Range resolution and sampling frequency trade-off for GPS passive radar

doi:10.23919/JSEE.2022.000004

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (1): 28-37.doi: 10.23919/JSEE.2022.000004

• ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

Range resolution and sampling frequency trade-off for GPS passive radar

Zhuxian ZHANG^1,²(), Yu ZHENG²(), Linhua ZHENG^1,*(), Peidong ZHU²()

¹ College of Electronic Science, National University of Defense Technology, Changsha 410073, China
² College of Electronic Communication and Electrical Engineering, Changsha University, Changsha 410022, China

Received:2020-12-15 Accepted:2021-11-29 Online:2022-01-18 Published:2022-02-22
Contact: Linhua ZHENG E-mail:zxzhang@ccsu.edu.cn;y_zheng170@sina.com;lhzheng131@sohu.com;pdzhu@nudt.edu.cn
About author:|ZHANG Zhuxian was born in 1984. She received her M.S. degree in telecommunication from Melbourne University in 2009. She is now a Ph.D. candidate in National University of Defense Technology. Her main research in GNSS passive radar sensing include static and dynamic object detection. E-mail: zxzhang@ccsu.edu.cn||ZHENG Yu was born in 1989. He received his B.E. degree and M.S. degree from Xiangtan University, China and University of Alberta, Canada in 2012 and 2014, respectively. In 2015, he joined the Department of Land Surveying and Geo-informatics, Hong Kong Polytechnic University as a Ph.D. student. He received his Ph.D. degree in 2018. Now he is a lecturer in the College of Electronic Communication and Electrical Engineering, Changsha University. His major research interests including environmental sensing using GNSS signals as source of opportunity and indoor location and surveillance using commercial devices. E-mail: y_zheng170@sina.com||ZHENG Linhua was born in 1961. He received his Ph.D. degree from the College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China. Currently he is a full professor with the same institution. He has published eight monographs and textbooks and over 100 technical papers. His research interests are wireless communication system, satellite communication, and radar signal processing. E-mail: lhzheng131@sohu.com||ZHU Peidong was born in 1971. He received his Ph.D. degree in the College of Computer Science and Technology, National University of Defense Technology, Changsha, China in 1999. After that, he has been a faculty member in the same institution from lecturer to full professor. He was a visiting chair professor in Saint Xavier University from 2008 to 2009. Now he is a full professor and departmental head for the College of Electronic Communication and Electrical Engineering, Changsha University, Changsha, China. His main research interests are wireless communication networks and security assessment for radar network. E-mail: pdzhu@nudt.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (42001297), the Research Foundation of Education Department of Hunan Province (19B061) and the National Natural Science Foundation of Hunan Province (2021JJ40631)

Abstract

Abstract:

In a global positioning system (GPS) passive radar, a high resolution requires a high sampling frequency, which increases the computational load. Balancing the computational load and the range resolution is challenging. This paper presents a method to trade off the range resolution and the computational load by experimentally determining the optimal sampling frequency through an analysis of multiple sets of GPS satellite data at different sampling frequencies. The test data are used to construct a range resolution-sampling frequency trade-off model using least-squares estimation. The theoretical analysis shows that the experimental data are the best fit using smoothing and nth-order derivative splines. Using field GPS C/A code signal-based GPS radar, the trade-off between the optimal sampling frequency is determined to be in the 20 461.25–24 553.5 kHz range, which supports a resolution of 43–48 m. Compared with the conventional method, the CPU time is reduced by approximately 50%.

Key words: global positioning system (GPS) radar, range resolution, sampling frequency, trade-off

Zhuxian ZHANG, Yu ZHENG, Linhua ZHENG, Peidong ZHU. Range resolution and sampling frequency trade-off for GPS passive radar[J]. Journal of Systems Engineering and Electronics, 2022, 33(1): 28-37.

