Abstract: In the last decade, automotive companies have invested a lot in terms of innovation about many aspects regarding the automatic driver assistance systems. One innovation regards the usage of a smart camera placed on the car’s side mirror for monitoring the back and lateral road situation. A common road scenario is the overtaking of the preceding car and, in this case, a brief distraction or a loss of concentration can lead the driver to undertake this action, even if there is an already overtaking vehicle, leading to serious accidents. A valid support for a secure drive can be a smart camera system, which is able to automatically analyze the road scenario and consequentially to warn the driver when another vehicle is overtaking. This paper describes a method for monitoring the side view of a vehicle by using camera optical flow motion vectors. The proposed solution detects the presence of incoming vehicles, assesses their distance from the host car, and warns the driver through different levels of alert according to the estimated distance. Due to the low complexity and computational cost, the proposed system ensures real time performances.
Abstract: In this paper, the problem of fault detection and
isolation in the attitude control subsystem of spacecraft formation
flying is considered. In order to design the fault detection method, an
extended Kalman filter is utilized which is a nonlinear stochastic state
estimation method. Three fault detection architectures, namely,
centralized, decentralized, and semi-decentralized are designed based
on the extended Kalman filters. Moreover, the residual generation
and threshold selection techniques are proposed for these
architectures.
Abstract: In communication systems, frequency jump is a serious problem caused by the oscillators used. Kalman filters are used to detect that jump, despite the tradeoff between the noise level and the speed of the detection. In this paper, an improvement is introduced in the Kalman filter, through a nonlinear change in the bandwidth of the filter. Simulation results show a considerable improvement in the filter speed with a very low noise level. Additionally, the effect on the response to false alarms is also presented and false alarm rate show improvement.
Abstract: Ultra-wide band (UWB) communication is one of
the most promising technologies for high data rate wireless networks
for short range applications. This paper proposes a blind channel
estimation method namely IMM (Interactive Multiple Model) Based
Kalman algorithm for UWB OFDM systems. IMM based Kalman
filter is proposed to estimate frequency selective time varying
channel. In the proposed method, two Kalman filters are concurrently
estimate the channel parameters. The first Kalman filter namely
Static Model Filter (SMF) gives accurate result when the user is static
while the second Kalman filter namely the Dynamic Model Filter
(DMF) gives accurate result when the receiver is in moving state. The
static transition matrix in SMF is assumed as an Identity matrix
where as in DMF, it is computed using Yule-Walker equations. The
resultant filter estimate is computed as a weighted sum of individual
filter estimates. The proposed method is compared with other existing
channel estimation methods.
Abstract: The growth and interconnection of power networks in many regions has invited complicated techniques for energy management services (EMS). State estimation techniques become a powerful tool in power system control centers, and that more information is required to achieve the objective of EMS. For the online state estimator, assuming the continuous time is equidistantly sampled with period Δt, processing events must be finished within this period. Advantage of Kalman Filtering (KF) algorithm in using system information to improve the estimation precision is utilized. Computational power is a major issue responsible for the achievement of the objective, i.e. estimators- solution at a small sampled period. This paper presents the optimum utilization of processors in a state estimator based on KF. The model used is presented using Petri net (PN) theory.
Abstract: There are various kinds of medical equipment which
requires relatively accurate positional adjustments for successful
treatment. However, patients tend to move without notice during a
certain span of operations. Therefore, it is common practice that
accompanying operators adjust the focus of the equipment. In this
paper, tracking controllers for medical equipment are suggested to
replace the operators. The tracking controllers use AHRS sensor
information to recognize the movements of patients. Sensor fusion is
applied to reducing the error magnitudes through linear Kalman filters.
The image processing of optical markers is included to adjust the
accumulation errors of gyroscope sensor data especially for yaw
angles.
The tracking controller reduces the positional errors between the
current focus of a device and the target position on the body of a
patient. Since the sensing frequencies of AHRS sensors are very high
compared to the physical movements, the control performance is
satisfactory. The typical applications are, for example, ESWT or
rTMS, which have the error ranges of a few centimeters.
Abstract: Micro electromechanical sensors (MEMS) play a vital
role along with global positioning devices in navigation of
autonomous vehicles .These sensors are low cost ,easily available but
depict colored noises and unpredictable discontinuities .Conventional
filters like Kalman filters and Sigma point filters are not able to cope
with nonwhite noises. This research has utilized H∞ filter in nonlinear
frame work both with Kalman filter and Unscented filter for
navigation and self alignment of an airborne vehicle. The system is
simulated for colored noises and discontinuities and results are
compared with not robust nonlinear filters. The results are found
40%-70% more robust against colored noises and discontinuities.
Abstract: In the normal operation conditions of a pico satellite,
conventional Unscented Kalman Filter (UKF) gives sufficiently good
estimation results. However, if the measurements are not reliable
because of any kind of malfunction in the estimation system, UKF
gives inaccurate results and diverges by time. This study, introduces
Robust Unscented Kalman Filter (RUKF) algorithms with the filter
gain correction for the case of measurement malfunctions. By the use
of defined variables named as measurement noise scale factor, the
faulty measurements are taken into the consideration with a small
weight and the estimations are corrected without affecting the
characteristic of the accurate ones. Two different RUKF algorithms,
one with single scale factor and one with multiple scale factors, are
proposed and applied for the attitude estimation process of a pico
satellite. The results of these algorithms are compared for different
types of measurement faults in different estimation scenarios and
recommendations about their applications are given.