CN109543143A - The Multi-sensor Fusion estimation method of non-linear belt bias system - Google Patents

The Multi-sensor Fusion estimation method of non-linear belt bias system Download PDF

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CN109543143A
CN109543143A CN201910079241.2A CN201910079241A CN109543143A CN 109543143 A CN109543143 A CN 109543143A CN 201910079241 A CN201910079241 A CN 201910079241A CN 109543143 A CN109543143 A CN 109543143A
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葛泉波
王宏
张建朝
牛竹云
何美光
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Hangzhou Dianzi University
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Abstract

The present invention relates to a kind of Multi-sensor Fusion estimation methods of non-linear belt bias system based on Dispersion Fusion filtering technique.For there are the estimation problems of non-linear more sensor-based systems of dynamic deviation, present invention adds Dispersion Fusion filtering techniques, propose two stages volume Kalman filtering fusion estimation method.In Dispersion Fusion structure, each sensor needs to send fusion center for the state estimation information of oneself, meanwhile, all filters carry out time update to state estimation information, obtain predicted value.Each local filter carries out measurement update to predicted value, obtains local state estimated information.In fusion center, the state estimation information of all filters is handled, globalstate estimation information is obtained.The performance of the method for the present invention is better than the two-stage Kalman filter method of single-sensor.

Description

The Multi-sensor Fusion estimation method of non-linear belt bias system
Technical field
The invention belongs to filter estimation field, in particular to a kind of more biographies with bias system based on Dispersion Fusion technology Sensor merges estimation method.
Background technique
In reality, nonlinear system occupies very big ratio, and in nonlinear system, due to various reasons, system shape State or measurement may be influenced by dynamic deviation.In face of above situation, the accurate estimation for how carrying out system mode is had become One important content.Conventional is quite a lot for the method for estimating state of zero deflection nonlinear system, such as expansion card Thalmann filter, Unscented kalman filtering device etc., and it is actually rare for the method for estimating state of the nonlinear system with deviation. For the band bias system being widely present, finds the new estimation method of one kind and be necessary.
In face of above situation, the accurate estimation for how carrying out system mode is had become for an important content.Usual situation Under, dynamic deviation is that linearly, nonlinear system can be divided into not by the zero deflection status system of deviation effects and deviation system System.Since state equation is a nonlinear equation, nonlinear Estimation Algorithms are can be used to estimate (such as to hold in zero deflection state G-card Thalmann filter), skew component is linear equation, and deviation can be obtained by being approximately that linear filter is estimated To the estimated value of deviation.Then, it is combined by a fusion factor, obtains the estimated value of system mode.Since matrix is transported The separation of calculation, hence it is evident that reduce calculation amount, receive the favor of related researcher.
Existing non-linear two-stage Kalman filter device research is mainly based upon single-sensor, based on the non-of multisensor Linear two-stage Kalman filter device is fewer.Due to the data sheet one of single-sensor, estimated accuracy is not high, and more biographies can be used Sensor estimates that system mode, wherein Dispersion Fusion estimation is a kind of extraordinary estimation to non-linear belt bias system Method.In this structure, senior filter and local filter resultant force are estimated, available more accurate estimated value.
Summary of the invention
The case where for being filtered using multisensor to nonlinear system, is popular in the more biographies of centralization of linear system Sensor information fusion (by measure vector be augmented) and distributed multi-sensor information merge mode be no longer appropriate for, this be because Precision for them is too poor or solution procedure is too complicated.Based on two stages volume Kalman filter, Dispersion Fusion skill joined Art proposes two stages volume Kalman and merges estimator.Senior filter uses the output information of each local filter, fusion The global estimated value to system mode is obtained, and reasonably feeds back to each local filter.Compared to two ranks of single-sensor Duan Rongji Kalman filter, new method improve estimated accuracy.
