It has been found that the accuracy of particle filtering is much lower when the maneuvering target tracking process has been executed for a long time. The reason for this problem is that the diversity of the sampled particles is rapidly lost because of the excessive resampling. Therefore, the track of the maneuvering target estimated by the particle filtering will be widely wiggly from the true one. Through the research of the distribution of the sampled particles, a new algorithm was proposed. And a detected threshold was set to detect if the particle was dried up badly. When the particle was dried up badly, the particles of the state-space would be reset to relax the degree, so the new particles could contain more distribution information. The new algorithm has a high capability in the simulation of the 2-D maneuvering target tracking.

The paper briefly introduced the merits and shortcomings of the existing anti-collision algorithms. Based on the idea of Jumping and Dynamic Searching (JDS) algorithm, a Novel JDS (NJDS) algorithm for tags' anti-collision was proposed. The algorithm brought stack into the new jumping before and after searching strategy to reduce the number of collision slots and avoid idle slots. When requested by readers, it adopted dynamic transmission and variable length adjustment strategy, and used the known information remembered by the feedback tags' information to identify the unknown data bits of tags, which reduced the number of search of readers and the transmission of system. The analysis on simulation results indicates that the proposed algorithm performs significantly better than the existing anti-collision algorithms. The transmission is greatly reduced, and throughput of the system has increased significantly.

Concerning the shortcomings of big recognition error and bad real-time calculation by the minimum differential directional algorithm, a method in combination with local standard deviation and directional derivation was put forward to identify the blurred direction of motion blur image. Firstly, the motion blur image was filtered by local standard deviation to enhance texture details in blurred direction. Secondly, the minimum directional derivation summation was obtained by bilinear interpolation, and its corresponding direction was the blurred direction. Meanwhile, the inherent law was found after concluding and analyzing the minimum directional derivation summation curve. Based on this law, the method of search range decreasing in half was presented to search minimum, which could reduce searching times. The simulation results show that this algorithm not only has high precision and strong immunity, but also meets the requirement of real-time calculation.