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Indoor positioning method based on particle filtering under condition of non-Gaussian noises

A non-Gaussian noise and particle filter technology, applied in positioning, radio wave measurement systems, measurement devices, etc., can solve the problems that the optimal density function cannot be directly obtained, and the efficiency and accuracy of calculation cannot be guaranteed, so as to improve calculation efficiency and Accuracy, improvement of degradation phenomenon, and effect of reducing dependence

Active Publication Date: 2015-07-22
SOUTHEAST UNIV
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Problems solved by technology

In order to overcome the degradation phenomenon, it is necessary to select a good important density function, but in the actual process, the observation equation is nonlinear, and the optimal density function cannot be directly obtained, so the efficiency and accuracy of the calculation cannot be guaranteed

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Embodiment Construction

[0024] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

[0025] Such as Figure 1-2 As shown, a particle filter-based indoor positioning method under non-Gaussian noise conditions uses a single ultra-wideband (UWB) anchor node to locate a single user. In this method, the acceleration in the state equation and the measurement noise in the observation equation are modeled as mixed Gaussian random variables, and the observation equation is locally linearized to obtain the suboptimal importance function, and then particle filtering is performed to obtain the state quantity Optimal estimation, the specific steps of the method:

[0026] (1) Establish the state equation and observation equation of the moving target motion, approximate the probability distribution of the acceleration vector in the state equation through a mixed Gaussian distribution, and use the mixed Gaussian distribution to approximate th...

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Abstract

The invention discloses an indoor positioning method based on particle filtering under the condition of non-Gaussian noises. The indoor positioning method comprises the following steps of modeling movement accelerated speed of an object and measurement noises into random vectors which obey Gaussian mixture distribution in a training stage by using a particle filtering method based on a suboptimum important function; and performing local linearization on a non-linear observation equation in a positioning state so as to obtain a suboptimum important function and a weight coefficient, change a degradation phenomenon in particle filtering and implement optimum estimation on state vectors. The indoor positioning method has the advantages that on one hand, compared with Gaussian noises, modeling of the Gaussian mixture model is close to actual conditions, and errors caused by model approximation can be reduced effectively; and on the other hand, the degradation speed of the weight coefficient in the particle filtering process can be increased through the solved suboptimum important function, the algorithm efficiency and the algorithm precision are improved, and the positioning precision is improved effectively.

Description

technical field [0001] The invention belongs to the technical field of wireless positioning. Background technique [0002] How to achieve precise positioning and tracking of moving targets in an indoor environment is one of the focuses of indoor positioning research. Among existing positioning technologies, ultra-wideband (UWB) technology can achieve centimeter-level ranging accuracy. Due to the influence of the indoor environment on the transmission of electromagnetic waves, the actual ranging value will be affected by non-Gaussian noise, resulting in a large deviation. The existing method is to assume that the noise obeys a normal distribution and perform Kalman filtering on the measurement results. However, the actual measurement noise often does not obey the unimodal normal distribution, but presents a multimodal distribution. Therefore, approximating the observation noise to a normal distribution can reduce the amount of calculation, but it is difficult to accurately ...

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Application Information

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IPC IPC(8): G01S5/10
CPCG01S5/10
Inventor 夏玮玮章跃跃沈连丰宋铁成胡静
Owner SOUTHEAST UNIV
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