Target tracking method based on pigeon intelligent optimization Kalman filtering parameters

Abstract

The invention relates to a target tracking method based on pigeon intelligent optimization Kalman filtering parameters. The method is implemented by the following steps: step 1, getting a real curve; step 2, getting an original curve; step 3, initializing pigeon-inspired optimization parameters; step 4, designing a cost function; step 5, performing optimization by use of a map compass operator; step 6, performing optimization by use of a landmark operator; and step 7, getting a result and verifying the result. By using the method, the parameters of a Kalman filter can be optimized better, the status value of a target at next moment can be predicted more quickly, and effective guarantee is provided for tracking a dynamic target.

Description

technical field

[0001] The invention relates to a target tracking method based on pigeon group intelligent optimization of Kalman filter parameters, which belongs to the technical field of automation. Background technique

[0002] In the modern intelligent video detection system, it is very necessary to track the dynamic target in real time. However, due to measurement noise or other factors, it will still affect the real-time tracking of dynamic targets to a certain extent. With the rapid development of modern computer technology and material science, the factors limiting real-time tracking are increasingly moving towards intelligent algorithms. In order to solve this problem, more and more people are constantly investing in the research of intelligent algorithms. Genetic algorithm, particle swarm algorithm, etc. have become more and more mature after years of development, and intelligent algorithm has gradually evolved into a very important subject. While people are stu...

Examples

Embodiment Construction

[0079] See figure 1 — Figure 5 , the following uses a specific practical example to verify that the pigeon group algorithm proposed by the present invention optimizes the parameters of the Kalman filter to achieve the purpose of tracking the target.

[0080] The specific steps to implement this example are as follows:

[0081] Step 1: Get the real curve

[0082] Obtain a specific curve as a real curve;

[0083] Step 2: Get the original curve

[0084] Add Gaussian white noise (noise) to the target curve, called the original curve;

[0085] Step 3: Initialize the parameters of the pigeon group algorithm

[0086] (1) Initialize the optimization parameter dimension D

[0087] The parameter to be optimized in this method is the measurement noise covariance R of the Kalman filter, which is a 2×2 matrix, so the pigeon group algorithm is used to find the target area in four dimensions, so D is 4.

[0088] (2) Initialize the parameters of the contraction and expansion coeffici...

technical field

[0001] The invention relates to a target tracking method based on pigeon group intelligent optimization of Kalman filter parameters, which belongs to the technical field of automation. Background technique

[0002] In the modern intelligent video detection system, it is very necessary to track the dynamic target in real time. However, due to measurement noise or other factors, it will still affect the real-time tracking of dynamic targets to a certain extent. With the rapid development of modern computer technology and material science, the factors limiting real-time tracking are increasingly moving towards intelligent algorithms. In order to solve this problem, more and more people are constantly investing in the research of intelligent algorithms. Genetic algorithm, particle swarm algorithm, etc. have become more and more mature after years of development, and intelligent algorithm has gradually evolved into a very important subject. While people are stu...

Examples

Embodiment Construction

[0079] See figure 1 — Figure 5 , the following uses a specific practical example to verify that the pigeon group algorithm proposed by the present invention optimizes the parameters of the Kalman filter to achieve the purpose of tracking the target.

[0080] The specific steps to implement this example are as follows:

[0081] Step 1: Get the real curve

[0082] Obtain a specific curve as a real curve;

[0083] Step 2: Get the original curve

[0084] Add Gaussian white noise (noise) to the target curve, called the original curve;

[0085] Step 3: Initialize the parameters of the pigeon group algorithm

[0086] (1) Initialize the optimization parameter dimension D

[0087] The parameter to be optimized in this method is the measurement noise covariance R of the Kalman filter, which is a 2×2 matrix, so the pigeon group algorithm is used to find the target area in four dimensions, so D is 4.

[0088] (2) Initialize the parameters of the contraction and expansion coeffici...

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