广播卫星电视接收机对SMAP卫星L波段微波辐射计的射频干扰特征分析

王新新; 王祥; 范剑超; 王林; 孟庆辉; 魏恩泊

doi:10.11999/JEIT200593

广播卫星电视接收机对SMAP卫星L波段微波辐射计的射频干扰特征分析

doi: 10.11999/JEIT200593

王新新^{1, 2, 3, 4},
王祥²,
范剑超²,
王林²,
孟庆辉²,
魏恩泊^{1, 3, ,}

1.
中国科学院海洋研究所青岛 266071
2.
国家海洋环境监测中心大连 116023
3.
中国科学院海洋环流与波动重点实验室青岛 266071
4.
中国科学院大学北京 100049

基金项目: 国家自然科学基金(41806212)，国家重点研发计划(2016YFC1401000)

详细信息

作者简介:
王新新：男，1989年生，助理研究员，主要研究方向为海表面盐度卫星微波遥感、RFI检测及抑制

王祥：男，1984年生，助理研究员，主要研究方向为海洋生态环境遥感及应用研究等工作

范剑超：男，1985年生，副研究员，主要研究方向为海洋遥感目标信息智能提取

王林：男，1981年生，副研究员，主要研究方向为海洋光学、水色及

孟庆辉：女，1987年生，助理研究员，主要研究方向为海洋生态环境遥夜光遥感等

魏恩泊：男，1965年生，研究员，主要研究方向为海洋遥感机理(白冠微波辐射、盐度微波遥感理论)和复合介质物性理论等

通讯作者:
魏恩泊　ebwei@qdio.ac.cn

中图分类号: TP722.6
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- 被引次数: 0
出版历程
- 收稿日期: 2020-07-20
- 修回日期: 2021-03-23
- 网络出版日期: 2021-04-08
- 刊出日期: 2021-08-10

Analysis of RF Interference Characteristics of Broadcasting Satellite TV Receivers to SMAP Satellite L-Band Microwave Radiometer

Xinxin WANG^{1, 2, 3, 4},
Xiang WANG²,
Jianchao FAN²,
Lin WANG²,
Qinghui MENG²,
Enbo WEI^{1, 3
, ,}

1.
Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
2.
National Marine Environmental Monitoring Center, Dalian 116023, China
3.
Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
4.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: The National Natural Science Foundation of China (41806212), The National Key R&D Program of China (2016YFC1401000)

摘要

摘要: 该文通过融合SMAP卫星L波段交叉极化亮温，建立基于密度和强度空间分布特征的多重迭代聚类射频干扰(RFI)检测识别算法。分析并提取日本典型RFI源(广播卫星电视接收机)密度和累积强度的时空分布和变化特征。电视接收机作为典型的RFI源，主要分布在日本城市化水平和范围均相对较大的区域(条状或面状)，局部区域内分布点圆状RFI(可能为微波辐射基站)，导致局地化RFI累积强度具有很高的水平。同时，在日本其他区域也检测到独立分布的点圆状RFI，干扰强度和范围相对局限。2018年开始，日本RFI整体分布范围和强度能级呈下降趋势。典型RFI源特征分析对于我国建立RFI检测、识别及抑制模型具有重要意义。
- SMAP卫星 /
- L波段射频干扰 /
- 广播卫星电视接收机 /
- 时空特征分析
Abstract: Based on the fusion of SMAP satellite L-band cross-polarized brightness temperature, a multi-iteration clustering Radio Frequency Interference (RFI) detection and recognition algorithm based on its spatial distribution of density and intensity is established, and the spatial and temporal distribution and variation characteristics of the density and cumulative intensity of typical Japanese RFI sources (broadcast satellite TV receivers) are analyzed and extracted. As a typical RFI source, TV receivers are mainly distributed in areas with relatively large urbanization level and range (stripes or planes), with dotted RFI sources (possibly microwave radiation base stations) distributed in local areas, resulting in local areas with high RFI levels. In other areas where the urbanization level and scope are relatively small, the dot-round RFI sources are also detected, but the interference intensity and range are relatively limited. Beginning in 2018, the overall RFI distribution range and intensity level showed a downward trend. This work is of great significance to the establishment of RFI detection, identification and suppression models in China.
- SMAP satellite /
- L-band RFI /
- Broadcast satellite TV receivers /
- Analysis of space-time characteristics

HTML全文

图 1 家用电视室外接收设备基本内部结构及IF辐射发射导致的RFI机制示意图

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图 2 2015年至2020年研究区域RFI累积强度空间分布图

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图 3 2015年至2020年研究区域RFI疑似样本累积标记空间分布图

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图 4 NPP-VIIRS夜光遥感卫星观测数据年均分布图