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References 33

1	FRNTI P, MARIESCU-ISTODOR R. Averaging GPS segments competition 2019. Pattern Recognition, 2021, 112: 107730.
2	ZHUANG X S, WANG L Moving target imaging method for passive radar applicable to non-cooperative illumination source. Systems Engineering and Electronics, 2015, 37 (3): 560- 565.
3	BALASUNRAMANIAM R, RUF C. Characterization of rain impact on L-band GNSS-R ocean surface measurements. Remote Sensing of Environment, 2020, 239: 111607.
4	GEREMIA-NIEVINSKI F, HOBIGER T, HAAS R, et al SNR-based GNSS reflectometry for coastal sea-level altimetry: results from the first IAG inter-comparison campaign. Journal of Geodesy, 2020, 94 (8): 70. doi: 10.1007/s00190-020-01387-3
5	PAN Y L, REN C, LIANG Y J, et al Inversion of surface vegetation water content based on GNSS-IR and MODIS data fusion. Satellite Navigation, 2020, 1 (1): 21. doi: 10.1186/s43020-020-00021-z
6	YUEH S H, SHAH R, CHAUBELL M J, et al. A semiempirical modeling of soil moisture, vegetation, and surface roughness impact on CYGNSS reflectometry data. IEEE Trans. on Geoscience and Remote Sensing, 2020. DOI: 10.1109/TGRS.2020.3035989.
7	ZHANG Z Y, GUO F, ZHANG X H. Triple-frequency multi-GNSS reflectometry snow depth retrieval by using clustering and normalization algorithm to compensate terrain variation. GPS Solutions, 2020, 24(2): 52.
8	MUNOZMARTIN J F, PEREZ A, CAMPS A, et al. Snow and ice thickness retrievals using GNSS-R: preliminary results of the MOSAiC experiment. Remote Sensing, 2020, 12(24): 4038.
9	STEINER L, MEINDL M, MARTY C, et al Impact of GPS processing on the estimation of snow water equivalent using refracted GPS signals. IEEE Trans. on Geoscience and Remote Sensing, 2020, 58 (1): 123- 135. doi: 10.1109/TGRS.2019.2934016
10	CAMPS A, MUNOZ-MARTIN J F Analytical computation of the spatial resolution in GNSS-R and experimental validation at L1 and L5. Remote Sensing, 2020, 12 (23): 3910. doi: 10.3390/rs12233910
11	ZHENG Y, YANG Y, CHEN W. A novel range compression algorithm for resolution enhancement in GNSS-SARs. Sensors, 2017, 17(7): 1496.
12	CARTWRIGHT J, BANKS C J, SROKOSZ M Improved GNSS-R bi-static altimetry and independent digital elevation models of Greenland and Antarctica from TechDemoSat-1. The Cryosphere, 2020, 14 (6): 1909- 1917. doi: 10.5194/tc-14-1909-2020
13	MA H, ANTONIOU M, PASTINA D, et al Maritime moving target indication using passive GNSS-Based bistatic radar. IEEE Trans. on Aerospace and Electronic Systems, 2018, 54 (1): 115- 130. doi: 10.1109/TAES.2017.2739900
14	ZHENG Y, YANG Y, CHEN W New imaging algorithm for range resolution improvement in passive Global Navigation Satellite System-based synthetic aperture radar. IET Radar, Sonar & Navigation, 2019, 13 (12): 2166- 2173.
15	LONG T, ZENG T, HU C, et al High resolution radar real-time signal and information processing. China Communications, 2019, 16 (2): 105- 133.
16	TZADOK A, VALDES-GARCIA A, PEPELJUGOSKI P, et al AI-driven event recognition with a real-time 3D 60-GHz radar system. Proc. of the IEEE/MTT-S International Microwave Symposium, 2020, 795- 798.
17	WANG H, ZENG Z, JIANG M, et al Study on motion compensation method for W-band UAV MISAR real-time imaging. Proc. of the 21st International Radar Symposium, 2020, 143- 147.
18	DEMIR B, ERGUNAY S, NURLU G, et al Real-time high-resolution omnidirectional imaging platform for drone detection and tracking. Journal of Real-Time Image Processing, 2020, 17, 1625- 1635.
19	BI H, BI G A, ZHANG B C, et al From theory to application:real-time sparse SAR imaging. IEEE Trans. on Geoscience and Remote Sensing, 2020, 58 (4): 2928- 2936. doi: 10.1109/TGRS.2019.2958067
20	TAJDINI M M, MORGENTHALER A W, RAPPAPORT C M Multiview synthetic aperture ground-penetrating radar detection in rough terrain environment: a real-time 3-D forward model. IEEE Trans. on Geoscience and Remote Sensing, 2020, 58 (5): 3400- 3410. doi: 10.1109/TGRS.2019.2954776
21	SALEHI-BARZEGAR A, CHELDAVI A, NAYYERI V, et al A fast diffraction tomography algorithm for 3-D through-the-wall radar imaging using nonuniform fast fourier transform. IEEE Geoscience and Remote Sensing Letters, 2020, 19, 3504805. doi: 10.1109/LGRS
22	WANG Y Z, JIANG Z Y, LI Y D, et al. RODNet: a real-time radar object detection network cross-supervised by camera-radar fused object 3D localization. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(4): 954−967.
23	ZHOU X, WANG P, CHEN J, et al. A modified radon fourier transform for GNSS-Based bistatic radar target detection. IEEE Geoscience and Remote Sensing Letters, 2020, 19: 3501805.
24	WANG S, BAO Q L Single target tracking for noncooperative bistatic radar with unknown signal illumination. Signal Processing, 2021, 183, 107991. doi: 10.1016/j.sigpro.2021.107991
25	ZHENG Y, YANG Y, CHEN W. Object detectability enhancement under weak signals for passive GNSS-based SAR. 2019, 13(8): 1549−1557.
26	SOMMER A. Backprojection autofocus of large ships with arbitrary motion for synthetic aperture radar. Berlin: University of Hanover, 2020.
27	REN K, BURKHOLDER R J. A 3-D novel fast back projection imaging algorithm for stratified media based on near-field monostatic and bistatic SAR. IEEE Trans. on Antennas and Propagation, 2020, 69(4): 2326−2335.
28	GAIBEL A, BOAG A. Back projection imaging of moving objects. IEEE Trans. on Antennas and Propagation, 2020. DOI: 1-1.10.1109/TAP.2020.3045500.
29	ZENG Z F. Passive bistatic sar with GNSS transmitter and a stationary receiver. Britain: University of Birmingham, 2013.
30	TAKANE Y, YOUNG F W, LEEUW J D Nonmetric individual differences multidimensional scaling: an alternating least squares method with optimal scaling features. Psychometrika, 1977, 42 (1): 7- 67. doi: 10.1007/BF02293745
31	QIU S, LI X M, HUANG Y D, et al New algorithm of response curve for fitting HDR image. International Journal of Pattern Recognition and Artificial Intelligence, 2020, 34 (1): 2054001. doi: 10.1142/S0218001420540014
32	ZHANG Y, WANG R, LI S Z, et al Temperature sensor denoising algorithm based on curve fitting and compound kalman filtering. Sensors, 2020, 20 (7): 1959. doi: 10.3390/s20071959
33	WANG J, LU Y, YE L, et al Efficient analysis-suitable T-spline fitting for freeform surface reconstruction and intelligent sampling. Precision Engineering, 2020, 66, 417- 428. doi: 10.1016/j.precisioneng.2020.08.008

