Wireless sensor network target positioning and tracking method

Abstract

The invention discloses a target positioning and tracking method for a wireless sensor network. The method comprises the following steps: at any positioning time, estimating a position of a target according to measurement information of a sensor node, building a learning region covering the estimated position of the target, selecting an arbitrary quantity of position points in the learning region, obtaining a decision function by use of a polynomial kernel function and a mapping relationship of the distance vector from an approaching position point of an epsilon-support vector machine for regression to the sensor node and a coordinate of the position point; and obtaining an estimated value of the position of the target by inputting a distance vector from the sensor node to the target intothe decision function and transmitting an estimated value of the position of the target to a base station which then fits the historical data of the position of the target and upgrades the motion patch of the target to complete target tracking. The method obviously reduces the influence of measurement errors of the sensor node on the target positioning and tracking and improves the accuracy of target tracking.

Description

technical field [0001] The invention relates to a wireless sensor network target positioning and tracking method, in particular to a wireless sensor network target positioning and tracking method based on an ε-support vector regression machine. Background technique [0002] Wireless sensor network is a new generation of sensor network, which has a very broad application prospect. Target positioning and tracking is one of the important applications of wireless sensor networks. It requires high accuracy in target position and trajectory estimation, but sensor node measurement information usually contains large noise, which directly affects the accuracy of target positioning and tracking. , under the same measurement information, different methods have different suppression abilities to the influence of measurement noise. The traditional target position estimation method uses the least square method and the maximum likelihood estimation method to determine the position of the ...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problem that the existing wireless sensor network target positioning and tracking methods are affected by sensor node measurement errors, the target positioning results and target trajectory fitting accuracy are low, the present invention provides a wireless sensor network target positioning and tracking method, Improving the accuracy of target localization and target trajectory estimation

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