Browsing by Subject "State space methods"

Now showing 1 - 13 of 13

Open Access
Adaptive tracking of narrowband HF channel response
(Wiley-Blackwell Publishing, 2003) Arikan, F.; Arıkan, Orhan
Estimation of channel impulse response constitutes a first step in computation of scattering function, channel equalization, elimination of multipath, and optimum detection and identification of transmitted signals through the HF channel. Due to spatial and temporal variations, HF channel impulse response has to be estimated adaptively. Based on developed state-space and measurement models, an adaptive Kalman filter is proposed to track the HF channel variation in time. Robust methods of initialization and adaptively adjusting the noise covariance in the system dynamics are proposed. In simulated examples under good, moderate and poor ionospheric conditions, it is observed that the adaptive Kalman filter based channel estimator provides reliable channel estimates and can track the variation of the channel in time with high accuracy.
Open Access
Framework for online superimposed event detection by sequential Monte Carlo methods
(IEEE, 2008-03-04) Urfalıoğlu, Onay; Kuruoğlu, E. E.; Çetin, A. Enis
In this paper, we consider online seperation and detection of superimposed events by applying particle filtering. We concentrate on a model where a background process, represented by a 1D-signal, is superimposed by an Auto-Regressive (AR) 'event signal', but the proposed approach is applicable in a more general setting. The activation and deactivation times of the event-signal are assumed to be unknown. We solve the online detection problem of this superpositional event by extending the state space dimension by one. The additional parameter of the state represents the AR-signal, which is zero when deactivated. Numerical experiments demonstrate the effectiveness of our approach. ©2008 IEEE.
Open Access
An inventory problem with two randomly available suppliers
(Institute for Operations Research and the Management Sciences, 1997) Gürler, Ü.; Parlar, M.
This paper considers a stochastic inventory model in which supply availability is subject to random fluctuations that may arise due to machine breakdowns, strikes, embargoes, etc. It is assumed that the inventory manager deals with two suppliers who may be either individually ON (available) or OFF (unavailable). Each supplier's availability is modeled as a semi-Markov (alternating renewal) process. We assume that the durations of the ON periods for the two suppliers are distributed as Erlang random variables. The OFF periods for each supplier have a general distribution. In analogy with queuing notation, we call this an Es1[Es2]/G1[G2] system. Since the resulting stochastic process is non-Markovian, we employ the "method of stages" to transform the process into a Markovian one, albeit at the cost of enlarging the state space. We identify the regenerative cycles of the inventory level process and use the renewal reward theorem to form the long-run average cost objective function. Finite time transition functions for the semi-Markov process are computed numerically using a direct method of solving a system of integral equations representing these functions. A detailed numerical example is presented for the E2[E2]/M[M] case. Analytic solutions are obtained for the particular case of "large" (asymptotic) order quantity, in which case the objective function assumes a very simple form that can be used to analyze the optimality conditions. The paper concludes with the discussion of an alternative inventory policy for modeling the random supply availability problem.
Open Access
Matrix-geometric solutions of M/G/1-type Markov chains: A unifying generalized state-space approach
(1998) Akar, N.; Oǧuz, N.C.; Sohraby, K.
In this paper, we present an algorithmic approach to find the stationary probability distribution of M/G/1-type Markov chains which arise frequently in performance analysis of computer and communication networ ks. The approach unifies finite- and infinite-level Markov chains of this type through a generalized state-space representation for the probability generating function of the stationary solution. When the underlying probability generating matrices are rational, the solution vector for level k, x k, is shown to be in the matrix-geometric form x k+1 = gF k H, k ≥ 0, for the infinite-level case, whereas it takes the modified form x k+1 = g 1F 1 kH 1 + g 2F 2 K-k-1 H 2, 0 ≤ k < K, for the finite-level case. The matrix parameters in the above two expressions can be obtained by decomposing the generalized system into forward and backward subsystems, or, equivalently, by finding bases for certain generalized invariant subspaces of a regular pencil λE - A. We note that the computation of such bases can efficiently be carried out using advanced numerical linear algebra techniques including matrix-sign function iterations with quadratic convergence rates or ordered generalized Schur decomposition. The simplicity of the matrix-geometric form of the solution allows one to obtain various performance measures of interest easily, e.g., overflow probabilities and the moments of the level distribution, which is a significant advantage over conventional recursive methods.
Open Access
Neural networks based online learning
(IEEE, 2017) Ergen, Tolga; Kozat, Süleyman Serdar
In this paper, we investigate online nonlinear regression and introduce novel algorithms based on the long short term memory (LSTM) networks. We first put the underlying architecture in a nonlinear state space form and introduce highly efficient particle filtering (PF) based updates, as well as, extended Kalman filter (EKF) based updates. Our PF based training method guarantees convergence to the optimal parameter estimation under certain assumptions. We achieve this performance with a computational complexity in the order of the first order gradient based methods by controlling the number of particles. The experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods.
Open Access
Observer based control of chaos
(IEEE, 1997-08) Solak, Ercan; Morgül, Ömer; Ersoy, Umut
In this work we consider the control of forced chaotic oscillators. To obtain any desirable behavior, the system parameters are effectively modified using state feedback. The system states used in the feedback are estimated through a nonlinear observer. The application of the proposed method is illustrated for Duffing and Van der Pol oscillators.
