Interference Detection Based on Time-Frequency Analysis for GNSS

K. Sun, W. Liu, H. Xu, D. Yang

Abstract:	During the recent years, the demand for navigation and location based services (LBS) using Global Navigation Satellite Systems (GNSS) have gained worldwide popularity and extensive exploitation in many areas. This has been fueled by an increase in the number of consumer electronic devices in the marketplace, such as mobile phones, PDAs and popular in-car navigation systems that come equipped with GNSS receivers. As these devices become increasingly popular, their uses and applications will inevitably extend towards more challenging environments such as shopping malls, deep urban canyons and office buildings, where signal attenuations are introduced because of the existence of blocking obstacles (e.g. walls, floors, etc.). Blockage of the line-of-sight (LOS) component is one of the main problems of GNSS receivers when operating in harsh working conditions. Moreover, the requirements of emergency caller localization services spurred by the Enhanced 911 (E-911, North America) mandate and the E-112 (European Union, EU) initiative have required mobile terminals to be able to report their position even in harsh environments like indoor scenarios. Without any doubt, these requirements have emerged as a catalyst for the research and development in GNSS receiver innovative technologies, and the motivation to face new challenges such as the operation of GNSS receivers in harsh environments. The common feature of all these new working scenarios is the presence of blocking obstacles and propagation disturbances that prevent GNSS receivers from observing the expected perfect clear-sky conditions that were assumed in the nominal design of the system. Consequently, the motivation of our work is to improve the operation of GNSS receivers in urban areas by providing the means to track the received signal despite of being affected by signal fading or blockage. GNSS Receivers operating under these working conditions are often referred to as high-sensitivity(HS) GNSS receivers, which are essential to cope with such a severe impairment while still providing precise positioning information even though the radio environment is far from ideal in harsh environments where extremely low power signals dominate. Thus, the objective to be pursued here involves the design of improved high-sensitivity tracking algorithms, with special emphasis in their application to urban areas or seriously attenuated indoor locations. In a GNSS receiver, after the acquisition stage has detected the presence of a given satellite vehicle (SV) and coarsely estimated the offset on the residual carrier and the code phase delay with respect to the local replicas, a fine synchronization stage, referred to as signal tracking, is activated to refine these estimation values. The fundamental objective of a GNSS receiver during signal tracking stage is to generate a local signal replica that matches the incoming signal as closely as possible by means of closed loop operations. Most traditional GNSS receivers are designed to track each satellite independently at single carrier frequency per channel; an approach herein referred to as “scalar-tracking”. The scalar-tracking consists of two interoperating feedback loops, a Delay Lock Loop (DLL) for code tracking and a Phase Lock Loop (PLL) for carrier tracking (typically a Costas Loop). The objective of scalar-tracking is to estimate, on a satellite-by-satellite basis, the code phase delay and carrier frequency (and optionally carrier phase) of the incoming signals. These estimates, or more precisely, the tracking errors thereof, are then adopted in feedback loop to drive numerically controlled oscillator (NCO) for the local signal generation. In traditional GNSS receivers, scalar tracking loops are used to estimate the pseudoranges and pseudorange-rates for the detected satellites, and these estimates are then fed forward to the navigation processor, which solves for the receiver’s position, velocity, clock bias, and clock drift(i.e., the navigation states). The navigation processor is typically an iterative least squares algorithm or a Kalman filter. Each channel of the receiver tracks its respective satellite signal independent of the other channels. Usually no information is fed back to the track loops. The traditional receiver architecture does not exploit the inherent relation between signal tracking and navigation estimation. Scalar tracking loops operate well in environments with high carrier power-to-noise density ratio (C/N0) levels and low user dynamics. However, they have several inherent flaws. First, in traditional GNSS receivers, tracking loops use loop filters with fixed gains and bandwidths. This means that all the phase error signals are equally weighted. In ideal case, phase error measurements made during periods of high C/N0 levels should be weighted more heavily than those done during periods with low C/N0 values. In addition, during times of satellite blockage, the loops are in a state of random walk Second, the tracking loops in the different channels of the receiver operate independently of each other and don’t exploit all the user’s knowledge. Information about the satellite constellation and user position can be used to predict the received signals. Recently, several