Remote Sensing

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23 pages, 4600 KiB

Open AccessArticle

Parasitic Surveillance Potentialities Based on a GEO-SAR Illuminator

by Fabrizio Santi, Giovanni Paolo Blasone, Debora Pastina, Fabiola Colone and Pierfrancesco Lombardo

Remote Sens. 2021, 13(23), 4817; https://doi.org/10.3390/rs13234817 - 27 Nov 2021

Cited by 6 | Viewed by 2662

Abstract

Synthetic aperture radar systems operating with satellites in geosynchronous orbits (GEO-SAR) can provide a permanent coverage of wide specific areas of the Earth’s surface. As well as for primary applications in remote sensing areas such as soil moisture and deformation monitoring, the wide [...] Read more.

Synthetic aperture radar systems operating with satellites in geosynchronous orbits (GEO-SAR) can provide a permanent coverage of wide specific areas of the Earth’s surface. As well as for primary applications in remote sensing areas such as soil moisture and deformation monitoring, the wide availability of the signal emitted by a GEO-SAR on a regional scale makes it an appealing illuminator of opportunity for bistatic radars. Different types of receiving-only devices located on or near the Earth could exploit the same signal source, noticeably already conceived for radar purposes, for applications in the framework of both military and civil surveillance. This paper provides an overview of possible parasitic applications enabled by a GEO-SAR illuminator in different operative scenarios, including aerial, ground and maritime surveillance. For each selected scenario, different receiver configurations are proposed, providing an assessment of the achievable performance with discussions about the expected potentialities and challenges. This research aims at serving as a roadmap for designing parasitic systems relying on GEO-SAR signals, and also aims at extending the net of potential users interested in investing in GEO-SAR missions. Full article

(This article belongs to the Special Issue Recent Advances on Radar and Remote Sensing Using Satellite Signals of Opportunity)

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17 pages, 6327 KiB

Open AccessArticle

BeiDou-Based Passive Radar Vessel Target Detection: Method and Experiment via Long-Time Optimized Integration

by Chuan Huang, Zhongyu Li, Mingyue Lou, Xingye Qiu, Hongyang An, Junjie Wu, Jianyu Yang and Wei Huang

Remote Sens. 2021, 13(19), 3933; https://doi.org/10.3390/rs13193933 - 30 Sep 2021

Cited by 14 | Viewed by 2804

Abstract

The BeiDou navigation satellite system shows its potential for passive radar vessel target detection owing to its global-scale coverage. However, the restrained power budget from BeiDou satellite hampers the detection performance. To solve this limitation, this paper proposes a long-time optimized integration method [...] Read more.

The BeiDou navigation satellite system shows its potential for passive radar vessel target detection owing to its global-scale coverage. However, the restrained power budget from BeiDou satellite hampers the detection performance. To solve this limitation, this paper proposes a long-time optimized integration method to obtain an adequate signal-to-noise ratio (SNR). During the long observation time, the range migration, intricate Doppler migration, and noncoherence characteristic bring challenges to the integration processing. In this paper, first, the keystone transform is applied to correct the range walk. Then, considering the noncoherence of the entire echo, the hybrid integration strategy is adopted. To remove the Doppler migration and correct the residual range migration, the long-time integration is modeled as an optimization problem. Finally, the particle swarm optimization (PSO) algorithm is applied to solve the optimization problem, after which the target echo over the long observation time is well concentrated, providing a reliable detection performance for the BeiDou-based passive radar. Its effectiveness is shown by the simulated and experimental results. Full article

(This article belongs to the Special Issue Recent Advances on Radar and Remote Sensing Using Satellite Signals of Opportunity)

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15 pages, 7107 KiB

Open AccessArticle

Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System

by Yu’e Shao, Hui Ma, Shenghua Zhou, Xue Wang, Michail Antoniou and Hongwei Liu

Remote Sens. 2021, 13(4), 707; https://doi.org/10.3390/rs13040707 - 15 Feb 2021

Cited by 4 | Viewed by 2752

Abstract

To cope with the increasingly complex electromagnetic environment, multistatic radar systems, especially the passive multistatic radar, are becoming a trend of future radar development due to their advantages in anti-electronic jam, anti-destruction properties, and no electromagnetic pollution. However, one problem with this multi-source [...] Read more.

