Post on 18-Dec-2021
Study on the Feature Extraction Algorithm for
Efficient Ballistic Target Discrimination
In-Oh Choi1, Min-Kim1, Ki-Bong Kang1, Sang-Hong Park2, and Kyung-Tae Kim1
1Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang,
Gyeongbuk, Korea (South) 37673
2Department of Electronics Engineering, Pukyong National University, 45 Yongso-Ro, Nam-Gu, Busan, Korea (South)
48547
Abstract – In this paper, we propose a new feature
extraction algorithm based on the micro-Doppler (MD) phenomenon of ballistic target (BT), such as a warhead and
decoy, to more efficiently discriminate them. For this, we diagnose the MD phenomenon difference between the BTs using an electromagnetic prediction technique and cone-shaped
model. The measurements using a radar hardware and micro-motion device are utilized to demonstrate the performance of the proposed scheme.
Index Terms — micro-motion, warhead, decoy, occlusion effect, micro-Doppler effect.
1. Introduction
Since a ballistic missile releases a decoy to prevent the
intercept of a warhead from an antiballistic missile, it is
necessary to identify the ballistic target (BT), such as the
warhead and decoy, in efficient manner. For this purpose,
dynamic information (i.e. micro-motion) can be used
because the motion difference between the warhead and
decoy exists in the whole flight. Generally, the micro-motion
of the warhead consists of the precession and nutation to
control the attitude, while that of decoy is defined as the
wobble [1]. Then, these motions cause the effect of
modulation in a received radar signal from a target. V. Chen
defines this effect as micro-Doppler (MD) phenomenon, and
induces the mathematical relationship between micro-motion
dynamics and MD phenomenon [2]. Consequently, it is
evident that we must analyze the MD phenomenon to
identify the warhead from the decoys.
Recently, many studies on the MD phenomenon in
ballistic target discrimination (BTD) have been conducted
over the past few years [1]-[3]. Gao et al. demonstrates the
MD phenomenon of the cone-shaped target (i.e. warhead or
decoy) in two ways [1]: 1) Amplitude modulation; 2) Phase
modulation; Firstly, the amplitude modulation is defined as
the occlusion effect of radar cross section (RCS), which
represents that some effective scatterers are occluded at some
aspect angles, where “effective” means that their positions
are varied with the aspect angle of the target relative to the
radar. Next, the phase modulation is defined as MD effect,
which represents time-varying MD frequencies in phase term
of the received radar signal. Recently, for BTD, Persico et al.
utilizes three feature extraction techniques based on the
cadence velocity diagram (CVD) that is defined as the
Fourier transform of the joint time-frequency distribution [3].
However, these methods inevitably require an enough quality
image to classify the targets as well as computational burden
caused by complicated image processing. Here, reducing the
computational time is one of the main issues even for the BT
D application. Therefore, to achieve both computational cost
and reliability, we must establish a new feature extraction
framework.
In this paper, we propose an efficient BTD scheme using
a new three-dimensional (3D) feature vector and simple
nearest neighbor (NN) classifier. For this, we diagnose the
MD phenomenon using a computer-aided design (CAD)
model and electromagnetic prediction technique in the
virtual aircraft framework (VIRAF) software, which is a
commercial numerical electromagnetic solver. The
measurements using a radar hardware and micro-motion
device are utilized for BTD, and these results show the
effectiveness of the proposed scheme. The main contribution
of the proposed scheme is to reduce computational
complexity compared with the conventional methods in [3].
2. Analysis of MD phenomenon via VIRAF
To diagnose the MD phenomenon of the cone-shaped
warhead and decoy, we used the cone-shaped CAD model
and VIRAF signals obtained from two special cases: 1)
Warhead with precession and nutation using precession
frequency 5 Hz, precession angle c = 5˚, nutation frequency
5 Hz, and nutation angel amplitude 30˚; 2) Decoy with
wobbling using wobble frequency 5 Hz and wobble angel
amplitude w0 = 30˚;
Fig. 1(a) and (d) show that the RCSs of two special cases
appear with a period 0.2 and 0.1 s, respectively. Then, we
can see that these RCSs have quite different shapes. This is
due to the fact that both warhead and decoy have the same
model, but have different micro-motion parameters such as
c and w0.
