Analysis of non ambiguous BOC signal acquisition performance Acquisition

The Binary Offset Carrier planned for future GNSS signal, including several GALILEO Signals as well as GPS M-code, presents a high degree of spectral separation from conventional signals. It also greatly improves positioning accuracy and enhances multipath rejection. However, with such a modulation, the acquisition process is made more complex. Specific techniques must be employed in order to avoid unacceptable errors. This paper assesses the performance of three method allowing to acquire and track BOC signal unambiguously : The Bump-jumping technique, The "BPSK-like" technique and the subcarrier Phase cancellation technique

Linear-Combined-Code-Based Unambiguous Code Discriminator Design for Multipath Mitigation in GNSS Receivers

Unambiguous tracking and multipath mitigation for Binary Offset Carrier (BOC) signals are two important requirements of modern Global Navigation Satellite Systems (GNSS) receivers. A GNSS discriminator design method based on optimization technique is proposed in this paper to meet these requirements. Firstly, the discriminator structure based on a linear-combined code is given. Then the requirements of ideal discriminator function are converted into the mathematical constraints and the objective function to form a non-linear optimization problem. Finally, the problem is solved and the local code is generated according to the results. The theoretical analysis and simulation results indicate that the proposed method can completely remove the false lock points for BOC signals and provide superior multipath mitigation performance compared with traditional discriminator and high revolution correlator (HRC) technique. Moreover, the proposed discriminator is easy to implement for not increasing the number of correlators

Efficient Delay Tracking Methods with Sidelobes Cancellation for BOC-Modulated Signals

In positioning applications, where the line of sight (LOS) is needed with high accuracy, the accurate delay estimation is an important task. The new satellite-based positioning systems, such as Galileo and modernized GPS, will use a new modulation type, that is, the binary offset carrier (BOC) modulation. This type of modulation creates multiple peaks (ambiguities) in the envelope of the correlation function, and thus triggers new challenges in the delay-frequency acquisition and tracking stages. Moreover, the properties of BOC-modulated signals are yet not well studied in the context of fading multipath channels. In this paper, sidelobe cancellation techniques are applied with various tracking structures in order to remove or diminish the side peaks, while keeping a sharp and narrow main lobe, thus allowing a better tracking. Five sidelobe cancellation methods (SCM) are proposed and studied: SCM with interference cancellation (IC), SCM with narrow correlator, SCM with high-resolution correlator (HRC), SCM with differential correlation (DC), and SCM with threshold. Compared to other delay tracking methods, the proposed SCM approaches have the advantage that they can be applied to any sine or cosine BOC-modulated signal. We analyze the performances of various tracking techniques in the presence of fading multipath channels and we compare them with other methods existing in the literature. The SCM approaches bring improvement also in scenarios with closely-spaced paths, which are the most problematic from the accurate positioning point of view.</p

Design and Validation of a Software Receiver for Galileo

This paper presents a new concept of software-based GNSS receivers for the Binary Offset Carrier signals of the Galileo testbed satellites, Giove-A and Giove-B. The receiver designed in Matlab must be able to acquire, track and demodulate both signals. Its advantages in terms of flexibility and reconfigurability make it an excellent instrument to test new designs or new prototypes as a previous stage on a hardware implementation

Semi-Analytic Techniques for Fast MATLAB Simulations

Semi-analytic techniques are a powerful tool for the analysis of complex systems. In the semi-analytic framework, the knowledge of the system under analysis is exploited to reduce the computational load and complexity that full Monte Carlo simulations would require. In this way, the strengths of both analytical and Monte Carlo methods are effectively combined. The main goal of this chapter is to provide a general overview of semi-analytic techniques for the simulation of communications systems. Specific emphasis is given to their implementation in Matlab and two examples from the communications and navigation context are analyzed in detail. More specifically, the impact of RF interference on acquisition and tracking, the two main stages of a Global Navigation Satellite System (GNSS) receiver, are considered. Although semi-analytic techniques have been considered in textbooks on simulations, limited attention has been provided to their analysis and implementation. This chapter mainly focuses on these two aspects with specific emphasis on the potential of the Matlab environment for their implementation.JRC.G.6-Security technology assessmen

A METHOD OF SIDE-PEAK MITIGATION APPLIED TO BINARY OFFSET CARRIER MODULATED GNSS SIGNALS TRACKING APPLIED IN GNSS RECEIVERS

ABSTRACT In this study, a new method of signal tracking technique in Global Navigation Satellite System (GNSS) is proposed. It is based on a combination of the autocorrelation function (ACF) with another cross correlation function in order to eliminate or reduce the power of the side peaks in ACF of Binary Offset Carrier (BOC) modulated signals. These types of modulated signals are adopted by both GNSSs like the modernized Global Positioning System (GPS) and Galileo. Moreover, this method still keep the sharp of main peak of ACF in order to maintain the advantage of BOC(n,n) signals in code tracking and multipath mitigation. In the proposed method, the output of the discriminator in delay tracking loop has no false lock point. The performance of multipath mitigation of the proposed method is better than Narrow Correlator method. The good performance of the proposed scheme in multipath mitigation has been tested using simulation results

Advanced Multipath Mitigation Techniques for Satellite-Based Positioning Applications