The present invention can be generally divided into three parts.First part is that system model is established；Second part constructs two stages Volume Kalman filter；It obtains two stages volume Kalman according to local filter information in Part III and merges estimation Device.
Beneficial effects of the present invention: non-linear belt bias system can accurately be estimated, and relative to common patrilineal line of descent with only one son in each generation Sensor estimation method, the method for proposition can obtain higher system state estimation precision.
Detailed description of the invention
Fig. 1 invention's principle block diagram.
Fig. 2 is the detailed process figure of step 3.
Specific embodiment
As shown in Figure 1, specific implementation step of the invention:
Step 1. system modelling
Considering the band non-linear multisensor syste of deviation is model, and systematic procedure noise statistics are it is known that non-linear The state equation of multisensor syste, deviation equation and measurement equation mathematical description are as follows:
yi,k=hi(xk)+Di,kbk+vi,k (3)
In formula, k indicates time series；xk, bkAnd yi,kRespectively system n ties up state vector, m ties up bias vector and i-th The p of sensor ties up observation vector；And vi,kRespectively system mode noise vector, system deviation noise vector and i-th The measurement noise vector of sensor；f(xk) it is state transition function；hi(xk) be i-th of sensor state observation function.It crosses Journey noise, deviation noise and measurement noise are zero mean Gaussian white noise sequences: vi,k~N (0, Vi,k)。
Step 2. use single-sensor two stages volume Kalman filter, seek respectively zero deflection state estimated value and The estimated value of deviation
The sampling point set (volume point set) of two stages volume Kalman filter are as follows:
In view of Fig. 1, the estimated information of zero deflection state is obtained:
The residual error of deviation can be represented as:
The covariance matrix of deviation can be represented as:
Obtain linear filter bias state estimated information:
bk+1/k=bk/k (17)
Due to the presence of state transition function and measurement functions, need with zero deflection state estimationAnd predicted valueBased on, approximate statement is carried out to above-mentioned two function:
Therefore, system mode can be represented as:
Step 2 is as shown in Figure 1.
Step 3. carries out Dispersion Fusion to N number of two stages volume Kalman filter, obtains the estimation letter to system mode Breath
Dispersion Fusion filters a kind of good filtering estimation method to non-linear belt bias system of can yet be regarded as.In Dispersion Fusion In structure, each sensor needs to send fusion center for the state of oneself and its error co-variance matrix.Meanwhile all filters Wave device updates to obtain predicted value using state estimation and error co-variance matrix the progress time of last moment.In turn, each Local filter carries out measurement update using the predicted value of oneself, obtains local state estimated information.In fusion center, institute is utilized There is the state estimation information of filter, handled, obtains the global error covariance matrix of globalstate estimation value and it.The I zero deflection state filter and senior filter seek state estimation information:
Zero deflection state filter:
Senior filter:
I-th of zero deflection state filter measures update:
In formula, Yi,k+1/k+1For the inverse matrix of the partial estimation covariance matrix of zero deflection state,For zero deflection shape The inverse matrix of the partial estimation of state.
In Dispersion Fusion structure, overall situation estimation is fed back to each localized sensor to handle next survey by fusion center Magnitude, therefore the predictive information vector sum matrix of each partial estimation device is identical:
Therefore, the overall situation is estimated as
In formula, Yg,k+1/k+1For the global estimate covariance inverse of a matrix matrix of zero deflection state,For zero deflection shape The inverse matrix of the global estimation of state.
Then, the estimated result of zero deflection state filter and deviation filter is combined, is obtained to system mode Estimated information
In formula, Vi,k+1For fusion factor, step 3 is as shown in Figure 2.

