Abstract
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.
Highlights
With the rapid development and maturity of hardware manufacturing and software development technologies, such as embedded technology, micro-electromechanical systems, wireless communication technology, and sensor technology, the development and application of highly integrated, multifunctional and miniaturized sensor nodes has become may. These sensor nodes have the capabilities of information collection, data processing and wireless communication
At the beginning of the transition from military to civilian use, wireless sensor networks were mainly used in environmental monitoring
We divide the sensor nodes into different clusters according to the location and energy of the nodes in the wireless sensor network
Summary
With the rapid development and maturity of hardware manufacturing and software development technologies, such as embedded technology, micro-electromechanical systems, wireless communication technology, and sensor technology, the development and application of highly integrated, multifunctional and miniaturized sensor nodes has become may. Distributed data processing and coordinated work of multiple sensor nodes make tracking more comprehensive. For the Bayesian method of dynamic estimation, it is necessary to use all available information to construct the posterior density function of the state [5]. The update phase uses the latest measured value to modify the predicted density function This is achieved by the Bayesian theory of a mechanism for updating the target state based on additional information from new data [10]. From the perspective of Bayesian theory, this problem is to iteratively obtain a certain degree of credibility under a given situation This requires the construction of the posterior probability density function [13].
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