Nonlinear Bayesian Filtering Using the Unscented Linear Fractional Transformation Model

Pasha, Syed Ahmed; Tuan, Hoang Duong; Vo, Ba-Ngu

doi:10.1109/tsp.2009.2028950

Cited by 23 publications

(16 citation statements)

References 33 publications

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“…It is well known (see e.g. [15, Chapter III], [22], [28]) that almost all results for Gaussian target estimation are based on the derivation of the joint Gaussian distribution of the target and its observation. We will see later in the present paper that the joint GM distribution (1) facilitates unified framework for Bayesian and Kalman filters in both linear and nonlinear models.…”

Section: Joint Gmm Relayed Equationsmentioning

confidence: 99%

“…In fact, Trace(C lmse (α α α, β β β)) is the minimum MSE (MMSE) by linear estimator for X [28]. Therefore, it is true that g(α α α, β β β) ≤ Trace(C lmse (α α α, β β β)) ∀ α α α, β β β and we seek a suboptimal solution of the computationally intractable optimization problem (15) by solving its following majorant minimization 2 min α α α>0,β β β>0…”

Section: Joint Gmm Relayed Equationsmentioning

confidence: 99%

“…Consider a typical third-order nonlinear autoregressive model described mathematically by [22], [28] as…”

Section: Linear Sensor Network For Nonlinear Dynamicsmentioning

confidence: 99%

“…In this example, using the unscented transformation for updating the joint GM distribution of X k|k and X k+1|k for state prediction will not track the target. Following [28], we represent…”

Section: Linear Sensor Network For Nonlinear Dynamicsmentioning

confidence: 99%

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Joint Sensor and Relay Power Control in Tracking Gaussian Mixture Targets by Wireless Sensor Networks

Bengua

Tuan

Poor

2018

IEEE Trans. Signal Process.

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Abstract-This paper considers a wireless sensor network (WSN) for locating a static target or tracking a dynamic target, which is prior characterized by a Gaussian mixture (GM) distribution. An amplify-and-forward relay node is acting as a wireless bridge in relaying the sensor's independent observations of the target to a fusion center (FC). We are concerned with the joint transmitter power allocation for the sensors and relay to optimize the Bayesian filter, which is deployed at the FC for a global estimate of the target. The mean squared error (MSE) of the Bayesian filter is already computationally intractable for fixed sensor and relay transmitter power, so power allocation to minimize its mean squared error is a very challenging problem. In this paper, the problem is addressed by an iterative procedure of very low computational complexity. Simulations are provided to support the efficiency of our proposed power allocation.

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Section: Joint Gmm Relayed Equationsmentioning

confidence: 99%

Section: Joint Gmm Relayed Equationsmentioning

confidence: 99%

“…Consider a typical third-order nonlinear autoregressive model described mathematically by [22], [28] as…”

Section: Linear Sensor Network For Nonlinear Dynamicsmentioning

confidence: 99%

Section: Linear Sensor Network For Nonlinear Dynamicsmentioning

confidence: 99%

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Joint Sensor and Relay Power Control in Tracking Gaussian Mixture Targets by Wireless Sensor Networks

Bengua

Tuan

Poor

2018

IEEE Trans. Signal Process.

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“…It is assumed that the components of are uncorrelated to each other, making a diagonal matrix. In order to determine the statistical parameters of the output of the senors in this nonlinear model, we use unscented transformations [6], [11]. Regression points , = 0, 1, .…”

Section: ) Example Imentioning

confidence: 99%

Optimized power allocation by semidefinite programming and unscented transformation for nonlinear sensor network

Rashid

Tuan

Kha

et al. 2010

2010 4th International Conference on Signal Processing and Communication Systems

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This paper presents a novel technique of allocating optimized power to wireless sensor nodes in a nonlinear measurement model. We consider the problem of distributed estimation of a random vector-valued parameter in an energyconstrained sensor network. Noise-corrupted local nonlinear observations are transmitted by spatially distributed sensor nodes towards fusion center where estimation of the vector parameter is carried out. In order to guarantee reliable communication, we minimize mean square error of this estimate subject to a constraint on total power consumed by the network. This optimization problem is then recast into a semi-definite program (SDP) which guarantees globally optimized values of the required power gains at sensor nodes. Estimation performance of this novel technique is demonstrated through examples of nonlinear models. Furthermore, for linear models the proposed strategy provides better performance when compared with the previous sub-optimial techniques.

