Abstracts of
Selected Publications
Fault Tolerant Indoor Positioning Based on Federated Kalman Filter
Tarik Ayabakan,
Feza Kerestecioğlu,
In this article, multi-sensor indoor positioning, which is based on fusing tri-laterated position data of the target, is considered. A novel method, which is based on federated Kalman filtering and makes use of the fingerprint data, namely, federated Kalman filter with skipped covariance updating (FKF-SCU) is proposed. The data collected on two test beds are used in comparingthe performances of the proposed algorithm and that of the regular federated filter. It is shown that the proposed algorithm provides fault tolerance and quick recovery, whenever signal reception from an access point is interrupted, as well as an improvement of 12.57% on the position accuracy.
Circular Formations of Non-Communicating Robot Groups via Local Strategies
Feza Kerestecioğlu,
Ümit Şen,
Çağrı Işıkver,
Ahmet Göktekin
Local strategies, which are based on cost minimization, to achieve circular formations of autonomous robot groups are presented. It is assumed that the group members have no communication capabilities or any means of interchanging information among themselves, and that they can only rely on their sensors, which provide relative positions of their nearby group members. It is verified on simulations that via appropriately defined cost functions arc, arc-triangle and circle formations are obtained, which can be maintained during navigation.
RSSI-Based Indoor Positioning via Adaptive Federated Kalman Filter
Tarık Ayabakan,
Feza Kerestecioğlu
In this paper, federated Kalman filter (FKF) is applied for indoor positioning. Position information that is multi-laterated from the distance information obtained using the received signal strengths collected from several access points are processed in a FKF to estimate the position of the target. Two approaches are presented to adjust the information-sharing coefficients of FKF using online measurements. The data collected on a test bed composed of four access points are used to assess and compare the performances of the proposed algorithms. It is shown that the estimation error can be improved considerably by adjusting the information-sharing coefficients online.
Navigation of Non-Communicating Autonomous Mobile Robots with Guaranteed Connectivity
Ahmet Cezayirli, Feza Kerestecioğlu
We consider the connectivity of autonomous mobile robots. The robots navigate using simple local steering rules without requiring explicit communication among themselves. We show that using only position information of neighbors, the group connectivity can be sustained even in the case of bounded position measurement errors and the occlusion of robots by other robots in the group. In implementing the proposed scheme, sub-optimal solutions are invoked to avoid an excessive computational burden. We also discuss the possibility of deadlock which may bring the group to a standstill and show that the proposed methodology avoids such a scenario in real-life settings.
Fault Tolerant
Control with Re-Configuring Sliding-Mode Schemes
Ufuk Demirci, Feza Kerestecioğlu
In this paper, a controller design method for linear MIMO systems is presented which a sliding mode controller is reconfigured in case of system faults. Faults are detected with the residual vector generated from a standard linear observer. Once a fault has been detected the fault distribution matrix can be obtained and used to update the corrective or equivalent control parts of the sliding mode controller. As a result, fault tolerant adaptive controllers keep the system performance within acceptable limits or at least avoids the system to wind-up.
Reconfiguring
Sliding Mode Controller Implementation with Adjustable Robustness against
Uncertainties
Ufuk Demirci, Feza Kerestecioğlu
In
this paper, a controller design method for underwater vehicles is presented,
which is based on re-configuration of a sliding-mode controller in case of
disturbances caused by shallow water conditions. The disturbance distribution
information can be obtained and used to update the corrective gain vector of
the sliding-mode controller. This increases the robustness of the controller
and, hence, keeps the system performance within acceptable limits. Proposed
method is validated with simulations on a submarine model.
Optimal Input
Design for Detecting Changes towards Unknown Hypotheses
Feza Kerestecioğlu, İlker Çetin
The
effects of auxiliary input signals on detecting changes in ARMAX processes via
statistical tests are discussed. Two extensions to the Cumulative Sum Test are
considered. The first is applicable when the direction of the change in the
parameter space is known, but its magnitude is unknown. The second is
applicable when neither is known. The performance criteria for the design of
stationary stochastic inputs are based on the asymptotic properties of the
test. It is shown that power-constrained optimal inputs have discrete spectra and
a suitably chosen input can drastically improve the detection performance.
