## Section: Bilateral Contracts and Grants with Industry

### Bilateral grants with industry

See 4.1.

#### Hybrid indoor navigation — PhD project at CEA LETI

Participants : François Le Gland, Kersane Zoubert--Ousseni.

This is a collaboration with Christophe Villien (CEA LETI, Grenoble).

The issue here is user localization, and more generally localization–based services (LBS). This problem is addressed by GPS for outdoor applications, but no such general solution has been provided so far for indoor applications. The desired solution should rely on sensors that are already available on smartphones and other tablet computers. Inertial solutions that use MEMS (microelectromechanical system, such as accelerometer, magnetometer, gyroscope and barometer) are already studied at CEA. An increase in performance should be possible, provided these data are combined with other available data: map of the building, WiFi signal, modeling of perturbations of the magnetic field, etc. To be successful, advanced data fusion techniques should be used, such as particle filtering and the like, to take into account displacement constraints due to walls in the building, to manage several possible trajectories, and to deal with rather heterogeneous information (map, radio signals, sensor signals).

The main objective of this thesis is to design and tune localization algorithms that will be tested on platforms already available at CEA. Special attention is paid to particle smoothing and particle MCMC algorithms, to exploit some very precise information available at special time instants, e.g. when the user is clearly localized near a landmark point.

In some applications, real time estimation of the trajectory is not needed, and a post processing framework may provide a better estimation of this trajectory. In [57], we present and compare three different algorithms to improve a real time trajectory estimation. Actually, two different smoothing algorithms and the Viterbi algorithm are implemented and evaluated. These methods improve the regularity of the estimated trajectory by reducing switches between hypotheses.

Post processing indoor navigation is interesting, for example to develop crowdsourcing analysis. The post processing framework allows to provide a better estimation than in a real time framework. The main contribution of [17] is to present a piecewise parametrization using IMU (inertial measurement unit) and RSS (received signal strength) measurements only, which lead to an optimization problem. A Levenberg–Marquardt algorithm improved with simulated annealing and an adjustment of RSS measurements data leads to a good estimation (55% of the error less than 5 meters) of the trajectory.

#### Bayesian tracking from raw data — CIFRE grant with DCNS Nantes

Participants : François Le Gland, Audrey Cuillery.

This is a collaboration with Dann Laneuville (DCNS Nantes).

After the introduction of MHT (multi–hypothesis tracking) techniques
in the nineties, multitarget tracking has recently seen promising
developpments with the introduction of new algorithms such
as the PHD (probability hypothesis density) filter [50], [56]
or the HISP (hypothesised filter for independent stochastic populations)
filter [40].
These techniques provide a unified multitarget model in a Bayesian
framework [54],
which makes it possible to design recursive estimators of
a *multitarget probability density*.
Two main approaches can be used here: sequential Monte Carlo (SMC, also
kown as particle filtering), and Gaussian mixture (GM).
A third approach, based on discretizing the state–space in a possibly
adaptive way, could also be considered despite its larger computational load.
These methods are well studied and provide quite good results
for *contact output* data, which correspond to regularly spaced
measurements of targets with a large SNR (signal–to–noise ratio).
Here, the data is processed (compared with a detection threshold) in each
resolution cell of the sensor, so as to provide a list of detections at
a given time instant.
Among these methods, the HISP filter has the best performance/computational
cost ratio.

However, these classical methods are unefficient for targets with a low SNR,
e.g. targets in far range or small targets with a small detection
probability.
For such targets, preprocessing (thresholding) the data is not a good idea,
and a much better idea is to feed a tracking algorithm with the
raw *sensor output* data directly.
These new methods [24] require a precise modeling of the
sensor physics and a direct access to the radar (or the sonar) raw data,
i.e. to the signal intensity level in each azimuth/range cell.
Note that these new methods seem well suited to new types of sensors such
as lidar, since manufacturers do not integrate a detection module and do
provide raw images of the signal intensity level in each azimuth/range cell.

The objective of the thesis is to study and design a tracking algorithm using raw data, and to implement it on radar (or sonar, or lidar) real data.