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A generalized memristor consisting of a memristive diode bridge with a first order parallel RC filter is proposed in this letter. The mathematical model of the circuit is established and its fingerprints are analyzed by the pinched hysteresis loops with different periodic stimuli. The results verified by experimental measurements indicate that the proposed circuit is a simple voltage-controlled generalized memristor.

In this paper, we mainly study a cooperative search and coverage algorithm for a given bounded rectangle region, which contains several unknown stationary targets, by a team of unmanned aerial vehicles (UAVs) with non-ideal sensors and limited communication ranges. Our goal is to minimize the search time, while gathering more information about the environment and finding more targets. For this purpose, a novel cooperative search and coverage algorithm with controllable revisit mechanism is presented. Firstly, as the representation of the environment, the cognitive maps that included the target probability map (TPM), the uncertain map (UM), and the digital pheromone map (DPM) are constituted. We also design a distributed update and fusion scheme for the cognitive map. This update and fusion scheme can guarantee that each one of the cognitive maps converges to the same one, which reflects the targets’ true existence or absence in each cell of the search region. Secondly, we develop a controllable revisit mechanism based on the DPM. This mechanism can concentrate the UAVs to revisit sub-areas that have a large target probability or high uncertainty. Thirdly, in the frame of distributed receding horizon optimizing, a path planning algorithm for the multi-UAVs cooperative search and coverage is designed. In the path planning algorithm, the movement of the UAVs is restricted by the potential fields to meet the requirements of avoiding collision and maintaining connectivity constraints. Moreover, using the minimum spanning tree (MST) topology optimization strategy, we can obtain a tradeoff between the search coverage enhancement and the connectivity maintenance. The feasibility of the proposed algorithm is demonstrated by comparison simulations by way of analyzing the effects of the controllable revisit mechanism and the connectivity maintenance scheme. The Monte Carlo method is employed to validate the influence of the number of UAVs, the sensing radius, the detection and false alarm probabilities, and the communication range on the proposed algorithm.

This paper proposes a compressed sensing (CS) framework for the acquisition and reconstruction of frequency-sparse signals with chaotic dynamical systems. The sparse signal acts as an excitation term of a discrete-time chaotic system and the compressed measurement is obtained by downsampling the system output. The reconstruction is realized through the estimation of the excitation coefficients with the principle of impulsive chaos synchronization. The l 1 -norm regularized nonlinear least squares is used to find the estimation. The proposed framework is easily implementable and creates secure measurements. The Hénon map is used as an example to illustrate the principle and the performance.

This paper focuses attention on strange nonchaotic attractor of Chua's circuit with two-frequency quasiperiodic excitation. Existence of the attractor is confirmed by calculating several characterizing quantities such as Lyapunov exponents, Poincaré maps, double Poincaré maps and so on. Two basic mechanisms are described for the development of the strange nonchaotic attractor from two-frequency quasiperiodic state (torus solution). One of them is torus-doubling bifurcation followed by a smooth transition from the torus attractor to the strange nonchaotic attractor; and another is that the torus does not undergo period-doubling bifurcation at all; instead, the torus attractor gradually becomes wrinkled, and eventually becomes strange but nonchaotic.

In this paper, we discuss the existence of strange nonchaotic attractors for the Ueda’s circuit with two-frequency quasiperiodic excitation. We have calculated the Lyapunov exponents, Poincaré map, winding number and frequency spectrum of characterizing the attractors. The results show that the Ueda’s circuit does indeed exhibit strange nonchaotic attractors. Because of the simplicity, it is expected that the circuit would be useful for the theoretical study and experimental observation of the strange nonchaotic attractors.

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This paper proposes an adaptive fading Bayesian unscented Kalman filter (AF-BUKF) and explores its application for state estimation of unmanned aircraft systems (UASs). In the AF-BUKF, the state and noise densities are approximated as finite Gaussian mixtures, in which the mean and covariance for each component are recursively estimated using the UKF. To avoid the prohibitive computational complexity caused by the exponential growth of mixture components, a Gaussian mixture simplification algorithm is employed. Moreover, the AF-BUKF algorithm employs a novel adaptive fading strategy to recursively update the Gaussian components, so that the adverse effect of inexact knowledge of the state and measurement noise covariance can be mitigated. An AF-BUK Smoother (AF-BUKS) is also proposed by extending the AF-BUKF algorithm using the concept of optimal Bayesian smoothing and the Rauch-Tung-Striebel Smoother to improve estimation accuracy. Experimental results on simulated and real UAS data show that the proposed AF-BUKF/S algorithms can achieve better performance compared with the conventional methods. Thus, they can serve as attractive alternative approaches for nonlinear state estimation of UASs and other problems. INDEX TERMS Bayesian smoothing, nonlinear and non-Gaussian system, Gaussian mixture, unmanned aircraft systems, unscented Kalman filter