To cope with the increasingly complex electromagnetic environment, multistatic radar systems, especially the passive multistatic radar, are becoming a trend of future radar development due to their advantages in anti-electronic jam, anti-destruction properties, and no electromagnetic pollution. However, one problem with this multi-source network is that it brings a huge amount of information and leads to considerable computational load. Aiming at the problem, this paper introduces the idea of selecting external illuminators in the multistatic passive radar system. Its essence is to optimize the configuration of multistatic T/R pairs. Based on this, this paper respectively proposes two multi-source optimization algorithms from the perspective of resolution unit and resolution capability, the Covariance Matrix Fusion Method and Convex Hull Optimization Method, and then uses a Global Navigation Satellite System (GNSS) as an external illuminator to verify the algorithms. The experimental results show that the two optimization methods significantly improve the accuracy of multistatic positioning, and obtain a more reasonable use of system resources. To evaluate the algorithm performance under large number of transmitting/receiving stations, further simulation was conducted, in which a combination of the two algorithms were applied and the combined algorithm has shown its effectiveness in minimize the computational load and retain the target localization precision at the same time. Full article

(This article belongs to the Special Issue Recent Advances on Radar and Remote Sensing Using Satellite Signals of Opportunity)

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16 pages, 3751 KiB

Open AccessArticle

Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar

by Binbin Wang, Hao Cha, Zibo Zhou and Bin Tian

Remote Sens. 2021, 13(4), 701; https://doi.org/10.3390/rs13040701 - 14 Feb 2021

Cited by 14 | Viewed by 3299

Abstract

Clutter cancellation and long time integration are two vital steps for global navigation satellite system (GNSS)-based bistatic radar target detection. The former eliminates the influence of direct and multipath signals on the target detection performance, and the latter improves the radar detection range. [...] Read more.

Clutter cancellation and long time integration are two vital steps for global navigation satellite system (GNSS)-based bistatic radar target detection. The former eliminates the influence of direct and multipath signals on the target detection performance, and the latter improves the radar detection range. In this paper, the extensive cancellation algorithm (ECA), which projects the surveillance channel signal in the subspace orthogonal to the clutter subspace, is first applied in GNSS-based bistatic radar. As a result, the clutter has been removed from the surveillance channel effectively. For long time integration, a modified version of the Fourier transform (FT), called long-time integration Fourier transform (LIFT), is proposed to obtain a high coherent processing gain. Relative acceleration (RA) is defined to describe the Doppler variation results from the motion of the target and long integration time. With the estimated RA, the Doppler frequency shift compensation is carried out in the LIFT. This method achieves a better and robust detection performance when comparing with the traditional coherent integration method. The simulation results demonstrate the effectiveness and advantages of the proposed processing method. Full article

(This article belongs to the Special Issue Recent Advances on Radar and Remote Sensing Using Satellite Signals of Opportunity)

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24 pages, 6680 KiB

Open AccessArticle

Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach

by Yan Jia, Shuanggen Jin, Patrizia Savi, Qingyun Yan and Wenmei Li

Remote Sens. 2020, 12(22), 3679; https://doi.org/10.3390/rs12223679 - 10 Nov 2020

Cited by 43 | Viewed by 4470

Abstract

Global Navigation Satellite System-Reflectometry (GNSS-R) as a microwave remote sensing technique can retrieve the Earth’s surface parameters using the GNSS reflected signal from the surface. These reflected signals convey the surface features and therefore can be utilized to detect certain physical properties of [...] Read more.

