Jean-Bernard Choquel

A recursive fusion filter for angular data

Many practical application in the field of robotic and perception are using angular data. In this work we present a multi-sensor multi-temporal data fusion filter for angular data. Most of the time, statistic filters, are designed on linear domain. In this work we propose a recursive filter defined on the circular domain with a von Mises distribution. In our application we consider a set of measurement taking at different instants and provided by different sensors. The sequential implementation of the recursive fusion filter we propose is deduced from the a posteriori distribution of the state vector, containing the angular direction and velocity. Temporal measurements are coming from several sensors. The feasibility and the contribution of our method are shown on synthetic data.

A new probabilistic and entropy fusion approach for management of information sources

This paper describes a new probabilistic fusion methodology based on Shannon’s entropy, whose goal is to reduce the combination space by explicitly representing the notions of source redundancy and source complementarity in form of entropy measures. This fusion methodology called Entropy Fusion Model (EFM) is defined and implemented in three steps: modeling step, combination step and decision step. The EFM approach shows how an information fusion problem can be formulated by using entropy criteria minimization as a basis for guiding the fusion system to the best fused information. The main advantage of such a fusion approach is to optimize the choice of measurements provided by information sources in order to improve the performance of the information fusion system. Experimental results from an application to mobile robotics are presented illustrating the performances and the robustness of the Entropy Adaptative Aggregation (EA2) resulting algorithm.

Normalized GNSS Interference Pattern Technique for Altimetry

It is well known that reflected signals from Global Navigation Satellite Systems (GNSS) can be used for altimetry applications, such as monitoring of water levels and determining snow height. Due to the interference of these reflected signals and the motion of satellites in space, the signal-to-noise ratio (SNR) measured at the receiver slowly oscillates. The oscillation rate is proportional to the change in the propagation path difference between the direct and reflected signals, which depends on the satellite elevation angle. Assuming a known receiver position, it is possible to compute the distance between the antenna and the surface of reflection from the measured oscillation rate. This technique is usually known as the interference pattern technique (IPT). In this paper, we propose to normalize the measurements in order to derive an alternative model of the SNR variations. From this model, we define a maximum likelihood estimate of the antenna height that reduces the estimation time to a fraction of one period of the SNR variation. We also derive the Cramér–Rao lower bound for the IPT and use it to assess the sensitivity of different parameters to the estimation of the antenna height. Finally, we propose an experimental framework, and we use it to assess our approach with real GPS L1 C/A signals.

GNSS dataless signal tracking with a delay semi-open loop and a phase open loop

In this article we propose to process the code and phase delay of a dataless GNSS signal in open and semi-open loops. The aims of this processing are to get an accurate estimate of the phase and code delays by integration of the GNSS signal. We show that in an open loop the code delay evolution is a piecewise stationary process and we propose to model the phase delay as a circular random variable distributed according to a von Mises distribution. In this context the code tracking is realised on the GNSS signal by estimating the abrupt changes in the code discriminator values. We propose a Bayesian modeling of the problem in order to define the change point estimator. The proposed estimator involves in its definition the inaccurate prior information of Doppler and signal to noise density ratio provided by the phase delay tracking loop. Furthermore we propose a circular Bayesian modeling of the observations provided by the phase open loop. From this model, we derive a circular recursive filter for the estimation of phase delay and frequency of the carrier. The proposed tools are assessed using synthetic and real data. Highlights? We propose a Delay Semi Open Loop for the tracking of the code of a GNSS signal. ? We propose a Bayesian change point detector for this code tracking loop. ? We propose a Phase Open Loop for the tracking of the phase of a GNSS signal. ? We propose a Bayesian circular filter for this phase tracking loop. ? The proposed methods are assessed on synthetic and real data.

Circular data processing tools applied to a Phase Open Loop architecture for multi-channels signals tracking

This paper proposes circular data processing tools dedicated to the tracking of the phase of GNSS signals in a Phase Open Loop, particularly in case of multi-channel signal structure. The objective of processing the phase in an open loop is to avoid time-correlation between two successive measurements. This allows the use of loop filters in order to smooth the measurements without producing unwanted oscillations in the phase estimations. In order to process the angular values produced by the Phase Open Loop, the choice had been made to develop a filter and a fusion operator in a Bayesian framework with circular statistics distributions. The proposed tools are assessed on synthetic and real data.

