Reconnaissance mission has a wide application in both civil and military fields, which provides intelligence and basis for the following decision-making to accomplish certain goals. Due to numerous advantages of UAV swarms such as strong flexibility, high efficiency, and low cost, conducting reconnaissance missions by UAV swarms has become a trend of future. However, the path planning problem of UAV swarms is a key challenge in the aspect of model construction, algorithm, selection and high computational complexity, especially when the mission is complicated. In this paper, various distributed particle swarm optimization (DPSO)-based path planning algorithms are proposed for UAV swarms conducting a reconnaissance mission, in which targets are gathered in the form of clusters and different tactic needs are taken into consideration. Three algorithms named the maximum density convergence DPSO algorithm (MDC-DPSO), the fast cross-over DPSO algorithm (FCO-DPSO), and the accurate coverage exploration DPSO algorithm (ACE-DPSO) are proposed correspond to the needs of fast convergence, random cross-over, and accurate search, respectively. Different fitness functions and search strategies are specifically designed considering the mobility and communication constraints of the UAV swarms. Besides, the jump-out mechanism and revisit mechanism are designed to save invalid search efforts and avoid falling into local optimum. The simulation results demonstrate that the proposed algorithms are effective in generating paths for UAV swarms conducting a reconnaissance mission, which can be easily applied to large scale swarms.

In this paper, the angle-of-arrival (AOA) measurements are adopted to locate a target using UAV swarms equipped with passive receivers. The measurement noise is considered to be target-to-receiver distance dependent. The Cramer-Rao low bound (CRLB) of AOA localization is calculated and the optimal deployments are explored through changing angular separations and distances, respectively. Then, a distributed collaborative autonomous generation (DCAG) method is proposed based on the deep neural network (NN). The off-line training and on-line application rules are applied to generate the optimal heading angles for UAV swarms in AOA localization. Simulation results show that through DCAG method, the generated heading angles for UAV swarms enhance the localization accuracy and stability.

This paper develops the model of the joint phantom track deception and the joint techniques of time-difference of arrival (TDOA) and frequency-difference of arrival (FDOA) localization to deceive air defense radar networks under the condition that an unmanned aerial vehicle (UAV) swarm has no prior knowledge of the radars’ precise locations, and related performance experiment and analysis are presented to demonstrate the effectiveness of the proposed method and to clarify the influence factors of phantom track deception. The main contributions of this paper are as follows. Firstly, the model of phantom track deception against a radar network by UAV swarm without prior knowledge of the radars’ positions are established. Secondly, TDOA/FDOA are adapted to locate networked enemy radars using UAV swarm, where the Fisher information matrix (FIM) is derived to evaluate the estimation accuracy. Thirdly, the uncertainty analysis consisting of radar location error and UAV position error is deduced. With these efforts, the integrated capability of sensing and jamming is realized. Moreover, the same source testing using space resolution cell (SRC) from the perspective of a radar network is executed to provide guidance for phantom track design. Finally, performance experiment and analysis are given to verify the theoretical analysis with simulation results.

Regarding the deficiencies of clustering algorithm with fast method of searching and finding density peaks which is published in Science in 2014, an automatically and fast finding of clustering centers clustering algorithm is proposed, which applies linear regression and residual analysis and optimizes sample density value. The proposed algorithm improves location stability of clustering centers by measuring point density through sample's nearest neighbors information, and it determines clustering centers fast and automatically by applying linear regression and residual analysis, which reduces the subjectivity of artificial selection. Theoretical analysis and simulation results show that the proposed algorithm can overcome deficiencies of the original algorithm, what's more, the results of clustering effect and calculation time is superior to original algorithm, K-means and DBSCAN.

The adaptive tracking control problem of a class of uncertain SISO non-affine nonlinear systems is considered under the condition that system states are immeasurable and non-affine nonlinear function is possibly discontinuous. In order to remove the restriction that non-affine nonlinear function must be continuous, a novel model is developed for the non-affine nonlinear function. Combined with this model, an adaptive output feedback controller is proposed by utilising a high gain observer to estimate the immeasurable system states. It is proved that all the signals in the closed-loop system are bounded and the tracking error can be made arbitrarily small by choosing appropriately design parameters. Finally, simulation example is provided to demonstrate the effectiveness of the proposed approach.