Detection of Low-observable Maneuvering Target

Detection of Low-observable Maneuvering Target Using High-order Generalized Lv’s Distribution

Xiaolong Chen, Xiuyou Li, Yunlong Dong, Yong Huang, Jian Guan, You He Marine Target Detection Research Group, Naval Aeronautical and Astronautical University, Yantai, Shandong P.R. China Email: [email protected]

Overview 1. Introduction Difficulty of low-observable maneuvering target detection Research situation Signal model for long-time observation

2. Principle of HGLVD-based detection method Definition Long-time coherent integration Detection algorithm

3. Experiments (application of marine target detection) Micro-Doppler signature Experiments with CSIR data

4. Conclusions

Marine Target Detection Research Group, NAAU

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4. Conclusions


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1.1 Difficulty 1）Low-observable target detection

Small size

Stealth

Far-range

+

Complex environment

Lowobservable target 1

High-speed or highly mobile

Amplitude Normalized Amplitude

浮海浮海浮浮

Target

0.8

0.6

0.4

0.2

0

100

200

300

400

500

！Effective solution: Clutter suppression; Accumulate target’s energy to improve SCR/SNR. Marine Target Detection Research Group, NAAU

600

Samples Range

4

700

800

900

1000

Weak signal 2016-04-27

1.1 Difficulty 2）Clutter Clutter is one of the most important factors for low-observable target detection. For example, strong sea clutter (sea spikes) usually show nonhomogeneous, nonstationary, and time-varying properties, which would degrade the detection performance of radar detectors. Target Sea clutter Target Sea clutter

14

Sea spikes

Frequency domain 幅幅

Time domain

海海海 Doppler spread 海海海(无海海海)

12 10 8 6 4 2 -100


5

-50

0 50 频频 (Hz)

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100

150

1.1 Difficulty

Phased array radar, MIMO radar Multiple beams controlled by DBF technology Electronic beam scanning Cover wider space arbitrarily The dwell time is enough for long-time integration


With the development of digital phased array technology, it is possible to detect low-observable target via longtime integration, which is an effective way to improve SCR.

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1.2 Research situation Across range unit (ARU): range migration

—Both amplitude and Doppler information

Envelope correlation；

Poor performance

Keystone transform (KT)；

in low SNR/SCR

Radon-Fourier transform (RFT)

environment； Doppler ambiguity；

Frequency

Coherent integration technique

Problems:

DFM

Cannot correct

ARU

range curvature

Range

Doppler frequency migration (DFM): Chirp-Fourier transform；

Limited pulses for

Chirplet transform；

integration

Fractional Fourier transform (FRFT)

We need a method to deal with ARU, range curvature, and DFM effects for weak maneuvering target. Marine Target Detection Research Group, NAAU

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1.2 Research situation Long-time coherent integration

Radon-Fourier transform (RFT) ： Generalized RFT (GRFT) …….


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1.2 Research situation Radon-fractional Fourier transform (RFRFT)、Radon-Linear Canonical Transform (RLCT) Radon-fractional ambiguity function (RFRAF)、Radon-Linear Canonical ambiguity function (RLCAF)

MTD: SMTD = ∫ sPC (t , tm )exp( − j2πf dtm )dtm

RFT: SRFT = ∫ sPC [ 2(r0 − v0tm ) / c, tm ] exp( − j2πf dtm )dtm

α=π/2

α=π/2 FRFT: S RFRFT = ∫ sPC (t , tm ) Kα (tm , u )dtm

2 RFRFT: SRFRFT = ∫ sPC  2(r0 − v0tm − astm / 2) / c, tm  Kα (tm , u )dtm

One rangebin


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1.3 Signal model for long-time observation After demodulation and pulse compression Across range unit (ARU) effect

sPC ( t , tm ) = Ar sinc  B ( t − τ ) exp ( − j2πf cτ )

Fast time

Slow-time

τ = 2rs (tm ) / c

！The target’s envelope has been shifted away from its original position due to its motion.

