Double-matrix target motion analysis method based on self-adaptive Kalman filtering

An adaptive Kalman and target motion analysis technology, applied to radio wave measurement systems, using re-radiation, measurement devices, etc., can solve problems such as divergence and inaccurate filtering, improve estimation accuracy, reduce convergence time, and improve performance Effect

Inactive Publication Date: 2019-09-10
THE 715TH RES INST OF CHINA SHIPBUILDING IND CORP
View PDF10 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the azimuth-only target motion analysis of dual arrays, and are usually uncertain or only approximately determined, which may lead to inaccurate filtering or even divergence

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Double-matrix target motion analysis method based on self-adaptive Kalman filtering
  • Double-matrix target motion analysis method based on self-adaptive Kalman filtering
  • Double-matrix target motion analysis method based on self-adaptive Kalman filtering

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0020] Embodiment: As shown in the accompanying drawing, this dual-array target motion analysis method based on adaptive Kalman filter mainly includes the following steps:

[0021] 1) Perform conventional beamforming on the synchronous acquisition signals of the dual arrays to obtain the target azimuth sequence θ 1 (k) and θ 2 (k), k=1,2,...,K;

[0022] 2) Respectively for the azimuth sequence θ 1 (k) and θ 2 (k) Perform outlier elimination and data smoothing to obtain the preprocessed orientation sequence θ p1 (k) and θ p2 (k), k=1,2,...,K;

[0023] 3) Set the state initial value X separately 0 , the initial value of state variance P 0 , the initial value Q of the state noise matrix 0 ;

[0024] 4) Calculate the variance of the difference sequence between the measured azimuth sequence and the preprocessed azimuth sequence, and set it as the initial value R of the observation noise covariance matrix 0 ;

[0025] 5) Adaptive Kalman filtering is performed on the prepr...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a double-matrix target motion analysis method based on self-adaptive Kalman filtering, and the method comprises the following steps: performing conventional beam forming on double-matrix acquired data, performing preprocessing such as wild value elimination, data smoothing and the like on an obtained azimuth measurement sequence, setting a covariance matrix initial value ofa filter by using the statistical variance of an azimuth sequence, and setting filter parameters such as a state initial value, a state variance initial value, a state noise matrix initial value andthe like respectively, wherein the initial value of an observation noise covariance matrix is set by measuring the variance of a difference sequence of the azimuth sequence and a preprocessing azimuthsequence; performing self-adaptive Kalman filtering on the azimuth measurement sequence; and outputting a target distance and a speed estimation value by the filter. The double-matrix target motion analysis method based on self-adaptive Kalman filtering can provide a basis for setting filter parameters, effectively inhibit the problem of filter divergence caused by uncertainty of a double-matrixobservation direction covariance matrix, improve the estimation precision, greatly reduce the convergence time and improve the double-matrix target motion analysis performance.

Description

technical field [0001] The invention relates to the field of underwater acoustic signal processing and passive detection of underwater acoustic targets, in particular to a dual array target motion analysis method based on adaptive Kalman filtering. Background technique [0002] Underwater target positioning technology is an important research content of underwater acoustic detection. At present, the commonly used target passive positioning technologies mainly include traditional three-subarray passive positioning, matching field processing, and target motion analysis (TMA: Target Motion Analysis). Azimuth-only TMA only uses the measured target azimuth information to realize the estimation of target motion parameters (distance, speed, heading, etc.). Since this method requires less target information, it has been widely researched and applied. For the single-array azimuth-only TMA, the receiving platform must perform an effective maneuver at least once to meet the observabili...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G01S11/14
CPCG01S11/14
Inventor 宋雪晶田玲爱刘福臣
Owner THE 715TH RES INST OF CHINA SHIPBUILDING IND CORP
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap