Weak Targets Detection and Estimation Based on Joint Use of Doppler and Micro-Doppler

SONG Zhiyong; XU Yuntao

doi:10.11999/JEIT230687

Volume 45 Issue 11

Nov. 2023

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SONG Zhiyong, XU Yuntao. Weak Targets Detection and Estimation Based on Joint Use of Doppler and Micro-Doppler[J]. Journal of Electronics & Information Technology, 2023, 45(11): 4083-4091. doi: 10.11999/JEIT230687

Citation:

SONG Zhiyong, XU Yuntao. Weak Targets Detection and Estimation Based on Joint Use of Doppler and Micro-Doppler[J]. Journal of Electronics & Information Technology, 2023, 45(11): 4083-4091. doi: 10.11999/JEIT230687

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Weak Targets Detection and Estimation Based on Joint Use of Doppler and Micro-Doppler

doi: 10.11999/JEIT230687 cstr: 32379.14.JEIT230687

SONG Zhiyong,
XU Yuntao^,

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds: The National Natural Science Foundation of China(61401475)

Received Date: 2023-07-12
Rev Recd Date: 2023-10-09

Available Online: 2023-10-14

Publish Date: 2023-11-28

Abstract

Abstract

In recent years, low-altitude slow and small targets, such as Unmanned Aerial Vehicles (UAVs), have posed a great challenge to the management of existing low-altitude airspace. These targets have low echo Signal Noise Ratio (SNR) due to their low flight altitude, slow flight speed and small Radar Cross Section (RCS), which result in low detection probability and inaccurate parameter estimation by traditional detection and estimation methods based on Doppler information of target. In addition to the Doppler information generated by the radial motion of the target, the micro-Doppler information generated by the micro-motion parts of the target can also be used for the detection and estimation of low-altitude slow and small targets like UAVs, which is expected to improve the SNR of the target by aggregating the energy dispersed in multiple Doppler cells due to the micro-motion. In this paper, a joint Doppler and micro-Doppler detection and estimation method based on the Cardinality Balanced Multi-target Multi-Bernoulli (CBMeMBer) filter is proposed, which makes full usage of the Doppler and micro-Doppler information contained in the echoes of UAV targets. By jointly modelling the Doppler and micro-Doppler information of UAV targets under the framework of Random Finite Sets (RFS), effective integration and fusion of Doppler and micro-Doppler information can be achieved. This leads to a better detection and estimation performance of low-altitude slow and small targets. Simulation experiments show that the method can achieve stable detection and state estimation of UAV targets, and the detection sensitivity is improved by 2 dB compared with the traditional detection method that only uses target Doppler information.
- Signal detection,
- Micro-doppler,
- Parameter estimation,
- Cardinality Balanced Multi-target Multi-Bernoulli (CBMeMBer) filter

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References(23)

