The task of the Imego signal processing group is to extract useful
information from sensor signals and to control the sensors so that they
operate optimally. By handling the signals in all steps from the physical
sensor to real-time signal processing to the graphical user interface, we
design measurement systems that are very accurate, very fast but also
easy to set up, operate and interpret.
Our tools for offline modeling, analysis and design include Matlab,
Simulink, C/C++, and of course, old-fashioned pen-and-paper. For
real-time embedded implementation we use high performance programmable
digital hardware (FPGAs), DSP processors, microcontrollers, and custom
integrated circuits — all adapted to the demands of the specific
application.
Real time signal processing
Our experience in real time signal processing and feedback control
systems is key for achieving state-of-the art sensor performance. For
instance, we stabilize our micro-mechanical inertial sensors using
digital control systems, perform advanced filtering (phase shaping
filters, noise reduction, equalization etc.), and extract the useful
sensor signals by digital coherent detection methods — all in real time.
Before the digital control system can operate, the signals, however, have
to be transferred from the analog to the digital domain. At Imego, we
have a deep understanding of the issues relating to the critical
transition from A-to-D (and vice versa). We are experienced in custom
mixed-signal electronics, digital noise shaping, interpolation /
decimation as well as optical sampling and digitization at gigahertz
sample rates.
System modeling and analysis
In order to extract useful information from the sensor signals, it is
necessary to have accurate models of the sensors, the electronics, the
noise, and the specific application dynamics. Our expertise in system
identification allows us to design efficient models that let us
characterize physical parameters from the sensor response. After thorough
calibration we also use the sensor models to compensate for e.g.
temperature dependencies and nonlinearities.
Based on our experience in optical research, we can design, model and
optimize imaging systems for specific measurement tasks, taking both
optical aberrations and image sensor deficiencies into account. After
calibration and compensation we can achieve a positional resolution much
better than one image sensor “pixel”, which we exploit for instance for
3D-triangulation measurements.
Estimation and pattern recognition
The use of statistical estimation techniques (e.g. Kalman filtering)
allows us to extract physical quantities that cannot be directly
observed, to estimate measurement uncertainties and to fuse information
from several sensors and/or points in time. Using our custom nonlinear
estimation software we can easily adapt to a specific application,
whereby we combine knowledge of the system, noise, and the expected
behavior to improve the estimation accuracy. We are particularly
experienced in the field of inertial navigation, where we use Kalman
estimation supported by application-specific kinematic models to
accurately follow the position and orientation of the device from
measurements of the acceleration and the rotation rate.
To further enhance the quality of estimation we also employ pattern
recognition methods and machine-learning to adaptively choose from
different process models. In the case of navigation, these models would
correspond to different modes of motion (“at rest”, “constant velocity”,
“walking”, “running” etc), with optimized signal processing for each type
of motion. We are also using pattern recognition in non-navigation
applications for classification and identification of more general
events.