Audrey Giremus

A Particle Filtering Approach for Joint Detection/Estimation of Multipath Effects on GPS Measurements

Multipath propagation causes major impairments to global positioning system (GPS) based navigation. Multipath results in biased GPS measurements, hence inaccurate position estimates. In this paper, multipath effects are considered as abrupt changes affecting the navigation system. A multiple model formulation is proposed whereby the changes are represented by a discrete valued process. The detection of the errors induced by multipath is handled by a Rao-Blackwellized particle filter (RBPF). The RBPF estimates the indicator process jointly with the navigation states and multipath biases. The interest of this approach is its ability to integrate a priori constraints about the propagation environment. The detection is improved by using information from near future GPS measurements at the particle filter (PF) sampling step. A computationally modest delayed sampling is developed, which is based on a minimal duration assumption for multipath effects. Finally, the standard PF resampling stage is modified to include an hypothesis test based decision step

A Rao-Blackwellized particle filter for INS/GPS integration

The localization performance of a navigation system can be improved by coupling different types of sensors. The paper focuses on INS-GPS integration. INS and GPS measurements allow a non-linear state space model, which is appropriate to particle filtering, to be defined. This model being conditionally linear Gaussian, a Rao-Blackwellization procedure can be applied to reduce the variance of the estimates.

Joint Particle Filter and UKF Position Tracking in Severe Non-Line-of-Sight Situations

The performance of localization techniques in a wireless communication system is severely impaired by biases induced in the range and angle measures because of the non-line-of-sight (NLOS) situation, caused by obstacles in the transmitted signal path. However, the knowledge of the line-of-sight (LOS) or NLOS situation for each measure can improve the final accuracy. This paper studies the localization of mobile terminals (MT) based on a Bayesian model for the LOS-NLOS evolution. This Bayesian model does not require having a minimum number of LOS measures at each acquisition. A tracking strategy based on a particle filter (PF) and an unscented Kalman filter (UKF) is used both to estimate the LOS-NLOS situation and the MT kinetic variables (position and speed). The approach shows a remarkable reduction in positioning error and a high degree of scalability in terms of performance versus complexity.

An improved regularized particle filter for GPS/INS integration

Hybridization techniques receive a renewed interest due to recent navigation systems such as Galileo. Hybridization takes advantage of the complementarity of different sensor types to increase navigation performance. This study focuses on the integration of the Global Positioning System (GPS) and the inertial navigation systems (INS). GPS allows to compensate for the long term drift of INS estimates, while aided INS provide a solution in case of GPS signal blocking. The GPS/INS coupling is performed by a nonlinear filtering approach whereby GPS measurements are used to correct INS estimates. However, a classical particle filter is bound to diverge due to the dynamics of the unknown parameters. Indeed, the state model has a small process noise and is exponentially unstable. The regularized particle filter presented in N. Oudjane et al. (2000) allows to overcome this limitation at the expense of an increased estimation variance. This study proposes to introduce a Metropolis-Hastings step to accept/reject the particles updated by the regularization process. The method is shown to prevent the degeneracy without introducing additional noise on the estimates.

Continuous-Discrete Extended Kalman Filter on Matrix Lie Groups Using Concentrated Gaussian Distributions

In this paper we generalize the continuous-discrete extended Kalman filter (CD-EKF) to the case where the state and the observations evolve on connected unimodular matrix Lie groups. We propose a new assumed density filter called continuous-discrete extended Kalman filter on Lie groups (CD-LG-EKF). It is built upon a geometrically meaningful modeling of the concentrated Gaussian distribution on Lie groups. Such a distribution is parametrized by a mean and a covariance matrix defined on the Lie group and in its associated Lie algebra respectively. Our formalism yields tractable equations for both non-linear continuous time propagation and discrete update of the distribution parameters under the assumption that the posterior distribution of the state is a concentrated Gaussian. As a side effect, we contribute to the derivation of the first and second order differential of the matrix Lie group logarithm using left connection. We also show that the CD-LG-EKF reduces to the usual CD-EKF if the state and the observations evolve on Euclidean spaces. Our approach leads to a systematic methodology for the design of filters, which is illustrated by the application to a camera pose filtering problem with observations on Lie group. In this application, the CD-LG-EKF significantly outperforms two constrained non-linear filters (one based on a linearization technique and the other on the unscented transform) applied on the embedding space of the Lie group.