Imaging step	Times of multiplication	Times of addition/subtraction	Total
Range correlation operator	M×M	M(M ? 1)	M(2M ? 1)
Secondary order differentiation operator	0	2M	2M
Generating reply factor in carrier phase recovery	0	M	M
Recover process in carrier phase recovery	0	M	M
Total operations	?	?	M(2M ? 1) + 4M

Sampling frequency/kHz	Number of samples	Resolution/m
4 092.25	4	293.24
8 184.50	6	219.93
16 369.00	4	73.31
24 553.50	4	48.87
32 738.00	6	54.98
40 922.50	6	43.98
49 107.00	7	42.76
65 476.00	10	45.82

Sampling frequency/kHz	Number of samples	Resolution/m
8 184.50	5	183.27
16 369.00	4	73.31
18 005.90	3	49.98
20 461.25	3	43.98
24 553.50	4	48.87
32 738.00	5	45.82

[1]	Lan LAN, Guisheng LIAO, Jingwei XU, Hanbing WANG. Multi-dimensional ambiguity function for subarray-based space-time coding radar [J]. Journal of Systems Engineering and Electronics, 2019, 30(5): 886-896.
[2]	Ruiwen ZHANG, Bifeng SONG, Yang PEI, Qijia YUN. Improved method for subsystems performance trade-off in system-of-systems oriented design of UAV swarms [J]. Journal of Systems Engineering and Electronics, 2019, 30(4): 720-737.
[3]	Feng J univen. Dual worth trade-off method and its application for solving multiple criteria decision [J]. Journal of Systems Engineering and Electronics, 2006, 17(3): 554-558.

Range resolution and sampling frequency trade-off for GPS passive radar

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