Mohammed Benjelloun

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.

Joint segmentation of the wind speed and direction

We present in this article a Bayesian estimation method for the joint segmentation of a set of piecewise stationary processes. The estimate we propose is based on the maximization of the posterior distribution of the change instants conditionally to the process parameter estimation. It is defined as a penalized contrast function with a first term related to the fit to the observation and a second term of penalty. The expression of the contrast function is deduced from the log-likelihood of the parametric distribution that models the statistic evolution of processes in the stationary segments. In the case of joint segmentation the penalty term is deduced from the prior law of change instants. It is composed of parameters that guide the number and the position of changes and of parameters that will bring prior information on the joint behavior of processes. This work is applied to the estimation of wind statistics parameters. We use data available from a cup anemometer and a wind vane, supposed to be piecewise stationary. The contrast function is deduced from the circular Von Mises distribution for the wind direction and from the log-normal distribution for the speed. The feasibility and the contribution of our method are shown on synthetic and real data.

A k-Means Clustering Algorithm Initialization for Unsupervised Statistical Satellite Image Segmentation

The increasing availability of satellite images acquired periodically by satellite on different area, makes it extremely interesting in many applications. In deed, the recent construction of multi and hyper spectral images will provide detailed data with information in both the spatial and spectral domain. This data shows great promise for remote sensing applications ranging from environmental and agricultural to national security interests. The exploitation of these images requires the use of different approach, and notably these founded on the unsupervised statistical segmentation principle. Indeed these methods that exploit the statistical images attributes offer some convincing and encouraging results, under the condition to have an optimal initialization step. Indeed, in order to assure a better convergence of the different images attributes, the unsupervised segmentation approaches, require a fundamental initialization step. We will present in this paper a k-means clustering algorithm and describe its importance in the initialization of the unsupervised satellite image segmentation

Non-linear matched filtering for object detection and tracking

In this paper, we present a method for detecting and tracking rigid moving objects in a monocular image sequence. The originality of this method lies in a state modelling of this estimation problem which is solved in an unified way. This hybrid estimation problem leads to non-linear state equations that are solved by the particle method. A particle filter is set for each shape model (modes). It estimates the motion and position parameters and tracks the object in the sequence. The algorithm also computes at each time the probability of all modes. This method is then applied to synthetic and real image sequences in order to evaluate the estimation accuracies and the robustness of the tracking procedure.

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.

A hybrid fusion system applied to off-line detection and change-points estimation

In this paper we investigate the problem of off-line detection and estimation of change-point instants on data provided by two sensors. In this context sensors synchronization, that provides simultaneous change-point instants on the data, is in practice a constraint hard to maintain. The contribution of this work is the proposition of a hybrid fusion system that performs as well as the centralized fusion detector respectively optimal for simultaneous and not simultaneous change. The system we propose is composed of two GLR tests (Generalized Likelihood Ratio) defined as centralized fusion detectors for the two configurations of change-point (simultaneous and not simultaneous). Decisions of these fusion detectors are combined in a fusion operator. The system is hybrid (centralized and distributed) because the distributed decisions supplied by the centralized fusion systems are combined in a global fusion operator. The contribution of our method is shown on synthetic data. The application to the treatment of a real multi-carrier GPS signal shows the feasibility of the method.

Information fusion sensors for vehicle localization using evidence theory

We describe a possible structure for a localization system providing 2-D position. The system uses global positioning system (GPS) sensor measures as well as odometric data and a digital road map to maintain a correct estimate of the location of a vehicle. The measurement results from these differences sensors are fused by using the evidence theory. The objective is to reduce the position error. Experimental results show the potential of the utilization of evidence theory to the localization problem.

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.