References

[1]	CHEN V C. Analysis of radar micro-Doppler with time-frequency transform[C]. The Tenth IEEE Workshop on Statistical Signal and Array Processing (Cat. No. 00TH8496), Pocono Manor, USA, 2000: 463–466.
[2]	CHEN V C, LI F, HO S S, et al. Micro-Doppler effect in radar: Phenomenon, model, and simulation study[J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(1): 2–21. doi: 10.1109/TAES.2006.1603402
[3]	CHEN V C. The Micro-Doppler Effect in Radar[M]. 2nd ed. Norwood: Artech House, 2019.
[4]	秦晓雨, 邓彬, 董俊, 等. 太赫兹雷达直升机旋翼目标微动特性研究[J]. 太赫兹科学与电子信息学报, 2023, 21(3): 317–324. doi: 10.11805/TKYDA2022058 QIN Xiaoyu, DENG Bin, DONG Jun, et al. Micro-motion characteristics of helicopter blades based on THz radar[J]. Journal of Terahertz Science and Electronic Information Technology, 2023, 21(3): 317–324. doi: 10.11805/TKYDA2022058
[5]	冯维婷, 梁青. 闪烁现象下基于微动补偿的旋转叶片参数估计方法[J]. 信号处理, 2022, 38(12): 2617–2627. doi: 10.16798/j.issn.1003-0530.2022.12.016 FENG Weiting and LIANG Qing. A method for parameters estimation of rotating blades based on micro-motion compensation under flashing[J]. Journal of Signal Processing, 2022, 38(12): 2617–2627. doi: 10.16798/j.issn.1003-0530.2022.12.016
[6]	陈小龙, 陈唯实, 饶云华, 等. 飞鸟与无人机目标雷达探测与识别技术进展与展望[J]. 雷达学报, 2020, 9(5): 803–827. doi: 10.12000/JR20068 CHEN Xiaolong, CHEN Weishi, RAO Yunhua, et al. Progress and prospects of radar target detection and recognition technology for flying birds and unmanned aerial vehicles[J]. Journal of Radars, 2020, 9(5): 803–827. doi: 10.12000/JR20068
[7]	CHEN Shiqian, DONG Xingjian, XING Guanpei, et al. Separation of overlapped non-stationary signals by ridge path regrouping and intrinsic chirp component decomposition[J]. IEEE Sensors Journal, 2017, 17(18): 5994–6005. doi: 10.1109/JSEN.2017.2737467
[8]	BRUNI V, TARTAGLIONE M, and VITULANO D. A pde-based analysis of the spectrogram image for instantaneous frequency estimation[J]. Mathematics, 2021, 9(3): 247. doi: 10.3390/math9030247
[9]	HU Yue, TU Xiaotong, LI Fucai, et al. Adaptive instantaneous frequency ridge extraction based on target tracking for frequency-modulated signals[J]. ISA Transactions, 2022, 128: 665–674. doi: 10.1016/j.isatra.2021.10.011
[10]	LI Yifan, YANG Yaocheng, FENG Ke, et al. Automated and adaptive ridge extraction for rotating machinery fault detection[J]. IEEE/ASME Transactions on Mechatronics, 2023: 1–11.
[11]	赵彤璐, 廖桂生, 杨志伟. 基于短时迭代自适应-逆Radon变换的微多普勒提取方法[J]. 电子学报, 2016, 44(3): 505–513. doi: 10.3969/j.issn.0372-2112.2016.03.002 ZHAO Tonglu, LIAO Guisheng, and YANG Zhiwei. Micro-Doppler extraction based on short-time iterative adaptive approach and inverse radon transform[J].Acta Electronica Sinica, 2016, 44(3): 505–513. doi: 10.3969/j.issn.0372-2112.2016.03.002
[12]	HOUGH P V C. Method and means for recognizing complex patterns[P]. USA Patent, 3069654A, 1962-12-18.
[13]	DING Yipeng, LIU Runjin, SHE Yanlong, et al. Micro-Doppler trajectory estimation of human movers by Viterbi–Hough joint algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5113111. doi: 10.1109/TGRS.2022.3171208
[14]	MAHLER R P S. Statistical Multisource-Multitarget Information Fusion[M]. Boston: Artech House, 2007.
[15]	VO B T. Random finite sets in multi-object filtering[D]. [Ph. D. dissertation], The University of Western Australia, 2008.
[16]	VO B T, SEE C M, MA N, et al. Multi-sensor joint detection and tracking with the Bernoulli filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2): 1385–1402. doi: 10.1109/TAES.2012.6178069
[17]	VO B T, VO B N, and CANTONI A. The cardinality balanced multi-target multi-Bernoulli filter and its implementations[J]. IEEE Transactions on Signal Processing, 2009, 57(2): 409–423. doi: 10.1109/TSP.2008.2007924
[18]	VO B N, VO B T, and PHUNG D. Labeled random finite sets and the Bayes multi-target tracking filter[J]. IEEE Transactions on Signal Processing, 2014, 62(24): 6554–6567. doi: 10.1109/TSP.2014.2364014
[19]	VO B N, SINGH S, and DOUCET A. Sequential Monte Carlo methods for multitarget filtering with random finite sets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1224–1245. doi: 10.1109/TAES.2005.1561884
[20]	VO B N and MA W K. The Gaussian mixture probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4091–4104. doi: 10.1109/TSP.2006.881190
[21]	SHIM C, VO B T, VO B N, et al. Linear complexity Gibbs sampling for generalized labeled multi-Bernoulli filtering[J]. IEEE Transactions on Signal Processing, 2023, 71: 1981–1994. doi: 10.1109/TSP.2023.3277220
[22]	蔡飞. 雷达弱小目标检测与跟踪技术研究[D]. [博士论文], 国防科学技术大学, 2015. CAI Fei. Research on detection and tracking technologies for dim targets in radar[D]. [Ph. D. dissertation], National University of Defense Technology, 2015.
[23]	VO B N, VO B T, PHAM N T, et al. Joint detection and estimation of multiple objects from image observations[J]. IEEE Transactions on Signal Processing, 2010, 58(10): 5129–5141. doi: 10.1109/TSP.2010.2050482