Moving target indication (MTI) is an effective means for radar to find moving targets in clutter environment. This paper introduces the basic principles of MTI, how to avoid the blind speed problem and the optimization of MTI filter. 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.
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” [
This paper first analyzes the moving target indication (MTI), on this basis, the MTI is optimized, and the appropriate filter coefficients are designed by the feature vector method, which can effectively suppress the clutter. And the use of stagger code design MTI filter to eliminate the impact of blind speed. For motion clutter, the spectral center is not at zero frequency, and is time-varying. In order to suppress such clutter, this paper adopts adaptive motion clutter suppression technique AMTI, and designs multi-notch AMTI filter [
The earliest MTI filter is a delay line canceller, is currently one of the most commonly used MTI filter. According to the different number of cancellation, but also divided into single delay line canceller, double delay line canceller and multi-delay line canceller [
Single delay line canceller as shown in
The impulse response of the counter is:
The power gain of the single delay line canceller is:
Double delay line canceller as shown in
The double delay line canceller impulse response is:
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:
where
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
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 variable T, the better methods are feature vector method, matching algorithm, zero-point allocation method and linear prediction method [
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
According to the Wiener filter theory, if the clutter is a stationary stochastic process, its power spectrum and autocorrelation function are Fourier transform pairs. Therefore, the clutter autocorrelation function
We obtain the clutter autocorrelation matrix A of N pulses
The target autocorrelation function is
Assume that the clutter data and the target data of the N pulse MTI input are respectively
Then the MTI output of the clutter power and signal power are
where
By
The characteristic equation of
where
In the eigenvalues of
In general, it is not possible to obtain a PRF that can meet the required ambiguous distance and Doppler coverage. Therefore, a method of stagger repetition frequency is proposed. Stagger repetition frequency is a measure that can be used to prevent blind influence [
If the radar uses N repetition frequencies, their repetition periods can be expressed as
If
The average repetition period of the radar is
Because
The coefficient of the MTI filter between the pulses is different for each pulse of the three pulse canceller, so it is a time-varying filter. If the radar uses three repetition frequencies
In the clutter region, the spectral center
First estimate the motion of the clutter spectrum center.
The radar suffers from narrowband clutter and noise that can be expressed as
Delay the signal after a PRI
The correlation function of
Therefore, the center frequency estimate of the clutter spectrum is obtained
After obtaining the center frequency of the clutter spectrum, the spectral width estimation is performed by the integral method.
Combined with the Gauss spectrum, there are Gaussian power spectra
According to the nature of Gaussian distribution, there are
Prior to the estimated spectrum as the center to both sides of the center
It is found that the power spectrum is the sum of their respective power spectra for the stagger clutter of multiple Gaussian spectra. The autocorrelation function should also have the sum of the corresponding multi-clutter components. Thus, we can derive the weight coefficients of two or more notch filters to design a multi-notch AMTI filter.
In
can be seen from the figure, the double delay line canceller has a deeper notch and a more flat passband response than a single delay line canceller.
In
In
corresponding notch, and avoid the loss of weak targets in one of them.
In
spectral width is 0.64 Hz, the meteorological clutter center frequency is 30 Hz, the spectral width is 1.4 Hz. It can be seen from the figure at 0 Hz and 30 Hz with a deeper notch, can inhibit the clutter.
In the process of receiving the echo signal by the radar, the presence of the clutter signal has been interfering with the detection and extraction of the useful signal, it is necessary to suppress clutter. The moving target indication (MTI) technique has a good ability to suppress static clutter, but it is powerless for dynamic clutter. The use of adaptive technology can effectively inhibit the dynamic
clutter. In this paper, we propose an algorithm for processing AMTI based on the maximum average improvement factor, and give the corresponding MATLAB simulation waveform. Especially with the development of DSP chip, the pro- cessing speed has been improved, which made this method very suitable for practical application.
This work is supported partly by National Natural Science Foundation of China under Grant No. 61301205 and No. 61571146, National Defense Based Science
Research Program under Grant No. JCKY2013604B001. This paper is funded by the International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation.
Zhang, W.X., Ma, S.D. and Du, Q.Y. (2017) Optimization of Adaptive MTI Filter. Int. J. Communications, Network and System Sciences, 10, 206-217. https://doi.org/10.4236/ijcns.2017.108B022