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—Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time according to a finite state Markov chain. In this paper, our aim is to recursively compute optimal state estimates for this class of systems. We present efficient simulation-based algorithms called particle filters to solve the optimal filtering problem as well as the(More)
—In this paper, sequential or " on-line " hidden Mar-kov model (HMM) signal processing schemes are derived and their performance illustrated in simulation studies. The on-line algorithms are sequential expectation maximization (EM) schemes and are derived by using stochastic approximations to maximize the Kullback-Leibler information measure. The whemes can(More)
—Consider a Hidden Markov model (HMM) where a single Markov chain is observed by a number of noisy sensors. Due to computational or communication constraints, at each time instant, one can select only one of the noisy sensors. The sensor scheduling problem involves designing algorithms for choosing dynamically at each time instant which sensor to select to(More)
—This paper addresses the call admission control problem for multiservice wireless code division multiple access (CDMA) cellular systems when the physical layer channel and receiver structure at the base station are taken into account. The call admission problem is formulated as a semi-Markov decision process with constraints on the blocking probabilities(More)
—This paper develops adaptive step-size blind LMS algorithms and adaptive forgetting factor blind RLS algorithms for code-aided suppression of multiple access interference (MAI) and narrowband interference (NBI) in DS/CDMA systems. These algorithms optimally adapt both the step size (forgetting factor) and the weight vector of the blind linear multiuser(More)
— In this paper the authors derive a new class of finite-dimensional recursive filters for linear dynamical systems. The Kalman filter is a special case of their general filter. Apart from being of mathematical interest, these new finite-dimensional filters can be used with the expectation maximization (EM) algorithm to yield maximum likelihood estimates of(More)
We present decentralized adaptive filtering algorithms for sensor activation control in an unattended ground sensor network (UGSN) comprised of ZigBee-enabled nodes. Nodes monitor their environment in a low-power ldquosleeprdquo mode, until an intruder is detected, then must decide whether to enter a full-power monitoring and transmission mode if their(More)
—This paper is concerned with recursive algorithms for the estimation of hidden Markov models (HMMs) and autoregres-sive (AR) models under Markov regime. Convergence and rate of convergence results are derived. Acceleration of convergence by averaging of the iterates and the observations are treated. Finally, constant step-size tracking algorithms are(More)
We present transmission scheduling algorithms for maximizing the lifetime of a sensor network. In each data collection, only one group of sensors are scheduled to transmit their measurements directly to an access point (AP) through a fading channel, causing a reduction in battery energy levels of these sensors. We formulate the problem of dynamically(More)