Global Navigation Satellite System-Reflectometry (GNSS-R) as a microwave remote sensing technique can retrieve the Earth’s surface parameters using the GNSS reflected signal from the surface. These reflected signals convey the surface features and therefore can be utilized to detect certain physical properties of the reflecting surface such as soil moisture content (SMC). Up to now, a serial of electromagnetic models (e.g., bistatic radar and Fresnel equations, etc.) are employed and solved for SMC retrieval. However, due to the uncertainty of the physical characteristics of the sites, complexity, and nonlinearity of the inversion process, etc., it is still challenging to accurately retrieve the soil moisture. The popular machine learning (ML) methods are flexible and able to handle nonlinear problems. It can dig out and model the complex interactions between input and output and ultimately make good predictions. In this paper, two typical ML methods, specifically, random forest (RF) and support vector machine (SVM), are employed for SMC retrieval from GNSS-R data of self-designed experiments (in situ and airborne). A comprehensive simulated dataset involving different types of soil is constructed firstly to represent the complex interactions between the variables (reflectivity, elevation angle, dielectric constant, and SMC) for the requirement of training ML regression models. Correspondingly, the main task of soil moisture retrieval (regression) is addressed. Specifically, the post-processed data (reflectivity and elevation angle) from sensor acquisitions are used to make predictions by these two adopted ML methods and compared with the commonly used GNSS-R retrieval method (electromagnetic models). The results show that the RF outperforms the SVM method, and it is more suitable for handling the inversion problem. Moreover, the RF regression model built by the comprehensive dataset demonstrates satisfactory accuracy and strong universality, especially when the soil type is not uniform or unknown. Furthermore, the typical task of detecting water/soil (classification) is discussed. The ML algorithms demonstrate a high potential and efficiency in SMC retrieval from GNSS-R data. Full article

(This article belongs to the Special Issue Recent Advances on Radar and Remote Sensing Using Satellite Signals of Opportunity)

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24 pages, 6727 KiB

Open AccessArticle

Moving Target Detection in Multi-Static GNSS-Based Passive Radar Based on Multi-Bernoulli Filter

by HongCheng Zeng, Jie Chen, PengBo Wang, Wei Liu, XinKai Zhou and Wei Yang

Remote Sens. 2020, 12(21), 3495; https://doi.org/10.3390/rs12213495 - 24 Oct 2020

Cited by 10 | Viewed by 3528

Abstract

Over the past few years, the global navigation satellite system (GNSS)-based passive radar (GBPR) has attracted more and more attention and has developed very quickly. However, the low power level of GNSS signal limits its application. To enhance the ability of moving target [...] Read more.

Over the past few years, the global navigation satellite system (GNSS)-based passive radar (GBPR) has attracted more and more attention and has developed very quickly. However, the low power level of GNSS signal limits its application. To enhance the ability of moving target detection, a multi-static GBPR (MsGBPR) system is considered in this paper, and a modified iterated-corrector multi-Bernoulli (ICMB) filter is also proposed. The likelihood ratio model of the MsGBPR with range-Doppler map is first presented. Then, a signal-to-noise ratio (SNR) online estimation method is proposed, which can estimate the fluctuating and unknown map SNR effectively. After that, a modified ICMB filter and its sequential Monte Carlo (SMC) implementation are proposed, which can update all measurements from multi-transmitters in the optimum order (ascending order). Moreover, based on the proposed method, a moving target detecting framework using MsGBPR data is also presented. Finally, performance of the proposed method is demonstrated by numerical simulations and preliminary experimental results, and it is shown that the position and velocity of the moving target can be estimated accurately. Full article

(This article belongs to the Special Issue Recent Advances on Radar and Remote Sensing Using Satellite Signals of Opportunity)

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26 pages, 8848 KiB

Open AccessArticle

Passive Forward-Scattering Radar Using Digital Video Broadcasting Satellite Signal for Drone Detection

by Raja Syamsul Azmir Raja Abdullah, Surajo Alhaji Musa, Nur Emileen Abdul Rashid, Aduwati Sali, Asem Ahmad Salah and Alyani Ismail

Remote Sens. 2020, 12(18), 3075; https://doi.org/10.3390/rs12183075 - 19 Sep 2020

Cited by 26 | Viewed by 6090

Abstract

This paper presents a passive radar system using a signal of opportunity from Digital Video Broadcasting Satellite (DVB-S). The ultimate purpose of the system is to be used as an air traffic monitoring and surveillance system. However, the work focuses on drone detection [...] Read more.

