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centralized two-stage Kalman estimation method with related measurement noise

A technology for related measurement and measurement of noise, applied in the field of filters, can solve problems such as noise, and achieve the effect of reducing computational complexity

Pending Publication Date: 2019-06-11
HANGZHOU DIANZI UNIV
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Problems solved by technology

(3) In fields that require multi-sensor fusion, such as maneuvering target tracking, there are often noise-related situations
However, in practical situations, noise-dependent systems often exist

Method used

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  • centralized two-stage Kalman estimation method with related measurement noise
  • centralized two-stage Kalman estimation method with related measurement noise
  • centralized two-stage Kalman estimation method with related measurement noise

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Embodiment Construction

[0015] Concrete implementation steps of the present invention can be found in figure 1 , including the following steps:

[0016] Step 1. System Modeling

[0017] A common multi-sensor system with bias is considered as a model. The statistical characteristics of the system state noise are known. The state equation, bias equation and measurement equation of the system with related measurement noise are described as follows:

[0018]

[0019] In the formula, k represents the time series; x k ,b k and y i,k are respectively the n-dimensional state of the system, the m-dimensional deviation and the p-dimensional observation of the i-th sensor; and v i,k are the system state noise, system deviation noise and measurement noise of the i-th sensor, respectively; A k+1,k ∈R n×n is the state transition matrix; C i,k ∈ R p×n is the state matrix of the i-th sensor. State noise, bias noise, and measurement noise are all zero-mean white Gaussian noise sequences: And the corre...

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Abstract

The invention relates to a centralized two-stage Kalman estimation method with related measurement noise. Aimied at the filtering problem of a multi-sensor measurement system with related measurementnoise influencing a measurement value, a measurement equation with measurement noise irrelevance is re-established by introducing a decorrelation technology, and an optimal estimation value of a system state is obtained through a two-stage Kalman filter. Compared with a two-stage Kalman method directly used, the method provided by the invention has the advantages that although the data fusion results are the same, the calculation complexity is greatly reduced.

Description

technical field [0001] The invention belongs to the technical field of filters, in particular to a centralized two-stage Kalman estimation method with correlated measurement noise. Background technique [0002] Kalman filtering techniques require accurate models of process dynamics and measurements. In many practical situations, biases affect system dynamics and observations, and can lead to performance degradation if biases are not incorporated into the model. The two-stage approach is very effective in dealing with state estimation of systems with unknown biases, as it can improve computational performance and prevent the occurrence of the curse of dimensionality. [0003] In the 1960s, Friedland proposed a two-stage Kalman filter (TKF), whose basic idea is to decouple the enhanced state filter (ASKF) into two filters, namely a low-dimensional "unbiased" filter and a " biased" filter, and the best estimate can be viewed as the output of the unbiased filter corrected by t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/11G06F17/16
Inventor 葛泉波王宏张建朝牛竹云何美光
Owner HANGZHOU DIANZI UNIV
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