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Motivated by applications in biology and economics, we propose new volatility measures based on the H2 system norm for linear networks stimulated by independent or correlated noise. We identify critical links in a network, where relatively small improvements can lead to large reductions in network volatility measures. We also examine volatility measures of individual nodes and their dependence on...
This paper presents distributed state estimation methods through wireless sensor networks with event triggered communication protocols among the sensors. Optimal consensus filters are derived which apply to generic non-uniform and asynchronous information exchange scenarios among neighboring sensors. To obtain a scalable covariance propagation algorithm, the optimal filter is approximated by a suboptimal...
Contour representation is an important application in image compression, template matching, object detection and recognition. However, it is far from meeting the current requirement due to the expensive computational cost and complex noise in the real-world application. In order to make contour representation more practical, we propose a novel approach of abstracting contours of the objects in an...
A stochastic approximation with averaging is applied to a consensus algorithm for multi-agent systems and the convergence of the algorithm is analyzed. The consensus is considered with respect to the time average of the states of agents. For the fixed network structure and time-varying structure, the relation between the number of iterations of the algorithm and consensus accuracy is explicitly clarified...
The paper deals with state estimation of stochastic nonlinear systems by means of local filters. A new technique is designed to provide a self-assessment of the filter with respect to its estimate quality. It uses a non-Gaussianity measure based on conditional third moment of the state to indicate a possible decrease of estimate quality. The technique is proposed for general local filters with detailed...
Jointly optimizing a function over multiple parameters can sometimes prove very costly, particularly when the number of parameters is large. Cyclic optimization (optimization over a subset of the parameters while the rest are held fixed) may prove significantly simpler; it seeks to combine algorithms for performing conditional optimization with the hopes of obtaining a solution to the joint optimization...
This paper presents a bio-inspired central pattern generator (CPG) architecture for feedback control of rhythmic behavior. The CPG circuit is realized as a coupled oscillator feedback particle filter. The collective dynamics of the filter are used to approximate a posterior distribution that is used to construct the optimal control input. The architecture is illustrated with the aid of a model problem...
During the past decades some very interesting results have been obtained in controller synthesis using Linear Parameter-Varying (LPV) systems. However, the LPV models are commonly required to be transformed into State Space (SS) form. We tackle the LPV SS identification problem directly in the frequency domain. To the best of our knowledge, this is a novel approach. When the input and scheduling are...
In this paper, we consider state estimation for Stochastic Hybrid Systems (SHS). These are systems that possess both continuous-valued and discrete-valued dynamics. For SHS with nonlinear hybrid dynamics and/or non-Gaussian disturbances, state estimation can be implemented as an Interacting Multiple Model (IMM) particle filter. However, a disadvantage of particle filtering is the computational load...
The purpose of this work is to identify the parameters of a second order system from noisy data in a context where the difficulty is twofold. First, the model is strongly non linear and possibly non Gaussian. Second, the noise distribution is unknown. It is nevertheless assumed to belong to a finite set, thus, the identification issue is coupled with a model selection problem. In a Bayesian framework,...
A poor choice of importance density can have detrimental effect on the efficiency of a particle filter. While a specific choice of proposal distribution might be close to optimal for certain models, it might fail miserably for other models, possibly even leading to infinite variance. In this paper we show how mixture sampling techniques can be used to derive robust and efficient particle filters,...
In many real-life Bayesian estimation problems, it is appropriate to consider non-Gaussian noise distributions to model the existence of outliers, impulsive behaviors or heavy-tailed physical phenomena in the measurements. Moreover, the complete knowledge of the system dynamics uses to be limited, as well as for the process and measurement noise statistics. In this paper, we propose an adaptive recursive...
A new tensor approximation method is developed based on the CANDECOMP/PARAFAC (CP) factorization that enjoys both sparsity (i.e., yielding factor matrices with some nonzero elements) and resistance to outliers and non-Gaussian measurement noise. This method utilizes a robust bounded loss function for errors in the low-rank tensor approximation while encouraging sparsity with Lasso (or ℓ1-) regularization...
We consider non parametric sequential hypothesis testing problem when the distribution under the null hypothesis is fully known but the alternate hypothesis corresponds to some other unknown distribution with some loose constraints. We propose a simple algorithm to address the problem. This is also generalized to the case when the distribution under the null hypothesis is not fully known. These problems...
This paper presents simple methods to tightly estimate the information rate achieved by a Gaussian input and the constrained capacity of a finite-alphabet input of a Bernoulli-Gaussian (BG) impulsive noise channel in Rayleigh fading. Specifically, under the assumption of a Gaussian input, we propose a novel approach to calculate the achievable rate by examining the instantaneous output entropy in...
Multidimensional stochastic optimization plays an important role in analysis and control of many technical systems. To solve the challenging problems of multidimensional optimization, it was suggested to use the randomized algorithms of stochastic approximation with perturbed input which have simple forms and provide consistent estimates of the unknown parameters for observations under “almost arbitrary”...
The nonlinear stochastic systems pose two important challenges in designing alarms: 1) measurements are not necessarily Gaussian distributed and 2) measurements are correlated - in particular for closed loop systems. We present an algorithm for designing alarms for such systems with unknown and known models. In the case of unknown models our approach is based on Monte Carlo simulations. In the case...
A deterministic method for sequential estimation of 3-D rotations is presented. The Bingham distribution is used to represent uncertainty directly on the unit quaternion hypersphere. Quaternions avoid the degeneracies of other 3-D orientation representations, while the Bingham distribution allows tracking of large-error (high-entropy) rotational distributions. Experimental comparison to a leading...
This paper considers the problem of motion planning for linear systems subject to Gaussian motion noise and proposes a risk-aware planning algorithm: CC-RRT∗-D. The proposed CC-RRT∗-D employs the chance-constraint approximation and leverages the asymptotically optimal property of RRT∗ framework to compute risk-aware and asymptotically optimal trajectories. By explicitly considering the state dependence...
The polar format algorithm (PFA) allows the use of computationally efficient fast Fourier transforms in synthetic aperture radar (SAR) image formation, but introduces phase errors when making the far-field approximations that facilitate this approach. The phase errors cause spatially variant distortion and defocus in the formed image. These effects may complicate target recognition applications. To...
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