Multipath remains a dominant source of ranging errors in Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS) or the future European satellite navigation system Galileo. Multipath is generally considered undesirable in the context of GNSS, since the reception of multipath can make significant distortion to the shape of the correlation function used for time delay estimation. However, some wireless communications techniques exploit multipath in order to provide signal diversity though in GNSS, the major challenge is to effectively mitigate the multipath, since we are interested only in the satellite-receiver transit time offset of the Line-Of-Sight (LOS) signal for the receiver's position estimate. Therefore, the multipath problem has been approached from several directions in order to mitigate the impact of multipath on navigation receivers, including the development of novel signal processing techniques. In this paper, we propose a maximum likelihood-based technique, namely, the Reduced Search Space Maximum Likelihood (RSSML) delay estimator, which is capable of mitigating the multipath effects reasonably well at the expense of increased complexity. The proposed RSSML attempts to compensate the multipath error contribution by performing a nonlinear curve fit on the input correlation function, which finds a perfect match from a set of ideal reference correlation functions with certain amplitude(s), phase(s), and delay(s) of the multipath signal. It also incorporates a threshold-based peak detection method, which eventually reduces the code-delay search space significantly. However, the downfall of RSSML is the memory requirement which it uses to store the reference correlation functions. The multipath performance of other delay-tracking methods previously studied for Binary Phase Shift Keying-(BPSK-) and Sine Binary Offset Carrier- (SinBOC-) modulated signals is also analyzed in closed loop model with the new Composite BOC (CBOC) modulation chosen for Galileo E1 signal. The simulation results show that the RSSML achieves the best multipath mitigation performance in a uniformly distributed two-to-four paths Rayleigh fading channel model for all three modulated signals

Multi-GNSS signals acquisition techniques for software defines receivers

Any commercially viable wireless solution onboard Smartphones should resolve the technical issues as well as preserving the limited resources available such as processing and battery. Therefore, integrating/combining the process of more than one function will free up much needed resources that can be then reused to enhance these functions further. This thesis details my innovative solutions that integrate multi-GNSS signals of specific civilian transmission from GPS, Galileo and GLONASS systems, and process them in a single RF front-end channel (detection and acquisition), ideal for GNSS software receiver onboard Smartphones. During the course of my PhD study, the focus of my work was on improving the reception and processing of localisation techniques based on signals from multi-satellite systems. I have published seven papers on new acquisition solutions for single and multi-GNSS signals based on the bandpass sampling and the compressive sensing techniques. These solutions, when applied onboard Smartphones, shall not only enhance the performance of the GNSS localisation solution but also reduce the implementation complexity (size and processing requirements) and thus save valuable processing time and battery energy. Firstly, my research has exploited the bandpass sampling technique, if being a good candidate for processing multi-signals at the same time. This portion of the work has produced three methods. The first method is designed to detect the GPS, Galileo and GLONASS-CDMA signals’ presence at an early stage before the acquisition process. This is to avoid wasting processing resources that are normally spent on chasing signals not present/non-existent. The second focuses on overcoming the ambiguity when acquiring Galileo-OS signal at a code phase resolution equal to 0.5 Chip or higher and this achieved by multiplying the received signal with the generated sub-carrier frequency. This new conversion saves doing a complete correlation chain processing when compared to conventionally used methods. The third method simplifies the joining implementation of the Galileo-OS data-pilot signal acquisition by constructing an orthogonal signal so as to acquire them in a single correlation chain, yet offering the same performance as using two correlation chains. Secondly, the compressive sensing technique is used to acquire multi-GNSS signals to achieve computation complexity reduction over correlator based methods, like Matched Filter, while still maintaining acquisition integrity. As a result of this research work, four implementation methods were produced to handle single or multi-GNSS signals. The first of these methods is designed to change dynamically the number and the size of the required channels/correlators according to the received GPS signal-power during the acquisition process. This adaptive solution offers better fix capability when the GPS receiver is located in a harsh signal environment, or it will save valuable processing/decoding time when the receiver is outdoors. The second method enhances the sensing process of the compressive sensing framework by using a deterministic orthogonal waveform such as the Hadamard matrix, which enabled us to sample the signal at the information band and reconstruct it without information loss. This experience in compressive sensing led the research to manage more reduction in terms of computational complexity and memory requirements in the third method that decomposes the dictionary matrix (representing a bank of correlators), saving more than 80% in signal acquisition process without loss of the integration between the code and frequency, irrespective of the signal strength. The decomposition is realised by removing the generated Doppler shifts from the dictionary matrix, while keeping the carrier frequency fixed for all these generated shifted satellites codes. This novelty of the decomposed dictionary implementation enabled other GNSS signals to be combined with the GPS signal without large overhead if the two, or more, signals are folded or down-converted to the same intermediate frequency. The fourth method is, therefore, implemented for the first time, a novel compressive sensing software receiver that acquires both GPS and Galileo signals simultaneously. The performance of this method is as good as that of a Matched Filter implementation performance. However, this implementation achieves a saving of 50% in processing time and produces a fine frequency for the Doppler shift at resolution within 10Hz. Our experimental results, based on actual RF captured signals and other simulation environments, have proven that all above seven implementation methods produced by this thesis retain much valuable battery energy and processing resources onboard Smartphones

An Efficient Method for GPS Multipath Mitigation Using the Teager-Kaiser-Operator-based MEDLL

An efficient method for GPS multipath mitigation is proposed. The motivation for this proposed method is to integrate the Teager-Kaiser Operator (TKO) with the Multipath Estimating Delay Lock Loop (MEDLL) module to mitigate the GPS multipath efficiently. The general implementation process of the proposed method is that we first utilize the TKO to operate on the received signal’s Auto-Correlation Function (ACF) to get an initial estimate of the multipaths. Then we transfer the initial estimated results to the MEDLL module for a further estimation. Finally, with a few iterations which are less than those of the original MEDLL algorithm, we can get a more accurate estimate of the Line-Of-Sight (LOS) signal, and thus the goal of the GPS multipath mitigation is achieved. The simulation results show that compared to the original MEDLL algorithm, the proposed method can reduce the computation load and the hardware and/or software consumption of the MEDLL module, meanwhile, without decreasing the algorithm accuracy