The conventional sense-and-avoid collision avoidance mode of UAV (unmaned aerial vehicle) lacks applicability and timeliness in a multi-threat environment. In this paper, a new efficient collision avoidance approach for uncertain threat environments derived from the idea of autonomous mental development is proposed. The proposed collision avoidance pattern consists of a sensory layer, a logic layer and a development layer. The threat information is sensed using the sensory layer, and the path planning approach in the logical layer is applied to the output configuration of UAV. In the development phase, the developmental networks approach is used for online learning, training and updating the logical layer so as to form the sense–action mapping, which is stored as the “basic experience” for UAV executing the avoidance manoeuvre. In the implementation phase, the command is executed by matching the sensing information and action base. The simulation results show that the proposed approach has better timeliness compared to the conventional approaches.

In multi-target tracking, the outliers-corrupted process and measurement noises can reduce the performance of the probability hypothesis density (PHD) filter severely. To solve the problem, this paper proposed a novel PHD filter, called Student’s t mixture PHD (STM-PHD) filter. The proposed filter models the heavy-tailed process noise and measurement noise as a Student’s t distribution as well as approximates the multi-target intensity as a mixture of Student’s t components to be propagated in time. Then, a closed PHD recursion is obtained based on Student’s t approximation. Our approach can make full use of the heavy-tailed characteristic of a Student’s t distribution to handle the situations with heavy-tailed process and the measurement noises. The simulation results verify that the proposed filter can overcome the negative effect generated by outliers and maintain a good tracking accuracy in the simultaneous presence of process and measurement outliers.

The optimal maneuver of observer for bearing-only tracking (BOT) in a threat environment is a complex problem which involves nonlinear filtering, threat avoidance, and optimal maneuver strategy. Under comprehensive consideration, the reward function comprised of the lower bound on detFIM and threat cost was established; the finite-horizon MDP principle was applied to obtain the optimal strategy. The quantization method was used to discretize the BOT process and calculate the transition matrix of Markov chain; to achieve quantization in the beginning of each period, CKF was applied to provide the initial state estimate and the corresponding error covariance. The numerical simulations illustrated the applicability and superior performance for static and dynamic target tracking in several scenarios in the threat environment.

Bearings-only tracking only adopts measurements from angle sensors to realize target tracking, thus, the accuracy of the state prediction has a significant influence on the final results of filtering. There exist unpredictable approximation errors in the process of filtering due to state propagation, discretization, linearization or other adverse effects. The idea of online covariance adaption is proposed in this work, where the post covariance information is proved to be effective for the covariance adaption. With theoretical deduction, the relationship between the posterior covariance and the priori covariance is investigated; the priori covariance is modified online based on the feedback rule of covariance updating. The general framework integrates the continuous-discrete cubature Kalman filtering and the feedback rule of covariance updating. Numerical results illustrated that the proposed method has advantages over decreasing unpredictable errors and improving the computational accuracy and efficiency.

Bearings-only tracking is a continuous-discrete system actually, whose state of motion is in the continuous-time domain and the measurement is in the discrete-time domain. The unpredicted approximation errors are inevitable due to integration, discretization and linearization of continuous model in many filtering methods. The adaptive covariance feedback framework is proposed for solving this kind of problem, in which the posterior covariance sequence is proved theoretically to be useful for prior covariance updating. In this framework, the covariance feedback framework is integrated in the continuous-discrete cubature Kalman filtering, Chebyshev distance is applied to judge the proper condition for start-up of the feedback channel. The numerical results illustrate the proposed method’s superior performance in accuracy and computational efficiency.