Maneuvering target tracking method with organic combination of Kalman filtering and empirical mode decomposition

A technology of empirical mode decomposition and maneuvering target tracking, applied in complex mathematical operations and other directions, it can solve problems such as inaccurate calculation, IMF losing physical meaning, and limiting EMD applications, achieving high tracking accuracy and overcoming boundary effects.

Active Publication Date: 2010-11-24
HARBIN INST OF TECH
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  • Summary
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When applying the EMD method, there is an unavoidable problem, that is, when using poles to fit the upper and lower envelopes, envelope distortion will appear near the two endpoints, resulting in inaccurate calculations. This phenomenon is called for "Boundary Effects"
Moreover, as the order of obtaining IMF increases, the number of iterations increases continuously, and the "boundary effect" will sp...

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  • Maneuvering target tracking method with organic combination of Kalman filtering and empirical mode decomposition
  • Maneuvering target tracking method with organic combination of Kalman filtering and empirical mode decomposition
  • Maneuvering target tracking method with organic combination of Kalman filtering and empirical mode decomposition

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specific Embodiment approach 1

[0014] Specific implementation mode one: the maneuvering target tracking method that the Kalman filtering of the present implementation mode and empirical mode decomposition organically combine, and its specific process is as follows:

[0015] Step 1. Obtain the measurement data sequence at the current sampling moment, and use the Kalman prediction equation to obtain the system state prediction data at the next sampling moment, and combine the system state prediction data at the next sampling moment with the current measurement data sequence Combination, to obtain the combined data sequence;

[0016] Step 2, a plurality of interpolation points are uniformly distributed between every adjacent two data in the combined data sequence obtained in step 1, and a filter sequence is generated;

[0017] Step 3, using the EMD method to decompose the filter sequence generated in step 2 to obtain an IMF containing noise, remove the IMF containing noise in the filter sequence, and then obta...

specific Embodiment approach 2

[0022] Specific embodiment two: This embodiment is a further description of the maneuvering target tracking method of the organic combination of Kalman filtering and empirical mode decomposition in embodiment one. The process of the content described in step one is as follows:

[0023] At the sampling time t(n), n=1, 2, ..., the filtering system obtains the current measurement data sequence, and the length of the measurement data sequence obtained at each sampling time is N;

[0024] Use {y(i), i=1, 2, ..., N} to represent the measurement data sequence at time t(n), where y(N) is the system state measurement data sampled at time t(n), and y (N-1) is the system state measurement data obtained by sampling at time t(n-1), ..., y(1) is the system state measurement data obtained by sampling at time t(n-N+1);

[0025] The Kalman prediction equation is:

[0026] x ^ ( n + 1 | n...

specific Embodiment approach 3

[0033] Specific implementation mode three: this implementation mode is a further description of the maneuvering target tracking method that organically combines the Kalman filter and empirical mode decomposition of the first or second implementation mode, and the specific process of the content described in step two is:

[0034] make Indicates the combined data sequence obtained in step 1, where y(1), y(2), ... y(N) is the current measurement data sequence obtained in step 1 {y(i), i=1, 2, ..., N} N pieces of system state measurement data, The system state prediction data at the next sampling moment obtained for step 1;

[0035] exist j interpolation points are uniformly distributed between each adjacent two data in , j is a positive integer, and a filter sequence {x(i), i=1, 2, ..., n×(j+1)+1} is generated .

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Abstract

The invention discloses a maneuvering target tracking method with organic combination of Kalman filtering and empirical mode decomposition, which relates to the field of maneuvering target filtering tracking and solves the divergence problem that boundary effect and Kalman filtering of high maneuvering target in the EMD method cannot be overcome. The method comprises the steps of: 1. acquiring measure data sequence of current sampling moment, acquiring system state predicating data of the next moment by means of a Kalman predication equation, and combining the data with the current sequence; 2, generating a filtering sequence by means of interpolation in the combined sequence; 3, decomposing the filtering sequence according to the EMD method, and removing noise-containing IMF to acquire a current filtering value; 4, displaying the filtering value as current filtering result; and 5, taking the filtering value as posterior estimate of the current state of the system, and acquiring calculative parameters at the next moment in combination with the measure data and Kalman equations, then returning to step 1 and taking the next moment as the current moment so as to realize maneuvering target tracking. The method according to the invention can be used in the field of maneuvering target filtering and tracking.

Description

technical field [0001] The invention relates to the problem of maneuvering target filtering and tracking, in particular to a maneuvering target tracking method in which Kalman filtering and empirical mode decomposition are organically combined. Background technique [0002] The target tracking filtering method has important practical significance and broad application background in many fields such as detection, recognition and tracking, among which the maneuvering target tracking filtering problem is one of the research hotspots. Through the unremitting efforts of many scholars, many related theories have been proposed or developed, such as Kalman theory and its extended methods, wavelet analysis, particle filter, multi-model probability estimation, etc. Among them, Kalman theory and its extended filtering methods are the most popular. Reception is also the most widely used, but like many other methods, there are still problems such as strong dependence on parameters and ea...

Claims

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Application Information

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IPC IPC(8): G06F17/14
Inventor 沈毅赵振昊张淼
Owner HARBIN INST OF TECH
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