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Nonlinear system state estimation method based on Kalman filtering positioning

A Kalman filter and nonlinear system technology, applied in the field of nonlinear system state estimation based on Kalman filter positioning, can solve problems such as large root mean square error and inaccurate state estimation

Active Publication Date: 2017-05-10
XIDIAN UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to provide a nonlinear system state estimation method based on Kalman filter positioning, which aims to solve the problems of large root mean square error and inaccurate state estimation in existing nonlinear system state estimation methods

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  • Nonlinear system state estimation method based on Kalman filtering positioning
  • Nonlinear system state estimation method based on Kalman filtering positioning
  • Nonlinear system state estimation method based on Kalman filtering positioning

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Embodiment 1

[0060] refer to figure 2 , the implementation steps of the strongly adaptive Kalman filter design invention for the SCKF algorithm are as follows:

[0061] Step 1. Initialize filtering

[0062] Define the initial value of the state vector of the system at k=0 Its covariance matrix is ​​P 0|0 , get the square root S of the variance matrix by QR decomposition 0|0 , simulating and generating the RSSI values ​​of the beacon nodes within n communication ranges received by the node to be positioned;

[0063] Step 2. Loop through SCKF

[0064] For k=1,2,...N, N is the number of samples, the obtained in step 2.2 and the S obtained in step 2.1 k|k As an input, repeat steps 2.1 and 2.2 until N samples are completed.

[0065] According to formula (5), considering that the state vector of the node to be located at time k satisfies Estimated by this order, the estimated value obtained by SCKF at this time and The error between should conform to an additive noise relationshi...

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Abstract

The invention discloses a nonlinear system state estimation method based on Kalman filtering positioning, and proposes a strongly adaptive Kalman filtering mechanism which combines a nonlinear filtering algorithm and Kalman filtering. The method comprises the steps: carrying out the simultaneous estimation of node positions and channel parameters through employing an RSSI state estimation algorithm based on square root volume Kalman filtering, and obtaining the estimation value of a state vector; employing the Kalman filtering for further processing according to the linear change of a state equation, obtaining optimal estimation, and building a strongly adaptive square root volume Kalman filtering algorithm; giving the design steps of the strongly adaptive square root volume Kalman filtering algorithm; and calculating a theoretical square root error lower bound under a state space model based on the RSSI state estimation. The method enables an estimation result to be improved, and improves the precision. The method does not need to excessively depend on improper initial conditions, can be well adapted to a highly nonlinear system, and is not liable to enable the algorithm to be divergent and ineffective.

Description

technical field [0001] The invention belongs to the technical field of communication, in particular to a nonlinear system state estimation method based on Kalman filter positioning. Background technique [0002] As a product of the combination of computing, communication and sensor technology, Wireless Sensor Networks (WSNs) have changed the way humans interact with nature, integrated the logical information world with the objective physical world, and enhanced human The ability to understand and change the world. In the application domain of WSNs, sensory information without node locations will not produce any value. In order to successfully complete the monitoring task, randomly scattered sensor nodes need to have the ability to provide their own location information in a timely and accurate manner, which is crucial to the realization of the entire system function. The most commonly used classification method is to divide the node positioning method according to whether ...

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Application Information

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IPC IPC(8): G01S5/02
CPCG01S5/02
Inventor 张朝辉刘三阳王月娇
Owner XIDIAN UNIV
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