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Adaptive control and signal processing literature survey (No. 18)

2010

Adaptive Control & Signal

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that although the list has been broadly categorized, these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Erfu Yang at erfu.yang@eee.strath.ac.uk. ADAPTIVE SYSTEMSBaldi S, Battistelli G, Mosca E, Tesi P. Multi-model unfalsified adaptive switching supervisory control. Automatica 2010; 46(2):249-259. Cao C, Hovakimyan N. Stability margins of L 1 adaptive control architecture. IEEE Transactions on Automatic Control 2010; 55(2):480-487. Chen W, Jiao L. Adaptive tracking for periodically time-varying and nonlinearly parameterized systems using multilayer neural networks. IEEE Transactions on Neural Networks 2010; 21(2):345-351. Han T-T, Ge S, Lee T. Persistent dwell-time switched nonlinear systems: variation paradigm and gauge design. IEEE Transactions on Automatic Control 2010; 55(2):321-337. Li L, Petersen I. A Gramian-based approach to model reduction for uncertain systems. IEEE Transactions on Automatic Control 2010; 55(2):508-514. Li Q-K, Zhao J, Dimirovski G, Liu X-J. State convergence property of perturbed switched linear time-delay systems. IET Control Theory and Applications 2010; 4(2):273-281. Li T-S, Tong S-C, Feng G. A novel robust adaptive-fuzzytracking control for a class of nonlinear multi-input/multioutput systems. IEEE Transactions on Fuzzy Systems 2010; 18(1):150-160. Ma H-J, Yang G-H. Adaptive output control of uncertain nonlinear systems with non-symmetric dead-zone input. Automatica 2010; 46(2):413-420. Niedzwiecki M. Generalized adaptive notch smoothing revisited. IEEE Transactions on Signal Processing 2010; 58(3):1565-1576. Obaidat M, Misra S, Papadimitriou G. Adaptive and learning systems. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetic 2010; 40(1):2-5. Yang Z-J, Hara S, Kanae S, Wada K, Su C-Y. An adaptive robust nonlinear motion controller combined with disturbance observer. IEEE Transactions on Control Systems Technology 2010; 18(2):454-462. Yildiz Y, Annaswamy A, Kolmanovsky L, Yanakiev D. Adaptive posicast controller for time-delay systems with relative degree n * 2. Automatica 2010; 46(2):279-289. Zhang W-A, Yu L. Stabilization of sampled-data control systems with control inputs missing. IEEE Transactions on Automatic Control 2010; 55(2):447-452. 2. SIGNAL PROCESSING Aboutanios E. Estimation of the frequency and decay factor of a decaying exponential in noise. IEEE Transactions on Signal Processing 2010; 58(2):501-509. Addesso P, Marano S, Matta V. Estimation of target location via likelihood approximation in sensor networks. IEEE Transactions on Signal Processing 2010; 58(3):1358-1368. Bai K, Tepedelenlioglu C. Distributed detection in UWB wireles...

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Nonlinear Bayesian Filtering Using the Unscented Linear Fractional Transformation Model

Cited by 23 publications

References 33 publications

Joint Sensor and Relay Power Control in Tracking Gaussian Mixture Targets by Wireless Sensor Networks

Joint Sensor and Relay Power Control in Tracking Gaussian Mixture Targets by Wireless Sensor Networks

Optimized power allocation by semidefinite programming and unscented transformation for nonlinear sensor network

Adaptive control and signal processing literature survey (No. 18)

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