Software-Defined Radio GNSS Instrumentation for Spoofing Mitigation: A Review and a Case Study

Schmidt, Erick; Ruble, Zach A.; Akopian, David; Pack, Daniel J.

doi:10.1109/tim.2018.2869261

Cited by 48 publications

(15 citation statements)

References 57 publications

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“…This method requires no additional aiding, which is more feasible for instrumentation where GNSS receivers are embedded. A promising extension of this work is to correct the multipath-induced measurement error and spoofing mitigation using VTL-based GNSS SDR [28,45].…”

Section: Discussionmentioning

confidence: 99%

Vector Tracking Loop-Based GNSS NLOS Detection and Correction: Algorithm Design and Performance Analysis

Jia

Hsu

2020

IEEE Trans. Instrum. Meas.

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This paper deals with the none-line-of-sight (NLOS) reception issue in the field of global navigation satellite system (GNSS). The NLOS reception has attracted a significant amount of attention because it is one of the main factors that limit the GNSS position accuracy in urban areas. In this work, we dig into the baseband signal processing level to explore a new solution to the NLOS detection and correction by means of the vector tracking loop (VTL). The NLOS effects on both conventional scalar tracking loops (STL) and VTL are derived mathematically. Based on this, an NLOS detection algorithm is developed using metrics such as the equivalent noise bandwidth, the time delay of multi-correlator peaks, as well as code discriminator outputs. Once detected, the NLOS-induced measurement error is corrected before being fed forward into the navigation estimator to improve the position accuracy. Two field tests in urban areas in Hong Kong are conducted to illustrate the effectiveness of the proposed method in real applications. The NLOS correction performance is also assessed using simulated NLOS receptions with controllable time delays and reflection coefficients, which reveals how the proposed algorithm performs in different NLOS scenarios. Index Terms-Global navigation satellite system (GNSS), nonline-of-sight (NLOS) reception, vector tracking loops (VTL), software-defined receivers (SDR), urban canyon TABLE I TECHNIQUES DEALING WITH NLOS RECEPTION Category Techniques Mitigation Antenna design, e.g., choke-ring antenna[11] Consistency checking techniques, e.g., based on pseudorange residual and navigation solution [12-14] Kalman filter-based innovation filtering [15] Detection Hardware-based techniques, e.g., array antenna[16], dualpolarization antenna [17], sky-pointing camera [18, 19] Machine learning technique [20-23] 3D building model [20, 24-27] Vector tracking loops (VTL) [28] Correction 3D mapping-aided (3DMA) [24, 29], building height aided 3-dimentional light detection and ranging (3D LiDAR) [30], NLOS modeling [31] The first category is mitigation, where any potentially contaminated measurements, including the NLOS reception, are directly removed or reduced without being clearly classified. A typical example of this method is the usage of choke-ring antenna [11], which gives low gains to satellites with low research was supported by the Hong Kong PolyU startup fund on the project 1-ZVKZ, "Navigation for Autonomous Driving Vehicle using Sensor Integration".

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Section: Discussionmentioning

confidence: 99%

Vector Tracking Loop-Based GNSS NLOS Detection and Correction: Algorithm Design and Performance Analysis

Jia

Hsu

2020

IEEE Trans. Instrum. Meas.

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“…SDR solutions become popular because of providing full control of receiver modules, so the researchers can integrate and test their methods without redesigning all receiver chains. This becomes an advantage for SDR-based research for fast-prototyping [12], [34]. The proposed single-antenna SDR mimics legacy OFDM-based NICs that are capable of passively listening for available networks and decoding non-HT beacon frames.…”

Section: Proposed Ofdm Software Receiver a Receiver Overviewmentioning

confidence: 99%

“…The receiver is implemented by the Software Communications and Navigation Systems Laboratory at The University of Texas at San Antonio. We implemented an OFDM-based WLAN receiver in a platform-based environment by using LabVIEW, a platform from National Instruments that uses a visual programming language [34]. LabVIEW interfaces between the host PC, where the software resides, and the front-end.…”

Section: Proposed Ofdm Software Receiver a Receiver Overviewmentioning

confidence: 99%

“…Our SDR collects measurements asynchronously and in a realtime manner from genuine WLAN APs based on the 802.11 protocol [33]. The described SDR receiver achieves real-time packet collection at almost 10 Hz by use of acceleration features from a fast-prototyping software platform [17], [34]. Thus, we exploit SDR fast-prototyping features to assess a real-life application to an IPS where no modification to the WLAN infrastructure is required.…”

