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An optimal distributed state estimation method based on sensor network

A technology of sensor network and state estimation, which is applied in design optimization/simulation, instrumentation, calculation, etc., can solve the problem of not being able to deal with nonlinear interference sensor network state estimation at the same time, and achieve the effect of easy solution and improved accuracy

Active Publication Date: 2018-12-07
HARBIN UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
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Problems solved by technology

[0004] The purpose of the present invention is to solve the state estimation problem of the sensor network with multiplicative noise and randomly occurring nonlinear interference phenomenon that the existing state estimation method cannot handle at the same time

Method used

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  • An optimal distributed state estimation method based on sensor network
  • An optimal distributed state estimation method based on sensor network
  • An optimal distributed state estimation method based on sensor network

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specific Embodiment approach 1

[0023] Specific implementation mode one: combine figure 1 This embodiment will be described. A sensor network-based optimized distributed state estimation method described in this embodiment, the specific steps of the method are:

[0024] Step 1. Establishing a sensor network-based dynamic model of a time-varying system with multiplicative noise and randomly occurring nonlinear disturbances;

[0025] Step 2, constructing the distributed filter equation of the dynamic model established in step 1, and utilizing the distributed filter equation to estimate the state of the dynamic model of the time-varying system;

[0026] Step 3, calculate the upper bound Ξ of the one-step prediction error covariance matrix of the dynamic model at time k k+1|k ;

[0027] Step 4, the one-step prediction error covariance matrix upper bound Ξ of the dynamic model that obtains according to step 3 at time k k+1|k , calculate the gain matrix K of the i-th sensor in the dynamic model at time k+1 ij...

specific Embodiment approach 2

[0032] Specific implementation mode two: this implementation mode further defines a sensor network-based optimized distributed state estimation method described in implementation mode one, and the specific process of step one is:

[0033] A dynamic model of a sensor network-based time-varying system with multiplicative noise and stochastic nonlinearity is established, and the state space form of the dynamic model is:

[0034]

[0035]

[0036] in, is the state vector of the dynamic model at time k, is the state vector of the dynamic model at time k+1, is the real field of the state of the dynamic model, n is the dimension;, y i,k is the measurement output of the i-th sensor of the dynamic model at time k, i=1,2,...,n, N is the number of sensors of the dynamic model; A k is the system matrix, α k is the multiplicative noise of the dynamic model, is the system disturbance matrix; β i,k is the multiplicative noise of the i-th sensor at time k, C i,k is the measur...

specific Embodiment approach 3

[0044]Specific embodiment three: this embodiment further defines a sensor network-based optimized distributed state estimation method described in embodiment two, and the specific process of step two in this embodiment is:

[0045] Construct the distributed filter equation as follows:

[0046]

[0047]

[0048] in, is the state estimate of the i-th sensor at time k, is the estimate of the i-th sensor at time k+1, is the one-step prediction of the i-th sensor at time k, is the independent variable The nonlinear function of K ij,k+1 is the gain matrix of the i-th sensor of the dynamic model at time k+1, Represents the set of all sensors coupled with the i-th sensor; when j is in When inside, h ij = 1, otherwise h ij = 0, h ij Represents the connection relationship between the i-th sensor and the j-th sensor; y j,k+1 is the measurement output of sensor j at time k+1; is the one-step prediction of sensor j at time k; C j,k+1 is the measurement matrix of s...

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Abstract

An optimized distributed state estimation method based on a sensor network is used in the field of control system and signal processing technology. The present invention solves the problem that existing conventional state estimation method cannot simultaneously process the state estimation of a sensor network with multiplicative noise and a non-linear interference phenomenon randomly occurring. Atthat same time, the method considers the influence of multiplicative noise and randomly occurring non-linearity on the state estimation performance, and a distributed filtering method based on Riccati-Riccati difference equation is obtained. Compared with existing conventional state estimation method of a nonlinear time-varying system, the method of the invention can control the estimation errorin a very small range, and can improve the estimation accuracy by more than 10% while being easy to solve. The invention can be applied to the field of control system and signal processing technology.

Description

technical field [0001] The invention belongs to the technical field of control systems and signal processing, and in particular relates to an optimized distributed state estimation method based on a sensor network. Background technique [0002] Distributed filtering is an important research problem in control systems and signal processing, and has been widely used in signal processing tasks in the fields of aircraft formation, target tracking systems, environmental and ecological monitoring, health monitoring, home automation, and traffic control. [0003] For sensor networks with multiplicative noise and randomly occurring nonlinear disturbance phenomena, these phenomena have been affecting the performance of state estimation methods since the existing state estimation methods cannot simultaneously deal with the state estimation problem of complex networks with such phenomena . Contents of the invention [0004] The purpose of the present invention is to solve the proble...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06F17/11G06F17/15G06F17/16
CPCG06F17/11G06F17/15G06F17/16G06F30/18G06F30/20
Inventor 胡军王志功陈东彦张红旭于浍李佳兴
Owner HARBIN UNIV OF SCI & TECH
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