High-order interactive multi-model filtering method based on mixture transition distribution

An interactive multi-model and model technology, applied in complex mathematical operations and other directions, can solve problems such as low precision, cumbersome setting process, and high-order Markov chain setting parameters, etc., to achieve high precision, solve setting difficulties, and reduce the possibility of sexual effect

Active Publication Date: 2017-08-04
HARBIN INST OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problems of many high-order Markov chain setting parameters, cumbersome setting process and low precision in the existing generalized high-order interactive multi-model filtering method, and propose a high-order filter based on mixed transfer distribution. Interactive Multi-Model Filtering Method

Method used

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  • High-order interactive multi-model filtering method based on mixture transition distribution
  • High-order interactive multi-model filtering method based on mixture transition distribution
  • High-order interactive multi-model filtering method based on mixture transition distribution

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specific Embodiment approach 1

[0021] Embodiment 1: A high-order interactive multi-model filtering method based on mixed transfer distribution includes the following steps:

[0022] Step 1: Use the mixed transition distribution model to obtain the n-order model sequence transition probability ρ(m k |m k-n ,...,m k-1 );

[0023] Step 2: The estimated state vector is and the corresponding covariance is Perform real-time processing on time k; when k=1, go to step three; when k=2, go to step four; when 3≤k≤n, go to step six; when k>n, go to step Step seven;

[0024] Step 3: After initializing the state when k=1, turn to step 2 and wait for processing the radar observation data at the next k=k+1 moment;

[0025] Step 4: After initializing the state when k=2, go to Step 5;

[0026] Step 5: judge k, when k=n, the n-order model sequence probability U at time k k (m k-n+1 ,...,m k ), the estimated value of the n-order model sequence state and with corresponding covariance After initialization, turn...

specific Embodiment approach 2

[0029] Specific embodiment two: the difference between this embodiment and specific embodiment one is: adopt mixed transfer distribution model to obtain n-order model sequence transition probability ρ(m k | m k-n ,...,m k-1 ) The specific process is:

[0030]

[0031] where m j is the model at time j, j=k-n,...,k, if the number of models is r, then m j The value range of is from 1 to r; is from the model m k-g transfer to model m k The probability, is an element in the first-order Markov chain, λ g is each step factor, satisfying the following conditions:

[0032]

[0033] Other steps and parameters are the same as those in Embodiment 1.

specific Embodiment approach 3

[0034] Specific implementation mode three: the difference between this implementation mode and specific implementation mode one or two is: the described model is specifically:

[0035] x k+1 =F k (m k )X k +G k (m k )u k (m k )+Γ k (m k )v k (m k )

[0036] where X k is the x-axis position x at time k k , x-axis speed y-axis position y k , y-axis speed Composed of state vectors. f k (m k ) means that at time k the model m k The system transition matrix under, G k (m k ) is the input control matrix, u k (m k ) is the signal input, Γ k (m k ) is the noise coefficient matrix, v k (m k ) is the k-time model m k Zero-mean white Gaussian process noise under , with covariance Q k (m k ).

[0037]

[0038] Among them, T represents the sampling interval.

[0039] (1) When the model is a uniform motion model

[0040]

[0041] (2) When the model cooperates with the turning model

[0042]

[0043] (3) When the model uniformly accelerates the moti...

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Abstract

The invention relates to a high-order interactive multi-model filtering method based on mixture transition distribution. In order to solve the problems that in an existing method, high-order Markov chain setting parameters are multiple, setting processes are complex and the precision is low, the method comprises the steps that 1, a mixture transition distribution model is adopted for obtaining n-order model sequence transition probability; 2, real-time processing is performed at the moment k; 3, initialization is performed in the state when k is equal to 1; 4, initialization is performed in the state when k is equal to 2; 5, judgment is performed on k, when k is equal to n, initialization is performed on the n-order model sequence probability at the moment k, an estimated value of the n-order model sequence state and corresponding covariance when k is equal to n; 6, an interactive multi-model filtering algorithm is performed in the state when k is larger than or equal to 3 and smaller than or equal to n; 7, when k is larger than n, generalized high-order interactive multi-model filtering is performed. The method is used for the field of maneuvering target tracking.

Description

technical field [0001] The invention relates to a high-order interactive multi-model filtering method based on a mixed transition distribution. Background technique [0002] In the model uncertainty problem of target tracking, multi-model filtering algorithm is often used to solve it. Among them, the classic algorithm interaction is proposed in H.A.P.Blom, Y.Bar-Shalom. "The interacting multiple model algorithm for systemswith Markovian switching coefficients," IEEE Transactions on Automatic Control, vol.33(8), pp.780-783, 1988 Multi-model filtering algorithm (IMM). Although the algorithm can adaptively identify the current model, its accuracy is not very high. [0003] In P. Suchomski, "High-order interacting multiple-model estimation for hybrid systems with Markovian switching parameters," International Journal of Systems Science, vol.32(5), pp.669-679, 2001 proposed a generalized high-order interactive multiple Model Filtering Method (IMMn), which utilizes sequences of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/15G06F17/16
CPCG06F17/15G06F17/16
Inventor 周共健叶晓平许荣庆吴立刚
Owner HARBIN INST OF TECH
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