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图 5 2015年至2020年RFI月均尺度累计强度统计直方图

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图 6 2015年至2020年研究区域RFI月均尺度累计强度分布图

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表 1 日本Ku波段BBS新增频道号与指派频率的对应关系及RF转换IF后对应的IF中心频率及频率范围^[20]

BBS 频道号	指派频率(MHz)	转换IF中心频率(MHz)	转换IF频率范围(MHz)
17	12034.36	1356.36	1339.11～1373.61
19	12072.72	1394.72	1377.47～1411.97
21	12111.08	1433.08	1415.83～1450.33
23	12149.44	1471.44	1454.19～1488.69
注：本振LO频率：10678 MHz；带宽BW：34.5 MHz

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表 2 2015年4月至2020年6月RFI检测识别结果统计表

年份	2015年	2016年	2017年	2018年	2019年	2020年	合计
有效数据天数(天)	274	366	365	365	332	181	1883
有效数据轨道数(个)	669	900	913	905	817	457	4661
平均每月数据个数(个)	74	75	76	75	74	76	75
检测为RFI疑似样本数(个)	111294	144865	147848	134979	114354	56745	710085
识别为符合RFI特征样本数(个)	102072	133798	135805	123103	104097	51759	650634
未识别为符合RFI特征样本数(个)	9222	11067	12043	11876	10257	4986	59451
疑似样本识别率P(%)	91.71	92.36	91.85	91.20	91.03	91.21	91.63

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参考文献(29)

