Normalized leakage LMS self-adaptive mobile target detector based on FRFT

A normalized and moving target technology, applied in the field of radar signal processing and detection, can solve the problems of limiting the application of FRFT domain moving target detection methods, the algorithm cannot effectively converge, and the increase of filter lag error, so as to suppress sea clutter , good discrimination ability, fast convergence effect

Active Publication Date: 2011-03-09
NAVAL AVIATION UNIV
View PDF3 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Only when the FRFT whose transformation order matches the signal parameters is used, the mean square error of the corresponding adaptive process can converge to its minimum value, while in other FRFT domains, the algorithm cannot effectively converge, while the traditional FRFT domain The method of performing two-dimensional peak search to determine the optimal transformation order is not only computationally intensive, but also the parameter estimation accuracy is not high
At the same time, the adaptive filter has a memory effect on the changing frequency, which leads to a significant increase in the lag error of the filter output and reduces the performance of the filter
The above problems limit the practical application of the FRFT domain moving target detection method

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
  • Normalized leakage LMS self-adaptive mobile target detector based on FRFT
  • Normalized leakage LMS self-adaptive mobile target detector based on FRFT
  • Normalized leakage LMS self-adaptive mobile target detector based on FRFT

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0020] The present invention will be described in further detail below in conjunction with the accompanying drawings. With reference to the accompanying drawings in the description, the specific implementation of the present invention is divided into the following steps:

[0021] (1) After the echo signal of the radar antenna is amplified and mixed, it is sent to the storage device 1 for preprocessing, so as to obtain the N-dimensional input signal vector x(n)=[x(n) formed by N sampling points at n time )=[(n-1),...,x(n-N+1)] T .

[0022] (2) The calculation device 2 receives the input signal vector x(n) of the storage device 1, and uses the FRFT decomposition algorithm proposed by H.M.Ozaktas et al. to complete the discrete fractional Fourier transform (DFRFT) under different transformation orders, based on the following formula

[0023] X p ( m 2 Δx ...

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 normalized leakage LMS self-adaptive mobile target detector based on FRFT, belonging to the technical field of radar signal processing and detecting. The existing statistics method and FRFT method are respectively applied to mobile target detection in sea clutter in the presence of the defects such as undesirable sea clutter inhibition, complex parameter evaluation and poor real-time. The invention combines the time domain LMS algorithm with the FRFT domain mobile target detection method, the kurtosis iterative computation is adopted for rapidly determining a best transform angle; an FRFT domain self-adaptive spectral line enhancer is constructed for improving the signal-to-noise ratio for inhibiting the sea clutter, the leakage factor is introduced into a weighting vector iterative formula for reducing the influence of memory effect to the spectral line enhancer, the step length is normalized in power for improving the convergence rate, and the amplitude value of output signal is regarded as the detection statistic. The detector can self-adaptively inhibit the sea clutter, and the target motion parameter evaluation is high in precision; and the detector is suitable for applying to the detection and filtration of LFM signal, and has popularization and application values.

Description

1. Technical field [0001] The invention belongs to the technical field of radar signal processing and detection, in particular to the moving target detection technology of sea detection radar. 2. Background technology [0002] Fast and robust weak moving target detection in the background of sea clutter is always a difficult problem in the field of radar signal processing, which is of great significance in both military and civilian applications. The radar cross section (Radar Cross Section, RCS) of a moving small ship is very small, and its echoes are often submerged in sea clutter and noise. The traditional method for detecting weak moving targets in sea clutter is to use sea clutter Sea clutter is regarded as a random process based on statistical theory, but its versatility is poor and the detection process is complicated. In the case of high sea conditions, the non-Gaussian characteristics of sea clutter make it difficult to accurately model sea clutter. difficulty. Th...

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): G01S7/41
Inventor 关键陈小龙何友郭海燕刘宁波
Owner NAVAL AVIATION UNIV
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