrease the effect of the multi-user and multi-target interference. Finally, various simulation results are provided to verify the effectiveness of the proposed method.

To improve the performance of adaptive beamformer with large steering vector mismatch, a modified eigenspace based iterative robust capon beamformer (ME-IRCB) is proposed. The approach iteratively implements the robust Capon beamformer (RCB) with an adaptively adjusted uncertainty set. Based on the modified eigenspace method, the desired signal subspace is given, whereafter the size of the uncertainty set is adaptively adjusted according to the mismatch between the estimated and presumed steering vector of the desired signal at each iteration. The proposed approach gives two estimation methods about the uncertainty size, and both of them contribute to the robustness improving of the beamformer. Moreover, unlike other robust beamformers, the proposed approach does not take the integration of the steering vector’s outer product over the adjacent direction of the desired signal, and it works well with the adjacent directions of the desired signal and interference. This merit is due to that, the proposed approach only uses the fundamental sample covariance matrix during the subspace construction, therefore the influence of imprecise steering vector on the subspace is overcame. Simulation results show that the proposed approach performs better than the existing robust beamformers under different evaluation criteria.