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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