This paper presents a passive radar system using a signal of opportunity from Digital Video Broadcasting Satellite (DVB-S). The ultimate purpose of the system is to be used as an air traffic monitoring and surveillance system. However, the work focuses on drone detection as a proof of the concept. Detecting a drone by using satellite-based passive radar possess inherent challenges, such as the small radar cross section and low speed. Therefore, this paper proposes a unique method by leveraging the advantage of forward-scattering radar (FSR) topology and characteristics to detect a drone; in other words, the system is known as a passive FSR (p-FSR) system. In the signal-processing algorithm, the empirical mode decomposition (EMD) is applied to the received signal to extract the unique feature vector of the micro-Doppler frequency from the drone’s rotating blades. The paper highlights the p-FSR experimental setup and experiment campaign to detect drones. The experimental results show the feasibility of the p-FSR using a signal transmitted from a satellite to detect flying drone crossing the forward-scatter baseline between the satellite and ground station. Full article

(This article belongs to the Special Issue Recent Advances on Radar and Remote Sensing Using Satellite Signals of Opportunity)

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14 pages, 1015 KiB

Open AccessArticle

Evaluation of the Ocean Surface Wind Speed Change following the Super Typhoon from Space-Borne GNSS-Reflectometry

by Hongsu Liu, Shuanggen Jin and Qingyun Yan

Remote Sens. 2020, 12(12), 2034; https://doi.org/10.3390/rs12122034 - 24 Jun 2020

Cited by 8 | Viewed by 3434

Abstract

Ocean surface wind speed is an essential parameter for typhoon monitoring and forecasting. However, traditional satellite and buoy observations are difficult to monitor the typhoon due to high cost and low temporal-spatial resolution. With the development of spaceborne GNSS-R technology, the cyclone global [...] Read more.

Ocean surface wind speed is an essential parameter for typhoon monitoring and forecasting. However, traditional satellite and buoy observations are difficult to monitor the typhoon due to high cost and low temporal-spatial resolution. With the development of spaceborne GNSS-R technology, the cyclone global navigation satellite system (CYGNSS) with eight satellites in low-earth orbit provides an opportunity to measure the ocean surface wind speed of typhoons. Though observations are made at the extremely efficient spatial and temporal resolution, its accuracy and reliability are unclear in an actual super typhoon case. In this study, the wind speed variations over the life cycle of the 2018 Typhoon Mangkhut from CYGNSS observations were evaluated and compared with European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-5 (ERA-5). The results show that the overall root-mean-square error (RMSE) of CYGNSS versus ECMWF was 4.12 m/s, the mean error was 1.36 m/s, and the correlation coefficient was 0.96. For wind speeds lower and greater than 15 m/s, the RMSE of CYGNSS versus ECMWF were 1.02 and 4.36 m/s, the mean errors were 0.05 and 1.61 m/s, the correlation coefficients were 0.91 and 0.90, and the average relative errors were 9.8% and 11.6%, respectively. When the typhoon reached a strong typhoon or super typhoon, the RMSE of CYGNSS with respect to ERA-5 from ECMWF was 5.07 m/s; the mean error was 3.57 m/s; the correlation coefficient was 0.52 and the average relative error was 11.0%. The CYGNSS estimation had higher precision for wind speeds below 15 m/s, but degraded when the wind speed was above 15 m/s. Full article

(This article belongs to the Special Issue Recent Advances on Radar and Remote Sensing Using Satellite Signals of Opportunity)

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