[1]	DINNAT E P, LE VINE D M, BOUTIN J, et al. Satellite sea surface salinity: Evaluation of products and impact of retrieval algorithms[C]. 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019: 7936–7939. doi: 10.1109/IGARSS.2019.8899065.
[2]	FORE A, YUEH S, TANG Wenqing, et al. The JPL SMAP sea surface salinity algorithm[C]. 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019: 7920–7923. doi: 10.1109/IGARSS.2019.8898359.
[3]	OLIVA R, DAGANZO E, KERR Y H, et al. SMOS radio frequency interference scenario: Status and actions taken to improve the RFI environment in the 1400–1427-MHz passive band[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(5): 1427–1439. doi: 10.1109/TGRS.2012.2182775
[4]	LE VINE D M, DE MATTHAEIS P, RUF C S, et al. Aquarius RFI detection and mitigation algorithm: Assessment and examples[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8): 4574–4584. doi: 10.1109/TGRS.2013.2282595
[5]	MISRA S, JOHNSON J, AKSOY M, et al. SMAP RFI mitigation algorithm performance characterization using airborne high-rate direct-sampled SMAPVEX 2012 data[C]. 2013 IEEE International Geoscience and Remote Sensing Symposium, Melbourne, Australia, 2013: 41–44. doi: 10.1109/IGARSS.2013.6721087.
[6]	MOHAMMED P N, AKSOY M, PIEPMEIER J R, et al. SMAP L-band microwave radiometer: RFI mitigation prelaunch analysis and first year on-orbit observations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 6035–6047. doi: 10.1109/TGRS.2016.2580459
[7]	CAMPS A J, CORBELLA I, TORRES F, et al. RF interference analysis in aperture synthesis interferometric radiometers: Application to L-band MIRAS instrument[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(2): 942–950. doi: 10.1109/36.841976
[8]	CAMPS A, GOURRION J, TARONGI J M, et al. Radio-frequency interference detection and mitigation algorithms for synthetic aperture radiometers[J]. Algorithms, 2011, 4(3): 155–182. doi: 10.3390/a4030155
[9]	PARK J, JOHNSON J T, MAJUREC N, et al. Airborne L-Band radio frequency interference observations from the SMAPVEX08 campaign and associated flights[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(9): 3359–3370. doi: 10.1109/TGRS.2011.2107560
[10]	SOLDO Y, DE MATTHAEIS P, and LE VINE D M. L-band RFI in Japan[C].2016 Radio Frequency Interference (RFI), Socorro, USA, 2016: 111–114. doi: 10.1109/RFINT.2016.7833542.
[11]	LE VINE D M, JOHNSON J T, and PIEPMEIER J. RFI and remote sensing of the earth from space[C]. 2016 Radio Frequency Interference (RFI), Socorro, USA, 2016: 49–54. doi: 10.1109/RFINT.2016.7833530.
[12]	SOLDO Y, LE VINE D M, DE MATTHAEIS P, et al. L-Band RFI detected by SMOS and Aquarius[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(7): 4220–4235. doi: 10.1109/TGRS.2017.2690406
[13]	MIRANDA J J, VALL-LLOSSERA M, CAMPS A, et al. Sea state effect on the sea surface emissivity at L-band[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(10): 2307–2315. doi: 10.1109/TGRS.2003.817190
[14]	KERR Y H, WALDTEUFEL P, WIGNERON J P, et al. Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(8): 1729–1735. doi: 10.1109/36.942551
[15]	MISRA S and RUF C S. Detection of radio-frequency interference for the Aquarius radiometer[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(10): 3123–3128. doi: 10.1109/TGRS.2008.920371
[16]	PIEPMEIER J R, JOHNSON J T, MOHAMMED P N, et al. Radio-frequency interference mitigation for the soil moisture active passive microwave radiometer[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 761–775. doi: 10.1109/TGRS.2013.2281266
[17]	王新新, 王祥, 韩震, 等. 基于L波段Stokes参数遥感数据射频干扰检测及特性分析[J]. 电子与信息学报, 2015, 37(10): 2342–2348. doi: 10.11999/JEIT141577 WANG Xinxin, WANG Xiang, HAN Zhen, et al. Radio frequency interference detection and characteristic analysis based on the L band Stokes parameters remote sensing data[J]. Journal of Electronics &Information Technology, 2015, 37(10): 2342–2348. doi: 10.11999/JEIT141577
[18]	PENG Jinzheng, MISRA S, CHAN S, et al. SMAP radiometer brightness temperature calibration for the L1B_TB, L1C_TB (Version 4), and L1C_TB_E (Version 2) data products[EB/OL]. https://nsidc.org/sites/nsidc.org/files/technical-references/SMAP_L1_Assessment%20Report%2020180601_v9.pdf.2020.2.
[19]	QUEROL J, PEREZ A, and CAMPS A. A review of RFI mitigation techniques in microwave radiometry[J]. Remote Sensing, 2019, 11(24): 3042. doi: 10.3390/rs11243042
[20]	DAGANZO E, OLIVA R, RICHAUME P, et al. SMOS RFI experience in the 1400–1427 MHz passive band: Case of extended interference caused by broadcasting satellite home-TV receivers[C]. 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019: 4455–4458. doi: 10.1109/IGARSS.2019.8897873.
[21]	AKSOY M. Radio frequency interference characterization and detection in L-band microwave radiometry[D]. [Ph. D. dissertation], The Ohio State University, 2015.
[22]	PIEPMEIER J R, FOCARDI P, HORGAN K A, et al. SMAP L-band microwave radiometer: Instrument design and first year on orbit[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(4): 1954–1966. doi: 10.1109/TGRS.2016.2631978
[23]	SOLDO Y, LE VINE D M, BRINGER A, et al. Recent advances in Smap RFI processing[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018: 313–315. doi: 10.1109/IGARSS.2018.8518891.
[24]	SOLDO Y, LE VINE D M, BRINGER A, et al. Location of radio-frequency interference sources using the SMAP L-band radiometer[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(11): 6854–6866. doi: 10.1109/TGRS.2018.2844127
[25]	姜涛, 赵凯, 万祥坤. L波段微波辐射计周期脉冲式干扰时域检测方法研究[J]. 电子与信息学报, 2018, 40(7): 1539–1545. doi: 10.11999/JEIT170954 JIANG Tao, ZHAO Kai, and WAN Xiangkun. Research on detection methods to periodic pulsed interference for L band microwave radiometer in time domain[J]. Journal of Electronics &Information Technology, 2018, 40(7): 1539–1545. doi: 10.11999/JEIT170954
[26]	KRISTENSEN S S, BALLING J E, SKOU N, et al. RFI detection in SMOS data using 3^rd and 4^th Stokes parameters[C]. The 12th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad), Rome, Italy, 2012: 1–4. doi: 10.1109/MicroRad.2012.6185254.
[27]	WANG Xinxin, WANG Xiang, FAN Jianchao, et al. Automatic detection and identification of RFI sources for SMAP satellite polarized data based on IDL[C]. The 10th International Conference on Intelligent Control and Information Processing (ICICIP), Marrakesh, Morocco, 2019: 76–80. doi: 10.1109/ICICIP47338.2019.9012190.
[28]	马廷. 夜光遥感大数据视角下的中国城市化时空特征[J]. 地球信息科学学报, 2019, 21(1): 59–67. doi: 10.12082/dqxxkx.2019.180361 MA Ting. Spatiotemporal characteristics of urbanization in china from the perspective of remotely sensed big data of nighttime light[J]. Journal of Geo-Information Science, 2019, 21(1): 59–67. doi: 10.12082/dqxxkx.2019.180361
[29]	JIN Rong, LI Qingxia, and LIU Hang. A subspace algorithm to mitigate energy unknown RFI for synthetic aperture interferometric radiometer[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(1): 227–237. doi: 10.1109/TGRS.2019.2936005