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Distributed algorithm for simultaneously carrying out node self-positioning and target tracking

A distributed algorithm and target tracking technology, applied in the field of node self-positioning and target tracking, can solve the problems of difficult to accurately reflect the RSS value, limited accuracy, poor real-time performance, etc., to ensure model matching, eliminate truncation errors, and facilitate real-time processing. Effect

Inactive Publication Date: 2013-12-25
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology

But at the same time, due to the simplicity of the RSS detection method and the limited accuracy of the indicator itself, RSS observations are mostly incomplete observation data, that is, there are inevitably rounding errors, truncation errors, and non-line-of-sight errors, so that the observed RSS value is difficult to accurately reflect the actual RSS
To solve this problem, the existing solutions first use the expectation maximization algorithm to estimate the parameters of the path loss model to improve the positioning accuracy before performing node positioning. The process of parameter estimation requires offline data processing, and the real-time performance is poor.

Method used

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  • Distributed algorithm for simultaneously carrying out node self-positioning and target tracking
  • Distributed algorithm for simultaneously carrying out node self-positioning and target tracking
  • Distributed algorithm for simultaneously carrying out node self-positioning and target tracking

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Embodiment 1

[0030] Such as figure 1 As shown, the large hollow circle represents the cluster head node, the small hollow circle represents the ordinary node, and the solid circle represents the moving target, where the position of the cluster head node is known, and the whole area is randomly divided into M sub-areas W a ,a∈[1,…M], the moving target moves randomly in the whole area. First, when the target enters a certain sub-area, the nearby cluster head node i is activated, and works with multiple adjacent cluster head nodes to determine the sub-area W where the target is located. i . Then, the subregion W i Ordinary nodes in the adjacent area are activated to coordinate the precise positioning of moving targets and the self-positioning of ordinary nodes.

[0031] This algorithm is implemented on the computing centers of cluster head nodes and ordinary nodes respectively. First, each cluster head node estimates the parameters of the observation model online based on the Expectation ...

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Abstract

The invention provides a distributed algorithm for simultaneously carrying out node self-positioning and target tracking. A plurality of cluster node positions are set to be known; a plurality of common nodes are set to be unknown; existence of a mobile target is set; the mobile target moves within an entire region at random; meanwhile, the entire region is divided into M sub-regions at random. The method concretely comprises the following steps: 100, carrying out path loss model parameter estimation under incomplete observation; 101, carrying out coarse positioning of distributed targets on cluster nodes; 102, accurately positioning the targets on the common nodes; 103, carrying out self-positioning when carrying out target tracking on the common nodes. By adopting the scheme, the effects of noise signals such as a truncation error in incomplete observation data can be effectively removed; real-time matching of each node observation model and the external environment is ensured; the model matching of node positioning and target tracking carried out at the same time is also ensured.

Description

technical field [0001] The invention belongs to the technical field of node self-location and target tracking, and in particular relates to a distributed algorithm for simultaneous node self-location and target tracking under incomplete observation. Background technique [0002] Similar to the "simultaneous localization and mapping" (SLAM) problem in robot autonomous navigation, the algorithm research of node localization and target tracking at the same time also faces a dilemma: in order to track the target, the node needs to know its own position information; , multiple nodes need to specify the location information of the target. It's like an "egg-chicken" problem. The node needs to maintain two models at the same time, and perform node positioning and target tracking at the same time. [0003] In recent years, the algorithm research of simultaneous node location and target tracking has become a research hotspot in wireless location tracking technology. Under the initi...

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Application Information

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IPC IPC(8): H04W64/00
Inventor 姜向远张焕水
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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