researchers have studied vector tracking loops, such as a vector delay lock loop (VDLL) and a vector frequency lock loop (VFLL), in order to obtain improved tracking performance with GNSS receivers particularly in weak signal scenarios. Vector tracking loops are a novel type of receiver architecture. The difference between traditional receivers and those that use vector tracking algorithms is the manner in which they process the received GNSS satellite signals, and how they determine the receiver’s position and velocity. Vector-based tracking loops combine the two tasks of signal tracking and position/velocity estimation into one algorithm. The coupling of the tracking loops through the navigation solution has been deeply studied. In contrast to traditional GNSS receivers, vector tracking algorithms exploit the inherent coupling between signal tracking stage and navigation solution computation process, and combine both tasks into a single step, eliminating the need for intermediate tracking loops. Vector tracking-based receiver employs a vector delay/frequency lock loop (VDFLL). The pseudoranges and pseudorange-rates are usually predicted by an Extended Kalman Filter (EKF) for each satellite signal to be tracked. The prediction is performed using the estimated navigation states and the computed satellite position and velocity. Each channel of the receiver then produces pseudorange and pseudorange-rate residuals (differences) relative to the predicted pseudoranges and pseudorange-rates. The EKF then uses these residuals to update its estimates of the receiver’s navigation states. In the VDFLL architecture, the vector tracking loop is closed through the EKF. The pseudoranges are tied together through position states and one clock bias state; and similarly, the pseudorange-rates are coupled through three velocity states and one clock drift state. In a VDFLL, the tracking of all the received signals is handled by the single EKF. This research work is extended to quantify the robustness and high sensitivity benefits of a vector tracking-based GNSS receiver. Clearly the receiver requires no additional hardware and, as a result, no increase in power requirements, weight and size, and no decrease in reliability, all benefits to the users. The VDFLL has several potential advantages over the traditional scalar tracking loops. First, the EKF can weight measurements of the code phase and carrier frequency errors. Therefore, provided the noise statistics of the individual measurements, the EKF can optimally estimate the user’s states. Second, tracking weak signals can be facilitated by the tracking of stronger signals, and the receiver with the vector tracking-based arechitecture can get enough total signal power to track signal successfully and can obtain accurate position estimates although the signal strength from individual satellite is so low or weak. This is due to the replica signals being controlled by the EKF’s estimate of the user’s navigation states. Third, the VDFLL has the potential to rapidly reacquire signals after a satellite blockage. Last, vector tracking has increased immunity to interference and jamming. The minimum C/N0 at which the receiver can operate is lowered by processing the signals in aggregate instead of separately. In this paper, a variant of the Vector Delay/Frequency Lock Loop algorithm has been developed for Galileo E1 OS signals for reliable and high accuracy positioning in harsh environments. A single EKF is adopted to track the satellite signals and the user’s position, velocity, acceleration, and clock states. The EKF’s states are used to predict the received signals from each satellite. The performance of the vector tracking algorithms in a GNSS challenged environment has been compared to that of a high end commercial receiver with the traditional scalar tracking loops. The analysis results show that the pseudorange variance from the vector tracking method will generally be less than those with the scalar tracking approach if the number of the available satellites exceed four. This is another important benefit of vector based tracking architecture for GNSS receivers. Vector-based tracking techniques are extremely useful because of their ability to mitigate noise which should improve signal tracking capability under weak-signal conditions. The vector tracking loop combines the tracking of the different satellite PRN and carrier signals into a single algorithm, and it tracks codes and carriers of received signals from all visible GNSS satellites jointly, therefore, it can take full advantage of correlation between code and carrier. In addition, it can also mitigate the interference between GNSS signals. It is important to emphasize that a greater computational load and increased structural complexity is generally required to design vector tracking based architecture for robust and high sensitivity GNSS receivers.
Published in:	Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013) September 16 - 20, 2013 Nashville Convention Center, Nashville, Tennessee Nashville, TN
Pages:	3577 - 3585
Cite this article:	Sun, K., Liu, W., Xu, H., Yang, D., "Interference Detection Based on Time-Frequency Analysis for GNSS," Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013), Nashville, TN, September 2013, pp. 3577-3585.
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