Section: Introductionmentioning

confidence: 99%

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SDR-Fi: Deep-Learning-Based Indoor Positioning via Software-Defined Radio

et al. 2019

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Wi-Fi fingerprinting-based indoor localization has received increased attention due to its proven accuracy and global availability. The common received-signal-strength-based (RSS) fingerprinting presents performance degradation due to well-known signal fluctuations, but more recently, the more stable channel state information (CSI) has gained popularity. In this paper, we present SDR-Fi, the first reported Wi-Fi software-defined radio (SDR) receiver for indoor positioning using CSI measurements as features for deep learning (DL) classification. The CSI measurements are obtained from a fast-prototyping LabVIEWbased 802.11n SDR receiver platform. SDR-Fi measures CSI data passively from pilot beacon frames from a single access point (AP) at almost 10 Hz rate. A feed-forward neural network and a 1D convolutional neural network are examined to estimate location accuracy in representative testing scenarios for an indoor cluttered laboratory area, and an adjacent, covered outdoor area. The proposed DL classification methods leverage CSI-based fingerprinting for low AP scenarios, as opposed to traditional RSS-based systems, which require many APs for reliable positioning. Demonstration results are threefold: (a) A fast-prototyping SDR platform that passively extracts CSI measurements from Wi-Fi beacon frames, providing a genuine possibility for vendor network cards to provide such measurements, (b) two state-of-the-art DL classification methods outperforming traditional RSS-based methods for low AP scenarios, (c) a testing methodology for performance evaluation of the proposed indoor positioning system.INDEX TERMS Channel state information, deep learning, fingerprinting, indoor positioning, neural networks, software-defined radio.

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“…It is extremely important to keep this system free of interference. Types of interference with GNSS include multipath [ 3 , 4 ], ionospheric scintillation [ 5 ], spoofing [ 6 , 7 ], electromagnetic interference, satellite signal anomalies, and intense L-band solar radio bursts (SRBs) [ 8 ]. SRB is a strong signal in the microwave band from solar plasma radiation and cyclotron synchrotron radiation.…”

Section: Introductionmentioning

confidence: 99%

Intense L-Band Solar Radio Bursts Detection Based on GNSS Carrier-To-Noise Ratio Decrease over Multi-Satellite and Multi-Station

Fan

Zhu

Chen

et al. 2021

Sensors

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Intense solar radio bursts (SRBs) can increase the energy noise and positioning error of the bandwidth of global navigation satellite system (GNSS). The study of the interference from intense L-band SRBs is of great importance to the steady operation of GNSS receivers. Based on the fact that intense L-band SRBs lead to a decrease in the carrier-to-noise ratio (C/N0) of multiple GNSS satellites over a large area of the sunlit hemisphere, an intense L-band SRB detection method without the aid of a radio telescope is proposed. Firstly, the valley period of a single satellite at a single monitoring station is detected. Then, the detection of SRBs is achieved by calculating the intersection of multiple satellites and multiple stations. The experimental results indicate that the detection rates of GPS L2 and GLONASS G2 are better than those of GPS L1 L5, GLONASS G1, and Galileo E1 E5. The detection rate of SRBs can reach 80% with a flux density above 800 solar flux unit (SFU) at the L2 frequency of GPS. Overall, the detection rate is not affected by the satellite distribution relative to the Sun. The proposed detection method is low-cost and has a high detection rate and low false alarm rate. This method is a noteworthy reference for coping with interference in GNSS from intense L-band SRBs.

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Software-Defined Radio GNSS Instrumentation for Spoofing Mitigation: A Review and a Case Study

Cited by 48 publications

References 57 publications

Vector Tracking Loop-Based GNSS NLOS Detection and Correction: Algorithm Design and Performance Analysis

Vector Tracking Loop-Based GNSS NLOS Detection and Correction: Algorithm Design and Performance Analysis

SDR-Fi: Deep-Learning-Based Indoor Positioning via Software-Defined Radio

Intense L-Band Solar Radio Bursts Detection Based on GNSS Carrier-To-Noise Ratio Decrease over Multi-Satellite and Multi-Station

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