Next, as shown in Fig. 1(b) and (e), spectrums of two
special cases have different bandwidths. This is because how
MD frequency is shifted by the micro-motion is highly
dependent on the difference between c and w0. Moreover,
spectrograms shown in Fig. 1(c) and (f) represent different
modulations by means of an image. These results lead us to
conclude that the difference between c and w0 is the main
key point to make the modulation difference between the
warhead and decoy.
[WeD2-5] 2018 International Symposium on Antennas and Propagation (ISAP 2018)October 23~26, 2018 / Paradise Hotel Busan, Busan, Korea
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3. Proposed method and experiment
In this paper, we propose a new 3D feature vector F = [F1,
F2, F3] through the analyzed results in Section 2. The F1 is
defined as a fundamental frequency estimated from the
spectrum analysis of the RCS. Then, the RCS can be easily
acquired from the amplitude information of the radar signal.
Next, the F2 is estimated as peak-to-peak of the RCS to
provide the difference of the amplitude modulation between
BTs. For the F3, we extract the 3dB MD bandwidth from the
spectrum of the radar signal to show how MD frequency is
shifted by target’s micro-motion. Finally, the discrimination
performances of F are evaluated using the NN classifier.
Here, the measured real data using an X-band radar and
micro-motion device was utilized (see Fig. 2). The
measurement system has transmitted a linear frequency
modulation waveform whose transmitting power,
observation time, sampling frequency, and bandwidth are
29.8 dBm, 4.096 s, 1 kHz, and 20 MHz, respectively. The
performance is investigated using the Probability of correct
Discrimination (Pd), i.e. the ratio of the number of correct
discriminations to the total number of test samples. To
analyze the effect of noise, we applied test set to noise under
different signal-to-noise ratio (SNR) ranging from 15 dB to
25 dB with 2 dB steps. Fig. 3 shows that the proposed
method are much more robust to the noise than these of the
conventional method in [3]. In particular, the computation
efficiency of the proposed algorithm has been improved by
about 40 times as compared to the conventional methods.
4. Conclusion
In this paper, a study on the feature extraction algorithm
has been conducted for BTD. Experimental results in view of
the noise sensitivity and computational efficiency, show that
the proposed method outperforms the conventional methods.
References
[1] G. Hongwei, X. Lianggui, W. Shuliang, and K. Yong, “Micro-Doppler
signature extraction from ballistic target with micro-motions,” IEEE
Trans. Aerosp. Electron. Syst., vol. 46, no. 4, pp. 1969-1982, Oct. 2010.
[2] V. Chen, “Micro-Doppler effect of micro-motion dynamics: A review,” Proceedings of SPIE, vol. 5102, pp. 240-249, 2003.
[3] A. R. Persico, C. Clemente, D. Gaglione, C. V. Ilioudis, J. Cao, L.
Pallotta, A. D. Maio, I. Proudler, and J. J. Soraghan, “On model, algorithms, and experiment for micro-Doppler-based recognition of
ballistic tergets,” IEEE Trans. Aerosp. Electron. Syst., vol. 53, no. 3, pp. 1088–1108, Feb. 2017.
Peak-to-peak : 8 dB
A period : 0.2 sec
Bandwidth : 400 Hz
(a) (b) (c)
Peak-to-peak : 33 dB
A period : 0.1 sec
Bandwidth : 960 Hz
(d) (e) (f)
Fig. 1. Representation of VIRAF signals obtained from a cone-shaped CAD model. The selected parameters for these signals
are: carrier frequency = 10 GHz, observation time = 0.5 s, sampling frequency = 2 kHz. (a) RCS of warhead. (b) Spectrum of
warhead. (c) Spectrogram of warhead. (d) RCS of decoy. (b) Spectrum of decoy. (c) Spectrogram of decoy.
Horn antenna (HH)
RF boxIF box
AWG
Radar-absorbing material
Target
(a) (b)
Fig. 2. Measurement setup. (a) Radar. (b) Mechanical device.
Fig. 3. Pd for various SNRs.
2018 International Symposium on Antennas and Propagation (ISAP 2018)October 23~26, 2018 / Paradise Hotel Busan, Busan, Korea
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