Claims (1)

1. the Multi-sensor Fusion estimation method of non-linear belt bias system, it is characterised in that method includes the following steps:
Step 1. system modelling
Considering the band non-linear multisensor syste of deviation is model, and systematic procedure noise statistics are it is known that non-linear more biographies State equation, deviation equation and the measurement equation of sensor system are described as follows:
yi,k=hi(xk)+Di,kbk+vi,k (3)
In formula, k indicates time series；xk,bkAnd yi,kRespectively system n ties up state vector, m dimension bias vector and i-th of sensing The p of device ties up observation vector；And vi,kRespectively system mode noise vector, system deviation noise vector and i-th of sensing The measurement noise vector of device；f(xk) it is state transition function；hi(xk) be i-th of sensor state observation function；Process is made an uproar Sound, deviation noise and measurement noise are zero mean Gaussian white noise sequences: vi,k~N (0, Vi,k)；
Step 2. uses single-sensor two stages volume Kalman filter, seeks the estimated value and deviation of zero deflection state respectively Estimated value
The sampling point set of two stages volume Kalman filter are as follows:
The estimated information of zero deflection state:
The residual error of deviation indicates are as follows:
The covariance matrix of deviation indicates are as follows:
Obtain linear filter bias state estimated information:
bk+1/k=bk/k (17)
Due to the presence of state transition function and measurement functions, need with zero deflection state estimationAnd predicted valueFor base Plinth carries out approximate statement to function:
Therefore, system mode is expressed as:
Step 3. carries out Dispersion Fusion to N number of two stages volume Kalman filter, obtains the estimated information to system mode
In Dispersion Fusion structure, the state of oneself and its error co-variance matrix are sent fusion center by each sensor； Meanwhile all filters are updated using state estimation and error co-variance matrix the progress time of last moment and are predicted Value；In turn, each local filter carries out measurement update using the predicted value of oneself, obtains local state estimated information；Melting Conjunction center is handled using the state estimation information of all filters, obtains the global error of globalstate estimation value and it Covariance matrix；I-th of zero deflection state filter and senior filter seek state estimation information:
Zero deflection state filter:
Senior filter:
I-th of zero deflection state filter measures update:
In formula, Yi,k+1/k+1For the inverse matrix of the partial estimation covariance matrix of zero deflection state,For zero deflection state The inverse matrix of partial estimation；
In Dispersion Fusion structure, overall situation estimation is fed back to each localized sensor to handle next measurement by fusion center Value, therefore the predictive information vector sum matrix of each partial estimation device is identical:
Therefore, the overall situation is estimated as
In formula, Yg,k+1/k+1For the global estimate covariance inverse of a matrix matrix of zero deflection state,For zero deflection state The inverse matrix of overall situation estimation；
Then, the estimated result of zero deflection state filter and deviation filter is combined, obtains estimating system mode Count information xk+1/k+1,
In formula, Vi,k+1For fusion factor.
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CN110116139A (en) * 2019-04-28 2019-08-13 燕山大学 The decoupling method that cold-strip whole roll-type plate profile instrument channel intercouples
CN110490273A (en) * 2019-09-12 2019-11-22 河南牧业经济学院 The multisensor syste fused filtering algorithm that noise variance inaccurately models
CN111062359A (en) * 2019-12-27 2020-04-24 广东海洋大学深圳研究院 Two-stage Kalman filtering fusion method based on noise sequential decorrelation
CN111107070A (en) * 2019-12-10 2020-05-05 浙江工业大学 Target tracking method under DoS attack
CN111756353A (en) * 2020-06-12 2020-10-09 杭州电子科技大学 Liquid level meter noise optimization method based on nonlinear fusion filtering
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CN112649804A (en) * 2020-12-21 2021-04-13 杭州电子科技大学 Centralized multi-sensor fusion filtering method based on characteristic function
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CN110116139B (en) * 2019-04-28 2020-05-05 燕山大学 Decoupling method for mutual coupling of cold-rolled strip whole-roller type strip shape meter channels
CN110116139A (en) * 2019-04-28 2019-08-13 燕山大学 The decoupling method that cold-strip whole roll-type plate profile instrument channel intercouples
CN110490273A (en) * 2019-09-12 2019-11-22 河南牧业经济学院 The multisensor syste fused filtering algorithm that noise variance inaccurately models
CN111107070A (en) * 2019-12-10 2020-05-05 浙江工业大学 Target tracking method under DoS attack
CN111062359B (en) * 2019-12-27 2023-03-31 广东海洋大学深圳研究院 Two-stage Kalman filtering fusion method based on noise sequential decorrelation
CN111062359A (en) * 2019-12-27 2020-04-24 广东海洋大学深圳研究院 Two-stage Kalman filtering fusion method based on noise sequential decorrelation
CN111756353A (en) * 2020-06-12 2020-10-09 杭州电子科技大学 Liquid level meter noise optimization method based on nonlinear fusion filtering
CN111756353B (en) * 2020-06-12 2024-04-16 杭州电子科技大学 Nonlinear fusion filtering-based noise optimization method for liquid level instrument
CN112489075A (en) * 2020-12-03 2021-03-12 杭州电子科技大学 Sequence inertia type multi-sensor fusion filtering method based on characteristic function
CN112489075B (en) * 2020-12-03 2024-02-06 杭州电子科技大学 Sequential inertial multi-sensor fusion filtering method based on characteristic function
CN112649804A (en) * 2020-12-21 2021-04-13 杭州电子科技大学 Centralized multi-sensor fusion filtering method based on characteristic function
CN112561834A (en) * 2020-12-21 2021-03-26 杭州电子科技大学 Parallel multi-sensor fusion filtering method based on characteristic function
CN113011475A (en) * 2021-01-29 2021-06-22 深圳信息职业技术学院 Distributed fusion algorithm considering correlated noise and random parameter matrix
CN113011475B (en) * 2021-01-29 2022-12-02 深圳信息职业技术学院 Distributed fusion method considering correlated noise and random parameter matrix
CN115022348A (en) * 2022-05-27 2022-09-06 江南大学 High-end battery intelligent factory cloud-level architecture data storage method

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