Jean-Philippe Montillet

UWB Location and tracking for wireless embedded networks

In this paper, we investigate the performance of different position estimation methods which make use of time-of-arrival (TOA) of ultra wideband (UWB) signals for low cost/low complexity UWB systems. We first propose a simple and robust two-stage, non-coherent TOA estimation approach. We then explore positioning algorithms utilizing both noniterative and iterative techniques. A review of positioning in distributed networks is also performed and a positioning algorithm is proposed for node location in multi-hop distributed networks. Furthermore, we consider smoothing techniques to improve accuracy when tracking moving objects and we propose the use of sinc functions to smooth the estimate of the mobile position in order to achieve both good accuracy and low complexity. The system modelled and investigated corresponds to an actual test environment in a ski field where skiers are tracked.

Synchronization, TOA and position estimation for low-complexity LDR UWB devices

The paper provides an evaluation of a non-coherent UWB system, which is suitable for low complexity, cost and data rate UWB wireless sensor networks with positioning capability. Synchronization and time of arrival (TOA) estimation is performed using a non-coherent energy collection method. Coarse and fine synchronization are performed to identify the energy clusters and refine the energy collection window respectively. The effect of the integration window size is evaluated for both TOA estimation and position estimation. Direct method (DM) and Davidon-Fletcher-Powell (DFP) algorithms are implemented for position estimation. The result shows the possibility of attaining sub-meter performance using a low complexity and cost device.

Extracting White Noise Statistics in GPS Coordinate Time Series

The noise in GPS coordinate time series is known to follow a power-law noise model with different components (white noise, flicker noise, and random walk). This work proposes an algorithm to estimate the white noise statistics, through the decomposition of the GPS coordinate time series into a sequence of sub time series using the empirical mode decomposition algorithm. The proposed algorithm estimates the Hurst parameter for each sub time series and then selects the sub time series related to the white noise based on the Hurst parameter criterion. Both simulated GPS coordinate time series and real data are employed to test this new method; the results are compared to those of the standard (CATS software) maximum-likelihood (ML) estimator approach. The results demonstrate that this proposed algorithm has very low computational complexity and can be more than 100 times faster than the CATS ML method, at the cost of a moderate increase of the uncertainty (~5%) of the white noise amplitude. Reliable white noise statistics are useful for a range of applications including improving the filtering of GPS time series, checking the validity of estimated coseismic offsets, and estimating unbiased uncertainties of site velocities. The low complexity and computational efficiency of the algorithm can greatly speed up the processing of geodetic time series.

On the improvements of the single point positioning accuracy with Locata technology

This work focuses on the performances of Locata technology in single point positioning using different firmware versions (v2.0 and v4.2). The main difference is that the Locata transmitters with firmware v2.0 are single frequency, whereas in the v4.2, they are dual frequency. The performance of the different firmware versions has been measured in different environments including an urban canyon-like environment and a more open environment on the roof of the Nottingham Geospatial Building. The results obtained with firmware v4.2 show that with more available signals, cycle slips can be more easily detected, together with the improvement of the detection of multipath fading on the received signal. As a result, the noise level on the carrier phase measurements recorded with firmware v4.2 is equal on average to a third of the level of noise on the measurements recorded with firmware v2.0. In addition, with either firmware, the accuracy of the position is at the sub-centimeter level on the East and North coordinates. The Up coordinate accuracy is generally less accurate and more sensitive to the geometry of the network in our experiments. We then show the importance of the geometry of the Locata network on the accuracy of Locata positioning system through the demonstration of the relationship between the dilution of precision value and the confidence ellipse. We also demonstrate that the model of the noise on the Locata coordinates is a white Gaussian noise with the help of the autocorrelation function. To some extent, this technique can help to detect whether the Wi-Fi technology is interfering with the Locata technology and degrades the positioning accuracy.

Leaky LMS Algorithm and Fractional Brownian Motion Model for GNSS Receiver Position Estimation

This paper presents a new approach for smoothing long time series of position estimates of ground GNSS (global navigation satellite system) receivers. The fractional Brownian motion (fBm) model is employed to describe the position coordinate estimates that have long-range dependencies. A new and low-complexity method is proposed to estimate the Hurst parameter and the simulation results show that the new method achieves good accuracy and low complexity. A modified leaky least mean squares (ML-LMS) estimator is proposed to filter the long time series of the position coordinate estimates, which uses the Hurst parameter estimates to update the filter tap weights. Simulation results demonstrate that this ML-LMS estimator outperforms the classic LMS estimator considerably in terms of both accuracy and convergence.

Modeling Geodetic Processes with Levy \(\alpha \)-Stable Distribution and FARIMA

Over the last years the scientific community has been using the autoregressive moving average (ARMA) model in the modeling of the noise in global positioning system (GPS) time series (daily solution). This work starts with the investigation of the limit of the ARMA model which is widely used in signal processing when the measurement noise is white. Since a typical GPS time series consists of geophysical signals (e.g., seasonal signal) and stochastic processes (e.g., coloured and white noise), the ARMA model may be inappropriate. Therefore, the application of the fractional auto-regressive integrated moving average (FARIMA) model is investigated. The simulation results using simulated time series as well as real GPS time series from a few selected stations around Australia show that the FARIMA model fits the time series better than other models when the coloured noise is larger than the white noise. The second fold of this work focuses on fitting the GPS time series with the family of Levy α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}

Modified Leaky LMS Algorithms Applied to Satellite Positioning

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Extracting Colored Noise Statistics in Time Series via Negentropy

\end{document}-stable distributions. Using this distribution, a hypothesis test is developed to eliminate effectively coarse outliers from GPS time series, achieving better performance than using the rule of thumb of n\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}