Beach soil moisture measurement with a land reflected GPS bistatic radar technique

In this work we investigate the potential for sensing beach soil moisture with the L band GPS bistatic radar concept. Characterisation of sediment surface (properties like humidity) is indeed important to better understand morphodynamic activity of intertidal part of beaches. In our approach we compare the direct GPS Signal to Noise Ratio with the reflected one in order to measure the soil moisture. We use a bit grabber to digitize and store the GPS L1 carrier (1.5 Ghz) samples. In this context the signal processing is off-line. In this work we proposed a model of the received signal after demodulation and demultiplexing. We deduce from this model a MAP estimate of the navigation message and of the signal SNR. In our case the signal model is a piecewise stationary process with change instants at bit edge position. We present preliminary SNR measurement with this technique for the discrimination of water and humid sand.

Accurate Pseudorange Estimation by Means of Code and Phase Delay Integration: Application to GNSS-R Altimetry

This article presents a new estimator of the pseudorange between a global navigation satellite systems (GNSS) satellite and a stand-alone receiver, and its application to GNSS-reflectometry (GNSS-R) altimetry. In our approach, we use as independent observations the difference between the code delay provided by successive acquisitions of the GNSS signal and the code delay truncated using the phase and Doppler information. We show that the mean value of these observations is an estimate of the first pseudorange of the observed time series. The ability of the proposed estimator to provide the centimeter accuracy is theoretically evaluated. In ground-based altimetry applications using GNSS signals, the direct pseudorange and the pseudorange associated to the reflected signal can be assumed to have the same dynamic evolution. Based on this assumption, the proposed estimate uses as observations the difference between the code delays obtained by successive acquisitions of both signals, without using phase measurement. The assessment of the method for ground-based GNSS-R is realized with real data.

A recursive change point estimate of the wind speed and direction

Mean wind speeds and mean wind directions are important input parameters for environmental models and applications. In many practical cases, anemometer sensor provides measurements of direction and wind speed, supposed to be Gaussian and piecewise stationary. In this work we use a von Mises distribution and a Log-normal distribution in order to model sensor data with distributions having a physical reality. We derive from these distributions an “on-line” recursive estimate of the change point in the wind speed and direction processes. We present numerical results obtained using synthetic and real data to demonstrate the ability of these estimates.

Cycle slip detection and repair with a circular on-line change-point detector

In this article, we propose a circular change-point detector for on-line processing of the phase and the frequency of a GPS-L1 signal. The aims of this processing are to get an accurate estimation of the phase and to use it to get centimeter precise position estimates every millisecond. We propose to track the phase of the GPS signal in an open loop and the frequency in a semi-open loop. In an open loop, the phase delay evolves as a circular random variable. Furthermore, the phase is subject to cycle slips. These abrupt changes must be detected and repaired. We propose a circular generalized likelihood test for the on-line detection of changes in the phase measurements. With the estimation and detection being non-linear, we propose a particle filter defined according to the circular von Mises distribution for the estimation of the phase and frequency. The proposed architecture is assessed using synthetic and real data.

Bayesian Change-Points Estimation Applied to GPS Signal Tracking

A hierarchical Bayesian model is applied to off-line segmentation of the GPS signal discriminator. The purpose of this work is to estimate the code delay of the receiving GPS CDMA code in order to retime the local receiver code and to estimate the pseudorange satellite receiver. The goal of our approach is to obtain a high-rate accurate positioning in the dynamic navigation case. We show that the behaviour of the coherent discriminator of a GPS pilot channel can be modelized by a piecewise stationary process. In our approach the discriminator behaviour in each stationary segment is approximated by a constant acceleration model, and the code delay at each end of the segments is known. The interest of this approach is that we use the coherent values of the discriminator in each segment to estimate the change instants of the process and to get in this case an accurate estimation of the code delays. In this context, a simultaneous estimation of the change instants is considered. We define the a posteriori distribution which integrates in its expression the signal change instants and the parameters of its statistical model. The proposed model leads after marginalization to a penalized contrast function that we minimize to estimate the discriminator change instants. The interest of the proposed model is that we can integrate in our estimate prior information on the roughly known values of the signal-to-noise ratio and relative speed satellite receiver. The potential of the proposed method is shown on experimentations realized on synthetic and real data for millisecond receiver localization.