An Adaptive Sine/Cosine Filtering Algorithm Based on Speckle Phase Fringe Orientation

Hanyang, 蒋汉阳 Jiang; Meiling, 戴美玲 Dai; Zhilong, 苏志龙 Su; Fujun, 杨福俊 Yang; Xiaoyuan, 何小元 He

doi:10.3788/aos201737.0910001

Cited by 4 publications

(1 citation statement)

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“…In order to adapt to the above-mentioned environment, some people proposed the GMM algorithm [15]. The idea of the GMM algorithm is: set K different situations to describe the color content of the same pixel in a video image sequence [16].…”

Section: B Target Detection Gmm Algorithmmentioning

confidence: 99%

Multi-Target Tracking and Detection Based on Hybrid Filter Algorithm

Yuan

Wang

2020

IEEE Access

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Wireless sensor network is a fast-growing research field. In recent years, it has attracted considerable research attention. The generation of large-scale sensor networks interconnected hundreds of sensor nodes, opening up some technical challenges and huge application potential. This article mainly introduces multi-target tracking and detection based on wireless sensor network. This paper studies the positioning methods and target tracking algorithms of wireless sensor network nodes. It mainly studies the positioning algorithms based on ranging in the positioning algorithms in detail, analyzes the advantages and disadvantages of the algorithms, and analyzes the system models and related targets in the target tracking algorithms. The filtering has been studied in detail. In addition, a tracking algorithm under the mixed linear and non-linear motion of the moving target is also proposed, namely the hybrid filtering algorithm. This algorithm makes the motion state of the tracked moving target no longer restricted, and can freely switch between linear motion and nonlinear motion. The experimental results in this paper show that Kalman filter can effectively track moving targets without sudden changes in speed. When the mobile robot switches the grid, it will bring about the switching of the observation model. Compared with the least squares positioning algorithm, the smooth switching rate of the Kalman filter positioning algorithm is increased by 24%. When the three robots are running at a speed of 0. 5m/s in the monitoring area, the system can track the target in real time and send the positioning result to the robot to provide position navigation for the next formation feedback control.

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Section: B Target Detection Gmm Algorithmmentioning

confidence: 99%