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Markov-chain-Monte-Carlo-based particle filter positioning method

A Markov Chain Monte and particle filtering technology, applied in satellite radio beacon positioning systems, radio wave measurement systems, measurement devices, etc., can solve problems such as low positioning accuracy and infrequent

Active Publication Date: 2016-08-24
NANJING INST OF TECH
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  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

It is also possible to use the "proximity method" to roughly determine which reference node the terminal is near. This method has low positioning accuracy and is not common in practical applications.

Method used

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  • Markov-chain-Monte-Carlo-based particle filter positioning method

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Embodiment Construction

[0063] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0064] Such as figure 1 As shown, the localization method based on Markov chain Monte Carlo particle filter includes the following steps:

[0065] Step 1, take time k=1.

[0066] Step 2, from the probability density function p(x k ) to extract a set of initial particles

[0067] is the i-th particle extracted from the probability density function at time k, i∈[1,N].

[0068] Step 3, k=k+1.

[0069]Step 4, particle importance sampling is

[0070] x k i ~ q ( x k | x 1 : ...

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Abstract

The invention discloses a Markov-chain-Monte-Carlo-based particle filter positioning method. The method comprises: step one, carrying out initialization; step two, carrying out particle importance sampling; step three, calculating a weight value; step four, carrying out resampling; step five, carrying out correlation determination; step six, introducing an MCMC moving step; and step seven, entering a next time. According to the invention, correlation between two-times filtering is analyzed to determine whether a particle is centralized; cloud observation data are fused into the particle rate importance sampling stage to reduced the needed particle number; and the particle deficiency effect is reduced based on Markov chain Monte Carlo movement processing. Because the Markov-chain-Monte-Carlo particle filter positioning mechanism is established, cloud monitoring data and the map match well, thereby realizing intelligent parking.

Description

technical field [0001] The invention relates to a positioning method based on a Markov chain Monte Carlo particle filter, and belongs to the technical field of application of the Internet of Things. Background technique [0002] With the increase of personal vehicles, it is now a big problem to find cars in the reverse direction. Sometimes you can’t find a parking spot after turning around a few times. Some shopping malls and communities have underground parking lots, which increases a lot of parking spaces. But some friends are reluctant to park their cars in the underground parking lot. The reason is that they are not familiar with the parking rules of the parking lot at the address. It is difficult to find a car when parking. This has become one of the common problems encountered in life. How to efficiently find a car in reverse, through real-time monitoring and effective positioning control measures, can effectively solve the problem of parking and finding a car. [000...

Claims

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Application Information

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IPC IPC(8): G01S19/45G01C21/20H04L29/08
CPCG01C21/20G01S19/45H04L67/10
Inventor 戴慧刘伟伟
Owner NANJING INST OF TECH
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