Open Access
On algebraic properties of general proper decentralized systems
(Elsevier, 1993) Yu, R.; Sezer, M. E.; Gao, W.
The new concepts of the decentralized output feedback variable polynomial, the decentralized output feedback cycle index of general proper systems, and the geometric multiplicities of decentralized fixed modes are introduced. Their computational methods and some algebraic properties are presented. It is shown that the decentralized output feedback cycle index of a general proper system is equal to one when the system has no fixed modes or equal to the maximum of the geometric multiplicities of its decentralized fixed modes. It is also shown that almost all decentralized output feedback can be used to make the zeros of the decentralized variable polynomial distinct, and disjoint from any given finite set of points on the complex plane.
Open Access
Online distributed nonlinear regression via neural networks
(IEEE, 2017) Ergen, Tolga; Kozat, Süleyman Serdar
In this paper, we study the nonlinear regression problem in a network of nodes and introduce long short term memory (LSTM) based algorithms. In order to learn the parameters of the LSTM architecture in an online manner, we put the LSTM equations into a nonlinear state space form and then introduce our distributed particle filtering (DPF) based training algorithm. Our training algorithm asymptotically achieves the optimal training performance. In our simulations, we illustrate the performance improvement achieved by the introduced algorithm with respect to the conventional methods.
Open Access
Parameter identification for partially observed diffusions
(Kluwer Academic Publishers-Plenum Publishers, 1992) Dabbous, T.E.; Ahmed, N.U.
In this paper, we consider the identification problem of drift and dispersion parameters for a class of partially observed systems governed by Ito equations. Using the pathwise description of the Zakai equation, we formulate the original identification problem as a deterministic control problem in which the unnormalized conditional density (solution of the Zakai equation) is treated as the state, the unknown parameters as controls, and the likelihood ratio as the objective functional. The question of existence of elements in the parameter set that maximize the likelihood ratio is discussed. Further, using variational arguments and the Gateaux differentiability of the unnormalized density on the parameter set, we obtain the necessary conditions for optimal identification. © 1992 Plenum Publishing Corporation.
Open Access
Sentioscope: a soccer player tracking system using model field particles
(Institute of Electrical and Electronics Engineers, 2016) Baysal, S.; Duygulu, P.
Tracking multiple players is crucial to analyze soccer videos in real time. Yet, rapid illumination changes and occlusions among players who look similar from a distance make tracking in soccer very difficult. Particle-filter-based approaches have been utilized for their ability in tracking under occlusion and rapid motions. Unlike the common practice of choosing particles on targets, we introduce the notion of shared particles densely sampled at fixed positions on the model field. We globally evaluate targets' likelihood of being on the model field particles using our combined appearance and motion model. This allows us to encapsulate the interactions among the targets in the state-space model and track players through challenging occlusions. The proposed tracking algorithm is embedded into a real-life soccer player tracking system called Sentioscope. We describe the complete steps of the system and evaluate our approach on large-scale video data gathered from professional soccer league matches. The experimental results show that the proposed algorithm is more successful, compared with the previous methods, in multiple-object tracking with similar appearances and unpredictable motion patterns such as in team sports. © 1991-2012 IEEE.
Open Access
Superimposed event detection by sequential Monte Carlo methods
(IEEE, 2007) Urfalıoğlu, O.; Kuruoğlu, E. E.; Çetin, A. Enis
In this paper, we consider the detection of rare events by applying particle filtering. We model the rare event as an AR signal superposed on a background signal. The activation and deactivation times of the AR-signal are unknown. We solve the online detection problem of this superpositional rare event by extending the state space dimension by one. The additional parameter of the state represents the AR-signal, which is zero when deactivated. Numerical experiments demonstrate the effectiveness of our approach.
Open Access
Supporting hurricane inventory management decisions with consumer demand estimates
(Elsevier B.V., 2016) Morrice, D. J.; Cronin, P.; Tanrisever, F.; Butler, J. C.
Matching supply and demand can be very challenging for anyone attempting to provide goods or services during the threat of a natural disaster. In this paper, we consider inventory allocation issues faced by a retailer during a hurricane event and provide insights that can be applied to humanitarian operations during slow-onset events. We start with an empirical analysis using regression that triangulates three sources of information: a large point-of-sales data set from a Texas Gulf Coast retailer, the retailer's operational and logistical constraints, and hurricane forecast data from the National Hurricane Center (NHC). We establish a strong association between the timing of the hurricane weather forecast, the forecasted landfall position of the storm, and hurricane sales. Storm intensity is found to have a weaker association on overall inventory decisions. Using the results of the empirical analysis and the NHC forecast data, we construct a state-space model of demand during the threat of a hurricane and develop an inventory management model to satisfy consumer demand prior to a hurricane making landfall. Based on the structure of the problem, we model this situation as a two-stage, two-location inventory allocation model from a centralized distribution center that balances transportation, shortage and holding costs. The model is used to explore the role of recourse, i.e., deferring part of the inventory allocation until observing the state of the hurricane as it moves towards landfall. Our approach provides valuable insights into the circumstances under which recourse may or may not be worthwhile in any setting where an anticipated extreme event drives consumer demand.
Open Access
Testing communication protocols
(1992) Naik, K.; Sarıkaya, B.
[No abstract available]