In order to overcome the disadvantages of the conventional sliding mode reaching law, such as the large chattering and the slow convergence rate, an improved quick reaching law is proposed. The reaching law is composed of two terms which can, respectively, play the leading role when the system is far away from or near to the sliding mode surface. Thus, the system can arrive at the sliding mode surface with the faster convergence rate from beginning to end. Some other advantages of the reaching law, such as converging to the sliding mode surface in a finite time, and the second-order sliding mode characteristic are proved. Furthermore, in order to speed up the convergence rate to the equilibrium point along the sliding mode surface, the global terminal sliding mode control based on the quick reaching law is designed. It is used to control the unmanned surface vehicle. Simulation results show that the control method has better control performance than the conventional methods.

Gait energy image (GEI) preserves the dynamic and static information of a gait sequence. The common static information includes the appearance and shape of the human body and the dynamic information includes the variation of frequency and phase. However, there is no consideration of the time that normalizes each silhouette within the GEI. As regards this problem, this paper proposed the accumulated frame difference energy image (AFDEI), which can reflect the time characteristics. The fusion of the moment invariants extracted from GEI and AFDEI was selected as the gait feature. Then, gait recognition was accomplished using the nearest neighbor classifier based on the Euclidean distance. Finally, to verify the performance, the proposed algorithm was compared with the GEI + 2D-PCA and SFDEI + HMM on the CASIA-B gait database. The experimental results have shown that the proposed algorithm performs better than GEI + 2D-PCA and SFDEI + HMM and meets the real-time requirements.

In order to improve the control performance of the global sliding mode control method, a fast global sliding mode control method is proposed to accelerate the response of the system by changing the exponential decay function in the sliding surface as an exponential bilateral decay function which can make the dynamic sliding mode surface evolve into the linear sliding mode surface in the finite time. The exponential reaching law is used to design the control law, and Lyapunov stability theory is used to prove the stability of the system. The control method proposed in this article can be applied to control the uncertain nonlinear system. Simulation results show that the method has faster response rate than the conventional global sliding mode control method. The method can be used to control the quad-rotor unmanned helicopter, and its good practicability can be verified.

In modern radar detection systems, the particle filter technique has become one of the core algorithms for real-time target detection and tracking due to its good nonlinear and non-Gaussian system state estimation capability. However, when dealing with complex dynamic scenes, the traditional particle filter algorithm exposes obvious deficiencies. The main expression is that the sample degradation is serious, which leads to a decrease in estimation accuracy. In multi-target states, the algorithm is difficult to effectively distinguish and stably track each target, which increases the difficulty of state estimation. These problems limit the application potential of particle filter technology in multi-target complex environments, and there is an urgent need to develop a more advanced algorithmic framework to enhance its robustness and accuracy in complex scenes. Therefore, this paper proposes an improved particle filter algorithm for multi-target detection and tracking. Firstly, the particles are divided into tracking particles and searching particles. The tracking particles are used to maintain and update the trajectory information of the target, and the searching particles are used to identify and screen out multiple potential targets in the environment, to sufficiently improve the diversity of the particles. Secondly, the density-based spatial clustering of applications with noise is integrated into the resampling phase to improve the efficiency and accuracy of particle replication, so that the algorithm can effectively track multiple targets. Experimental result shows that the proposed algorithm can effectively improve the detection probability, and it has a lower root mean square error (RMSE) and a stronger adaptability to multi-target situation.