Automated parameter estimation of the hodgkin-huxley model using the differential evolution algorithm: Application to neuromimetic analog integrated circuits

We propose a new estimation method for the characterization of the Hodgkin-Huxley formalism. This method is an alternative technique to the classical estimation methods associated with voltage clamp measurements. It uses voltage clamp type recordings, but is based on the differential evolution algorithm. The parameters of an ionic channel are estimated simultaneously, such that the usual approximations of classical methods are avoided and all the parameters of the model, including the time constant, can be correctly optimized. In a second step, this new estimation technique is applied to the automated tuning of neuromimetic analog integrated circuits designed by our research group. We present a tuning example of a fast spiking neuron, which reproduces the frequency-current characteristics of the reference data, as well as the membrane voltage behavior. The final goal of this tuning is to interconnect neuromimetic chips as neural networks, with specific cellular properties, for future theoretical studies in neuroscience.

Joint detection/estimation of multipath effects for the Global Positioning System

Multipaths cause major impairments to navigation with the Global Positioning System (GPS). Indeed, non-line-of-sight (NLOS) propagation is well known to bias GPS position estimates. A recent methodology has been proposed to overcome this limitation by estimating simultaneously the kinematic states and the multipath biases all along the observation interval. However, multipaths clearly occur relatively infrequently during time intervals of fixed duration. The paper studies a particle filtering algorithm for joint detection and estimation of multipath biases. A Rao-Blackwellized approach allows estimation of the kinematic states by extended Kalman filters, whereas multipath detection is achieved by an appropriate fixed lag particle filter.

Calibration of an inertial-magnetic measurement unit without external equipment, in the presence of dynamic magnetic disturbances

Inertial-magnetic measurement units are inexpensive sensors, widely used in electronic systems (smartphones, GPS, micro-UAV, etc). However the precision of these sensors is highly dependent on their calibration. This article proposes a complete solution to calibrate the sensors (accelerometers, gyrometers and magnetometers), the inter-sensor rotations and the dynamic disturbances of the magnetic field due to the immediate environment. Contrary to most of the existing techniques, the proposed method does not necessitate any external equipment, apart from the sensors already included in the system. The calibration can be performed by hand manipulation by the final user. Simulations and experiments show the advantages of the proposed approach.

Parameter estimation of the Hodgkin-Huxley model using metaheuristics: Application to neuromimetic analog integrated circuits

In 1952 Hodgkin and Huxley introduced the voltage-clamp technique to extract the parameters of the ionic channel model of a neuron. Although this method is widely used today, it has a lot of disadvantages. In this paper, we propose an alternative approach to the estimation method of the voltage-clamp technique using metaheuristics such as simulated annealing, genetic algorithms and differential evolution. This method avoids approximations of the original technique by simultaneously estimating all the parameters of a single ionic channel with a single fitness function. To compare the different methods, we apply them on measurements from a neuromimetic integrated circuit. This circuit, due to its analog behavior, provides us noisy data like a biological system. Therefore we can validate the efficiency of our method on experimental-like data.

A Fixed-Lag Particle Filter for the Joint Detection/Compensation of Interference Effects in GPS Navigation

Interference are among the most penalizing error sources in global positioning system (GPS) navigation. So far, many effort has been devoted to developing GPS receivers more robust to the radio-frequency environment. Contrary to previous approaches, this paper does not aim at improving the estimation of the GPS pseudoranges between the mobile and the GPS satellites in the presence of interference. As an alternative, we propose to model interference effects as variance jumps affecting the GPS measurements which can be directly detected and compensated at the level of the navigation algorithm. Since the joint detection/estimation of the interference errors and motion parameters is a highly non linear problem, a particle filtering technique is used. An original particle filter is developed to improve the detection performance while ensuring a good accuracy of the positioning solution.