For a maneuvering target, its Doppler can be approximated as 2 drs (tm )  rs → ( r0 , v0 , as , tm )  f 0 + µs tm fd = ,  , fd =  2 ( , , , , ) r → r v a g t λ dtm  f 0 + µs tm + g s tm / 2 s 0 0 s s m

Doppler frequency migration (DFM)

！The acceleration and jerk will have an impact on the range curvature and Doppler spread. 200

150

Chen Xiaolong, et al. Maneuvering target detection via Radon-fractional Fourier transform-based long-time coherent integration. IEEE TSP, 2014, 62(4): 939-953.

Doppler 海海海海

100

Chen Xiaolong, et al. Radon-fractional ambiguity functionbased detection method of low-observable maneuvering target. IEEE TAES, 2015, 51(2): 815-833.

50

Clutter and noise 海海海海海海 0

-50

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Chen Xiaolong, et al. Radon-linear canonical ambiguity function-based detection and estimation method for marine target with micromotion. IEEE TGRS, 2015, 53(4): 2225-2240.

t (s)


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1.3 Signal model for long-time observation After demodulation and pulse compression Across range unit (ARU) effect

sPC ( t , tm ) = Ar sinc  B ( t − τ ) exp ( − j2πf cτ )

Fast time

Slow-time

τ = 2rs (tm ) / c

！The target’s envelope has been shifted away from its original position due to its motion.

For a maneuvering target, its Doppler can be approximated as Doppler frequency migration (DFM)

2 drs (tm )  rs → ( r0 , v0 , as , tm )  f 0 + µs tm fd = ,  , fd =  2 ( , , , , ) r → r v a g t λ dtm  f 0 + µs tm + g s tm / 2 s 0 0 s s m

！The acceleration and jerk will have an impact on the range curvature and Doppler spread.

In this paper, a novel long-time coherent integration method (High-order Generalized Lv’s Distribution, HGLVD) is proposed to achieve better detection performance without requiring more computational cost. Marine Target Detection Research Group, NAAU

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4. Conclusions


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2.1 Definition of HGLVD Time delay Slow-time High-order motion τ τ     Pf (tm , rs ) = f  tm + 0 , rs  f *  tm − 0 , rs  2   2  

Phase differentiation (PD) calculation τ +b  * τ +b   R f (tm ,τ , rs ) = f  tm + , rs  f  tm − , rs  2 2    

Radon instantaneous auto-correlation function (RIAF) 

t



n ,τ , rs  Scaling processing S  R f (tm ,τ , rs )  = R f   q(τ + b) 

2-D RFT operator Marine Target Detection Research Group, NAAU

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2.2 Long-time coherent integration f (tm ) = exp  j ( a0 + a1tm + a2 tm2 + a3tm3 ) τ τ     Pf (tm , rs ) = f  tm + 0 , rs  f *  tm − 0 , rs  2   2  

Phase differentiation (PD) calculation

Pf (tm , rs ) = exp  j ( a1τ 0 + a3τ 03 / 4 + 2a2τ 0tm + 3a3τ 0tm2 ) 

τ +b  * τ +b   R f (tm ,τ , rs ) = f  tm + , rs  f  tm − , rs  2 2    

Radon instantaneous auto-correlation function (RIAF) 

t

R f [τ , Pf (tm , rs )] = exp [2 ja2τ 0 (τ + b) + 6 ja3τ 0 (τ + b)tm ]



n S  R f (tm ,τ , rs )  = R f  ,τ , rs  q ( + b) τ  

Scaling processing 2-D RFT operator

R f (tn ,τ , rs ) = exp [ 2 ja2τ 0 (τ + b) + j6a3τ 0tn / q ]

+∞

H R ( g , a, rs ) = Rτ ( Rtn ) = ∫−∞ =e

The signal will appear as a peak in the (a,g) domain Marine Target Detection Research Group, NAAU

∫

+∞

−∞

R f (tn ,τ , rs )e − j(g + a ) tn dtn dτ

δ ( a − 2a2τ 0 ) δ ( g − 6τ 0 a3 / q )

j2 a2τ 0 b

    6 2 ( a, g ) =  2a2τ 0 , a3τ 0  =  2πµτ 0 , kτ 0  q q     14

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2.3 Detection algorithm Radar returns after demodulation and pulse compression

Phase differentiation (PD) calculation Parameters initialization

Radon instantaneous auto-correlation function (RIAF)

HGLVD-based long-time coherent integration

Scaling processing 2-D RFT operator

Form the detection statistic and carry out the CFAR detection

Motion Parameters estimation


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4. Conclusions


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3.1 Micro-Doppler signature Under high oceanic conditions, or due to the pushing and control effects caused by propeller, engine, and rudder, the attitude of target may vary with the fluctuation of sea surface, which induces the effect of power modulation on radar echo. The Doppler exhibits time-varying and nonstationary properties, which is periodically frequency modulated.