Zero-Crossing Based
Demodulation of Minimum Shift Keying
Mine Kalkan, Feza Kerestecioğlu
Minimum
shift keying (MSK) modulation has features such as constant envelope, compact
spectrum and good error performance, which are all desirable in many digital
applications including mobile radio. Numerous receiver structures to demodulate
MSK have been suggested, such as correlation receivers, differential detectors
and frequency discriminators. MSK is a form of biphase
keying and can be detected by a zero-crossing based phase demodulator which
gives near optimum performance. In this paper, the bit error performance of a
zero-crossing based coherent MSK demodulator is theoretically investigated and
a closed-form expression for the bit error rate is derived. The results
indicate that the demodulator performs within 0.8–1 dB of the theoretical
optimum for MSK. Towards the goal of deriving probability of bit error, it is
also shown that under additive white Gaussian noise (AWGN) zero-crossing
locations of MSK signals are Gaussian distributed except at very low
signal-to-noise ratios.
Non-Uniform
Sampling for Detection of Abrupt Changes
Feza Kerestecioğlu,
Sezai Tokat
In
this work, detection of abrupt changes in continuous-time linear stochastic
systems and selection of the sampling interval to improve the detection performance
are considered. Cost functions are proposed to optimize both uniform and nonuniform sampling intervals for the well-known cumulative
sum algorithm. Some iterative techniques are presented to make online
optimization computationally feasible. It is shown that considerable
improvement in the detection performance can be obtained by using nonuniform sampling intervals.
Input Design for
Change Detection
Feza Kerestecioğlu,
Martin B. Zarrop
After
a brief review of the cumulative sum test used for detecting abrupt changes in
dynamical systems, the design of inputs to improve its performance is
discussed. The chosen design objectives are to decrease the detection time and
to ensure a tolerable false alarm rate. Both offline and online inputs are
considered. In the offline case, the optimal input spectrum is shown to consist
of one or two frequencies when the input power is constrained. In the online
case, a suboptimal output feedback is obtained by linearizing
the cost and constraint functions in the related optimization problem.
Gain Adaptation in
Sliding Mode Control of Robotic Manipulators
Melikşah Ertuğrul,
Okyay Kaynak, F. Kerestecioğlu
In
this paper, a novel scheme is proposed to adapt the gains of a Sliding Mode
Controller (SMC) so that the problems faced in its practical implementations as
a motion controller are overcomed. A Lyapunov function is selected for the design of the SMC and
MIT rule is used for gain adaptation. The criterion that is minimised
for gain adaptation is selected as the sum of the squares of the control signal
and the sliding surface function. This novel approach is tested on a scara type robot manipulator. The experimental results
presented prove its efficacy.
Fault Detection in
Robot Manipulators Using Statistical Tests
Feza Kerestecioğlu,
Bekir Sami Nalbantoğlu
In
this work, application of the Cumulative Sum Test in detecting faults on a
two-link robot manipulator is considered. A continuous-time analogue of the
CUSUM test, rather than the traditional discrete-time version, is used. The
detection is based on the errors of the state estimates produced by Kalman filters, which use quasi-linear models of the
manipulator. This model is obtained by a Taylor series expansion of the
nonlinear state equations with respect to the measurement error. Simulations to
validate the proposed method for detection of several possible faults such as,
sensor bias, actuator torque bias and payload changes
are presented.
Optimal Diversity
Combining under Correlated Noise in Mobile Radio
Mine Kalkan, Feza Kerestecioğlu
In
this paper the performance of predetection maximal
ratio and equal gain combiners are investigated under conditions of correlated
branch noise. A statistical model is devised to determine the spatial noise
correlation coefficients at metropolitan area base stations and the cases where
significant correlations are likely are clarified. Optimal weightings for a
maximal ratio combiner with two-branch space diversity are derived under
correlated noise. Based upon this result it is shown that correlation in branch
noise can be used to improve the combiner performance by dynamically adjusting the
weightings so as to partially cancel the noise. Performance of equal gain
combiners is also shortly discussed.
Sequential Analysis
of Stationary Autoregressive Processes
Feza Kerestecioğlu,
Martin B. Zarrop
In
this work, the properties of a sequential probability ratio test to decide on
the parameter values of a stationary autoregressive process are investigated.
An analogue of Wald's Fundamental Identity is derived for this case. The
average sample number and operating characteristics of the test are obtained
using this identity and some useful approximate expressions are derived.
Input Design for
Detection of Abrupt Changes in Dynamical Systems
Feza Kerestecioğlu,
Martin B. Zarrop
The
detection and diagnosis of changes in stationary dynamical systems via
statistical methods and using input design to improve detection performance are
discussed. A cumulative sum test to detect a change towards one of several
hypotheses is obtained by exploiting connections with the sequential
probability ratio test. For input design, the objectives are taken to be to
decrease the detection time and, at the same time, to ensure a tolerable false
alarm rate. Both off-line auxiliary inputs and on-line generation of the inpurt signal by a linear output feedback are considered.
The problem is first introduced for the two-hypotheses
case and then the design techniques are extended to the general
multiple-hypotheses case.
