论文研究-自适应MTI滤波器的优化_mti滤波器

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Int. J. Communications, Network and System Sciences, 2017, 10, 206-217

http://www.scirp.org/journal/ijcns

ISSN Online: 1913-3723

ISSN Print: 1913-3715

DOI: 10.4236/ijcns.2017.108B022

August 14, 2017

Optimization of Adaptive MTI Filter

Wenxu Zhang, Shudi Ma, Qiuying Du

College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

Abstract

Moving target indication (MTI) is an effective means for radar to find moving

targets in clutter environment. This paper introduces the basic principles

MTI, how to avoid the blind speed problem and the optimization of MTI fi

ter. Implementing the multi-

notch adaptive moving target indication (AMTI)

filter that designed by using the stagger code in varied cases, which is

based on

a feature vector method optimization.

Keywords

Adaptive Moving Target Indication (AMTI), Stagger Code, Feature

Vector

Method, Multi-Notch

1. Introduction

MTI band-stop filter as a “single channel”, followed by detection is relatively

simple. When the target speed is large and the repetition frequency is low, make

sure that there is no distance blur, through the “variable week” variable repeat

cycle or repeat and “time varying” [1]. Can overcome the blind speed problem,

the drawback is no improvement in noise. In general, the mess is not very strong,

the radar can handle a limited number of pulses, suitable for the use of repetitive

and time-varying weighted system. The adaptive has a variety of ways to achieve,

in which the performance is better “first order” and “second order”. The first-

order basic method is to use the interval-based velocity measurement and the

zero-point distribution method to determine the weighting parameters of the

clutter cancellation filter to obtain the filter whose notch is aligned with the cen-

ter of the clutter spectrum [2]. Its advantages are simpler, the disadvantage is

that it cannot be adaptive with the clutter spectrum, so sometimes the perfor-

mance is worse. The second-order basic method is to estimate the clutter cova-

riance matrix, and then use matrix inversion or feature decomposition feature

vector method to determine the filter weight coefficient.

This paper first analyzes the moving target indication (MTI), on this basis, the

How to cite this paper:

Zhang, W.X., Ma

S.D. and

Du, Q.Y. (2017)

Optimization of

Adaptive MTI Filter

Int. J. Communic

tions

Network and System Sciences

206

-217.

https://doi.org/10.4236/ijcns.2017.108B022

Received:

May 31, 2017

Accepted:

August 11, 2017

Published:

August 14, 2017

W. X. Zhang et al.

208

The double delay line canceller impulse response is:

( )

2 22

16 sin

H HH

ω ωω



= =





(4)

The adaptive moving target indication (AMTI) filter is usually composed of a

FIR filter with a horizontal structure. The output of the MTI filter is:

( ) ( ) ( )

Y n W X n wx n i

−

= = −

∑

(5)

where

is the weight vector and

( )

is the input signal vector. The fre-

quency response of this filter is:

( )

exp 2

H f w j fT

−

= −

∑

(6)

In the radar system, in order to avoid the occurrence of blind effects, usually

the use of “variable T” approach, that is, by regularly changing the radar launch

pulse period so that the frequency of blindness is greater than the target possible

Doppler frequency. Adaptive clutter suppression is compatible with parametric

techniques, meaning that the clutter suppression filter must be time-varying. For

the determined

value, the frequency characteristic of the MTI filter is de-

termined only by the weight vector, so the calculation of the weight vector is the

core of the MTI process, according to different design methods, the optimal

weight vector is generally different. In engineering practice, the improvement

factor is often used to measure the performance of MTI system. The improve-

ment factor of the MTI filter is defined as

( ) ( )

/ //

o ii

I SC SC=

. Obviously, the

greater the

, the better the effect of the system on clutter suppression. It has

been proved that the optimal weight vector of the MTI filter should be the ei-

genvector corresponding to the minimum eigenvalue of the covariance matrix of

the input clutter, in order to maximize the average improvement factor of the

MTI. At this point the improvement factor is

max min

1/I

[6].

2.1. Optimal Design of Filter

The so-called optimization design requires a set of optimal filter coefficients, to

maximize the improvement factor, a lot of design methods. In the case of the va-

riable T, the better methods are feature vector method, matching algorithm, ze-

ro-point allocation method and linear prediction method [7]. The feature vector

method is the solution that minimizes the clutter output power when the target

gain is constant. The zero-point assignment method is to set the frequency re-

sponse zero at the notch when designing the band-stop filter. The matching al-

gorithm and the linear prediction method are the solutions that minimize the

clutter output power when one of the elements of the weight vector is constant.

So the feature vector method has better performance [8].

The feature vector method is a clutter suppression method based on the

maximum improvement factor.

It is usually assumed that the clutter has a Gaussian power spectrum, the

spectral center is

, the spectral width is

, and the spectral density function

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