In this paper, we investigate a resource allocation problem for a multi-unmanned aerial vehicle (UAV) assisted integrated sensing and communication system (ISAC), where a group of dual-functional UAVs perform simultaneous radar sensing of a target and data communication with multiple ground users. In particular, the trajectory of UAVs, user association, and beamforming design are jointly considered to maximize the sum weighted bit rate of all ground users while ensuring the sensing beampattern gain of the target. To cope with the above mixed-integer non-convex optimization problem, we propose an efficient strategy by decomposing the original problem into two subproblems under the alternating optimization framework. For the user association and beamforming design, we propose a novel algorithm to circumvent the coupling relationship among ground users and UAVs by leveraging matching theory and fractional programming theory. For the non-convex UAV trajectory subproblem, we apply the sequential quadratic programming to obtain a sub-optimal solution by solving a sequence of quadratic programming problems. The above two subproblems are iteratively solved and a stable solution is obtained upon convergence. Simulation results show that the proposed strategy outperforms various benchmark schemes that are based on the deferred acceptance algorithm, K-means algorithm, and a heuristic algorithm. It is demonstrated that the proposed strategy efficiently improve the sensing beampattern gain and communication rate.

Real noise environment may not be Gaussian due to existence of manmade interferences, natural bursts, etc. In this paper, a new multiple radar subbands fusion algorithm in complex noise environment is proposed. The considered complex noise includes impulsive noise and the mixture of impulsive and Gaussian noise. The proposed subband fusion algorithm consists of the following steps. Firstly, the incoherent factors are estimated and the multiple subbands are compensated to be mutually coherent. Then, coherent subbands are fitted by Geometrical Theory of Diffraction (GTD) model, where model parameters are estimated in the frame of sparse reconstruction. Different probability distribution functions are used to describe different noise environments, and the Maximum A Posteriori probability (MAP) estimate of scattering model parameters is achieved. It is shown that the MAP estimate can be obtained by iteratively solving a support vector regression problem. Finally, the fused fullband is obtained by extrapolating the measured subband data based on the estimated GTD model. Validity and performance of the proposed algorithm are investigated by analytical data, simulated data and static-range data.

In this paper, an integrated sensing and communication (ISAC) system is considered, with a dual function base station designated to serve communication users while sensing interested targets. To enhance throughput and spectrum efficiency, the sum weighted bit rate is maximized to guarantee the effective sensing power through beamforming design. To tackle the non-convexity of the original optimization problem, an alternating optimization algorithm which efficiently combinies fractional programming (FP) with successive convex approximation (SCA) is proposed. Specifically, when the non-convex constraint is approximated by SCA as the difference between an affine function and a constant, the remaining non-convex beamforming design problem is equivalently transformed and can be solved by FP. Simulations results validate the superiority of the proposed algorithm in improving the communication performance while ensuring sensing performance.

Phase measuring is cumbersome and expensive, especially when operation frequency of array is high. To avoid phase measuring, a novel failed sensor localization algorithm using amplitude-only near-field data is proposed. In perspective of Bayesian theory, the maximum a posteriori estimation of array excitation leads to a mixed-norm minimization problem subject to a quadratic constraint. Iterative procedure based on alternating directions method of multipliers is used to solve this problem. Computer simulations show that the proposed algorithm is able to give accurate estimation of array excitation and precisely locate the failed sensors of the array using amplitude-only near-field data.

In real application, most aerial targets are movable. In this paper, an effective multiple subbands coherent processing method is proposed for moving target. Firstly, an echoed signal model of motion target based on geometrical theory of diffraction is established and the influence of velocity on range profile of the target is analyzed. Secondly, a method based on minimum entropy principle is used to compensate velocity. Then, incoherent factors including a quadratic phase term, a linear phase factor, a fixed factor, and an amplitude difference term are analyzed. Subsequently, efficient methods are applied to estimate other incoherent factors, except that the quadratic term is small enough to be ignored. Finally, the feasibility and performance of the proposed method are investigated through numerical simulation.

This paper analyzes array pattern tolerance against excitation errors. The nonprobabilistic interval analysis algorithm is used for tolerance analysis of the nonideal uniform linear array in this work. Toward this purpose, corresponding interval models of the power pattern functions are established, respectively, with the consideration of the amplitude errors, phase errors, or both simultaneously, in antenna arrays. The tolerance for the amplitude-phase error of the main function parameters including the beamwidth, sidelobe level, and the directivity is simulated by computer according to the indicators and the actual requirements. Accordingly, the worst admissible performance of an array can be evaluated, which may provide theoretical reference for optimal antenna array design. As for the problem of array synthesis in the presence of various array errors, interval analysis-convex programming (IA-CP) is presented. Simulation results show that the proposed IA-CP based synthesis technique is robust for the amplitude and phase errors.