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Coprime arrays have been widely adopted for direction-of-arrival (DOA) estimation since it can achieve an increased number of degrees of freedom (DOF). To utilize all information received by the coprime array, array interpolation methods are developed, which construct a virtual uniform linear array (ULA) with the same aperture from the non-uniform coprime array. However, the conventional non-robust DOA estimation algorithms for coprime arrays, including the interpolation based methods, suffer from degraded performance or even failed operation when some sensors are miscalibrated. In this paper, a novel maximum correntropy criterion (MCC) based virtual array interpolation algorithm for robust DOA estimation is developed to address this problem. The proposed approach treats the miscalibrated sensor observations as outliers, and by exploiting the property of MCC, the interpolated virtual array covariance matrix is reconstructed via nuclear norm minimization (NNM) with less influence of these outliers. In this manner, the robust DOA estimation is enabled by the robustly reconstructed covariance matrix. Simulation results demonstrate that the proposed algorithm can effectively the mitigate effect of the miscalibrated sensors while maintaining the enhanced DOF offered by coprime arrays.

This paper proposes a robust signal interpolation approach for direction-of-arrival (DOA) estimation when several sensors in a coprime array are not properly calibrated. In such case, the conventional interpolation approaches will lead to an inaccurate or even failed DOA estimation. In our proposed approach, observations obtained from the sensors without calibration are blindly treated as outliers. The interpolation problem is formulated as an atomic norm minimization (ANM) problem, where the M-estimator is employed as the regularization term to reduce the adverse impact of outliers. The DOA estimation results can be thus robustly obtained by applying the interpolated virtual signal matrix. Numerical experiments verify that our interpolation algorithm outperformance existing array interpolation methods in terms of robustness.

The development of wireless sensor and actuator network is leading to high complexity networks and subsequently, new challenges in task assignment for effective sensor–actuator coordination. This article proposes an adaptive auction protocol for task assignment in multi-hop wireless actuator networks, considering a scenario where all actuators are immobile and each of them can obtain the target information from sensors. Unlike existing methods that neglect the adaptive auction area required by dynamic networks, the proposed method uses an adaptive factor [Formula: see text] (reflects the adaptive auction area) that is deduced based on the relation between network characteristics and protocol performance. Simulation results show that the adaptive protocol can dynamically change the auction area during the network operation. In addition, comparison with existing methods, such as simple auction protocol, 1 hop simple auction aggregation protocol, and 1 hop simple auction aggregation protocol greedy extension, shows that the adaptive auction protocol can choose the optimal solution at a low communication cost with a high probability, irrespective of the changes in the network.

Coprime array with M + N sensors can achieve an increased degrees-of-freedom (DOF) of O ( M N ) for direction-of-arrival (DOA) estimation. Utilizing the compressive sensing (CS)-based DOA estimation methods, the increased DOF offered by the coprime array can be fully exploited. However, when some sensors in the array are miscalibrated, these DOA estimation methods suffer from degraded performance or even failed operation. Besides, the key to the success of CS-based DOA estimation is that every target falls on the predefined grid. Thus, a coarse grid may cause the mismatch problem, whereas a fine grid requires great computational cost. In this paper, a robust CS-based DOA estimation algorithm is proposed for coprime array with miscalibrated sensors. In the proposed algorithm, signals received by the miscalibrated sensors are viewed as outliers, and correntropy is introduced as the similarity measurement to distinguish these outliers. Incorporated with maximum correntropy criterion (MCC), an iterative sparse reconstruction-based algorithm is then developed to give the DOA estimation while mitigating the influence of the outliers. A multiresolution grid refinement strategy is also incorporated to reconcile the contradiction between computational cost and the mismatch problem. The numerical simulation results verify the effectiveness and robustness of the proposed method.

Multiple paths planning of unmanned aerial vehicles is a typical multimodal optimization problem. This paper presents a particle swarm optimization algorithm combined with sequential niche technique to solve this problem. The sequential niche technique adjusts the objective function value of the surrounding area of minimum to ensure the diversity of multiple optimizations. The surrounding area of the minimum found by particle swarm optimization in the search domain is described as niche. The objective function value in the niche is adaptively modified to reject the particles from the niche in subsequent iterations. Besides, a novel method to determine the niche radius is proposed for multiple paths planning. Furthermore, the distance calculation method between two paths is improved to better distinguish different paths. Finally, the simulation results show that the proposed method can effectively solve multiple paths planning problem.

The article presents a novel targets assignment method for multiple UCAVs. In this work, minimization total attack time is chosen as the objective of the targets assignment problem, and the attack benefit of each target is affected by the target value. To solve this challenging problem, the tailored genetic algorithm (GA) incorporated with the opposition-based learning technique is proposed, denoted as OGA. By introducing the opposition-based learning technique into the evolutionary process, the global search capability is enhanced and the convergence and optimality of the algorithm could be improved. Finally, OGA is compared with ordinary GA on several multi-UCAVs targets assignment simulations. The comparison results show that the proposed method is more efficient and stronger in escaping from the local optimum in solving the multi-UCAVs targets assignment.

Sparse A* search (SAS) approach has been widely used in robot motion planning and flight vehicle path planning. However, considering the kinematics constraint of UAV, the estimated cost of SAS based on the straight line distance is not accurate which probably gives rise to abundant useless search. Dubins path is the shortest one to connect two poses under the kinematics constraint, thus the estimated cost using Dubins path is guaranteed to satisfy the admissible condition and it is beneficial to resolving accurate cost for the path planning objective. Therefore, the search efficiency of SAS is expected to be improved by introducing the Dubins path. Meanwhile, SAS employs the error of straight line distance from the current node to the terminal as the stopping criterion, which cannot guarantee the direct flyability of terminal path and leads to a farther real flight path. In this paper, the Dubins length is also used in the length evaluation for termination condition to ensure the final planned path could be flied and to increase the the accuracy of the result. Simulations are conducted to test the proposed algorithm, and the results show that the number of expanded nodes for exploring the optimal path is dramatically decreased and the planning time is reduced.