Critical government and industry sectors (such as law enforcement, transportation, communication, and finance) are growing increasingly dependent on Global Navigation Satellite Systems (GNSS) for positioning, navigation, and timing. At the same time, the availability of low-cost GNSS jamming devices are presenting a serious threat to GNSS and increasing the likelihood of outages to infrastructures relying on GNSS. The attacks range from malicious parties intentionally jamming GNSS signals within a targeted geographical region to uninformed users causing accidental interference. This paper is an overview of different approaches adopted to date to mitigate GNSS disruption caused by intentional and unintentional jamming. The first approach outlined in this paper is the use of inertial systems to aid GNSS. The second and third approaches are the filtering of jamming/interference in the spatial and time-frequency domains, respectively. The fourth approach is vector tracking of GNSS signals in the receiver.

Protecting GNSS Receivers From Jamming and Interference

The research regarding global positioning system (GPS) vector tracking (VT), based on a software-defined receiver (SDR), has been increasing in recent years. The strengths of VT include its immunity to signal interference, its capability to mitigate multipath effects in urban areas, and its excellent performance in tracking signals under high-dynamic applications. We developed open-source MATLAB code for GPS VT SDR to enable researchers and scientists to investigate its pros and cons in various applications and under various environments. To achieve this goal, we developed an "equivalent conventional tracking (CT)" SDR as a baseline to compare with VT. The GPS positioning estimator of this equivalent CT is based on an extended Kalman filter (EKF), which has exactly the same state, system, and carrier measurement models and noise tuning method as VT. This baseline provides users with a tool to compare the performance of VT and CT on common ground. In addition, this MATLAB code is well organized and easy to use. Users can quickly implement and evaluate their own newly developed baseband signal processing algorithms related to VT. The implementation of this VT code is described in detail. Finally, static and kinematic experiments were conducted in an urban and open-sky area, respectively, to show the usage and performance of the developed open-source GPS VT SDR.

/pdf/open-source-matlab-code-for-gps-vector-tracking-on-a-4nvp371vza.pdf

Open-source MATLAB code for GPS vector tracking on a software-defined receiver

Microelectromechanical System (MEMS) Inertial Measurement Unit (IMU) is popular in the community for constructing a navigation system, due to its small size and low power consumption. However, limited by the manufacturing technology, MEMS IMU experiences more complicated noises and errors. Thus, noise modeling and suppression is important for improving accuracy of the navigation system based on MEMS IMU. Motivated by this problem, in this paper, a deep learning method was introduced to MEMS gyroscope de-noising. Specifically, a recently popular Recurrent Neural Networks (RNN) variant Simple Recurrent Unit (SRU-RNN) was employed in MEMS gyroscope raw signals de-noising. A MEMS IMU MSI3200 from MT Microsystem Company was employed in the experiments for evaluating the proposed method. Following two problems were furtherly discussed and investigated: (1) the employed SRU with different training data length were compared to explore whether there was trade-off between the training data length and prediction performance; (2) Allan Variance was the most popular MEMS gyroscope analyzing method, and five basic parameters were employed to describe the performance of different grade MEMS gyroscope; among them, quantization noise, angle random walk, and bias instability were the major factors influencing the MEMS gyroscope accuracy, the compensation results of the three parameters for gyroscope were presented and compared. The results supported the following conclusions: (1) considering the computation brought from training dataset, the values of 500, 3000, and 3000 were individually sufficient for the three-axis gyroscopes to obtain a reliable and stable prediction performance; (2) among the parameters, the quantization noise, angle random walk, and bias instability performed 0.6%, 6.8%, and 12.5% improvement for X-axis gyroscope, 60.5%, 17.3%, and 34.1% improvement for Y-axis gyroscope, 11.3%, 22.7%, and 35.7% improvement for Z-axis gyroscope, and the corresponding attitude errors decreased by 19.2%, 82.1%, and 69.4%. The results surely demonstrated the effectiveness of the employed SRU in this application.

Performance Analysis of a Deep Simple Recurrent Unit Recurrent Neural Network (SRU-RNN) in MEMS Gyroscope De-Noising.

This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively.

https://www.mdpi.com/1424-8220/14/12/23630/pdf

Performance enhancement for a GPS vector-tracking loop utilizing an adaptive iterated extended Kalman filter.

Chip scale atomic clock (CSAC) is a recently developed low-profile atomic frequency reference device. Researchers have demonstrated CSAC was able to improve the accuracy of Global navigation satellite system (GNSS) receiver positioning results. However, the work was conducted using scalar tracking loops (STL). It is meaningful to investigate CSAC augmented vector tracking loop (VTL), since the VTL has different architecture with STL. Thus, in this paper, the authors investigated a CSAC driven VTL_DI system for exploring CASC impact on VTL. Firstly, the authors explored 1) whether VTL_DI could operate without regarding clock offset and drifts as VTL_DI integration filter state variables, and 2) whether VTL_DI could provide reliable position with only three satellites in view. Secondly, side-by-side comparative field tests were conducted to evaluate performance improvement: by solution A: VTL_DI supported by CSAC and solution B: a temperature compensated crystal oscillator (TXCO) with more than four satellites and three satellites in view scenarios. It was discovered that the CSAC could enhance the position performance, even if amount of satellites were fewer than four in view. However, VTL_DI system could not guarantee its positioning accuracy without clock offset as state variable, which was different from STL_DI system.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rsn.2018.5152

Research on a chip scale atomic clock driven GNSS/SINS deeply coupled navigation system for augmented performance

A number of methods have been developed to enhance the robustness of Global Positioning System (GPS) receivers when there are a limited number of visible satellites. Vector tracking is one of them. It utilizes information from all channels to aid the processing of individual channels to generate receiver positions and velocities. This paper analyzes relationships among code phase, carrier frequency, and receiver position and velocity, and presents a vector loop-tracking algorithm using an Extended Kalman filter implemented in a Matlab-based GPS software receiver. Simulated GPS signals are generated to test the proposed vector tracking method. The results show that when some of the satellites are blocked, the vector tracking loop provides better carrier frequency tracking results for the blocked signals and produces more accurate navigation solutions compared with traditional scalar tracking loops.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0373463311000440

Zhenming Feng

Papers

Implementation and Performance Assessment of a Vector Tracking Method Based on a Software GPS Receiver

A Phase-locked Loop Design with External Aiding Based on Kalman Filter