Chaos has been widely used in image encryption due to its rich properties. However, it remains an irreconcilable contradiction for security and implementation efficiency for image encryption schemes. In this paper, a novel chaos-based image encryption scheme has been proposed, where the Lorenz chaotic system is applied to generate pseudorandom sequences with good randomness, and a random switch control mechanism is introduced to ensure the security of the encryption scheme. Experimental results demonstrate the effectiveness and superiority of the algorithm.

A new neural network (NN) aided adaptive unscented Kalman filter (UKF) is presented for tracking high maneuvering target. In practice, the dynamic systems of many target tracking problems are usually nonlinear and incompletely observed, moreover, there may be large modeling errors when the target is maneuverable or some parameters of the system models are inaccurate or incorrect. The adaptive capability of filters is known to be increased by incorporating a neural network into the filtering procedure. On the other hand, some nonlinear filtering methods such as extended Kalman Filter (EKF) have been used to train a NN with fast convergence speed by augmenting the state with unknown connecting weights. Tackling the natural coalescent between the filtering algorithm and the NN described above, first a more efficient learning algorithm based on unscented Kalman filter (UKF) is derived, which can give a more accurate estimate of the weights and possess faster convergence rate. We then extend the algorithm to form a new NN aided adaptive UKF algorithm and use it in maneuvering target tracking applications. The NN in this algorithm is used to approximate the uncertainty of system models and is trained online, together with the target state estimation. Some simulations are also given to validate that the proposed method can give well state estimation of a highly maneuvering target.

In this paper, a multi-model Rao-Blackwellised particle filter algorithm is presented for tracking high maneuvering target in distributed acoustic sensor networks. It is more efficient for high-dimension nonlinear and non-Gaussian estimation problems than generic particle filter, and by stratified particles sampling from a set of system models, it can tackle the target's maneuver perfectly. In the simulation comparison, a high maneuvering target moves through an acoustic sensor network field. The target is tracked using both the RBPF and the multi-model RBPF algorithms, and a location-central protocol is applied for energy conservation. The results show that our approach has great performance improvements, especially when the target is making maneuver.