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3.1 Micro-Doppler signature

① Chen Xiaolong, et al. Effective coherent integration method for marine target with micromotion via phase differentiation and radon-Lvs distribution. IET RSN (special issue: m-D), 2015, 9(9): 1284-1295.

② Chen Xiaolong, et al. Sea clutter suppression and micromotion marine target detection via Radonlinear canonical ambiguity function. IET RSN, 2015, 9(6): 622-631.

③ Chen Xiaolong, et al. Radon-linear canonical ambiguity function-based detection and estimation method for marine target with micromotion. IEEE TGRS, 2015, 53(4): 2225-2240.

④ Chen Xiaolong, et al. Detection of a low observable sea-surface target with micromotion via the Radon-linear canonical transform. IEEE GRSL, 2014, 11(7): 1225-1229. Marine Target Detection Research Group, NAAU

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3.2 Experiments with CSIR data The measurement trial was conducted with the Fynmeet dynamic RCS measurement facility at the over-berg test range (OTB) in 2006. Sea clutter at grazing angles 0.501– 0.56° were recorded. The TFA17_014 dataset was chosen and the cooperative marine target named as WaveRider rigid inflatable boat (RIB) was deployed for the purpose of recording reflectivity measurement dataset.

Experiments Configurations


Plan overview of deployment site of the Fynmeet radar at OTB 19

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3.2 Experiments with CSIR data

Range profiles of sea clutter with the WaveRider RIB

High Doppler resolution spectrogram of targetGroup, range bin 18 Marine Target Detection Research NAAU

Range migration and GPS trajectory of the target

High Doppler resolution spectrogram of target range 20 bin 20 2016-04-27

3.2 Experiments with CSIR data 1

1

RFT GRFT

Target energy

0.8

0.8

0.7

0.7

0.6 Sea clutter

0.5 0.4 0.3

0.5 0.4 0.3 0.2

0.1

0.1

-200

-100 0 100 Frequency (Hz)

200

300

RFT and GRFT outputs (TFA17_014 dataset, Tn=5.12 s, 2048 pulses)

s

0.6

0.2

-300

v (m/s)=-0.54 0 a (m/s2)=0.36 s g (m/s3)=0.046 s r (m)=-29.33

Target

0.9

PD-RLVD outpurs

Outputs

0.9

-200

-100

0 Chirp rate (Hz/s)

100

200

HGLVD outputs (TFA17_014 dataset, Tn=5.12 s, 2048 pulses)

HGLVD

Comparisons of detection and computational burden performances (Pfa = 10−4)


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Applications in radar signal processing

Wideband radar （High range resolution）

① ⑤ Ability to suppress clutter and noise

④

RFRFT RLCT RFRAF RLCAF HGLVD

Low observable target detection （Far range、stealth target）

②

③

High-speed or highly mobile target

Digital phases array radar

clutter

Xiaolong Chen, et al. An effective coherent integration method for marine target with micromotion via PD-RLVD, Accepted by IETGroup, RadarNAAU sonar and Navigation, special issue for micro-Doppler, 2015. Marine Target Detection Research 22 2016-04-27




4. Conclusions


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4. Conclusions A novel detection method for low-observable target is proposed via HGLVD, which not only achieves long-time coherent integration but also directly represents the target’s signal in the 2-D CRCC domain. It is proved that HGLVD has great application potentials for target with high-order motion without introducing any nonphysical attributes such as order or rotation angle. The performance is validated by experiments using Xband CSIR data. Future work will focus on the applications and its fast calculations.


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Xiaolong Chen Marine Target Detection Research Group, Naval Aeronautical and Astronautical University Email: [email protected]