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Consider a set of random sequences, each consisting of independent and identically distributed random variables. Each sequence is generated according to one of the two possible distributions F0 or F1 with unknown prior probabilities (1 − ∊) and ∊, respectively. The objective is to design a sequential decision-making procedure that identifies a sequence generated according to F1 with the fewest number...
This paper presents a method for remaining useful life (RUL) estimation of lithium-ion batteries based on its power fading model. Firstly, an empirical model of power fading is developed based on battery test data. Then, the obtained model has been used in a particle filtering (PF) framework for making end of life (EOL) predictions at various stages of its cycle life. Finally, the predictions were...
Data from vehicles instrumented with GPS or other localization technologies are increasingly becoming widely available due to the investments in Connected and Automated Vehicles (CAVs) and the prevalence of personal mobile devices such as smartphones. Tracking or trajectory data from these probe vehicles are already being used in practice for travel time or speed estimation and for monitoring network...
Sparse code multiple access (SCMA) is a new type of non-orthogonal modulation suggested for 5G systems offering lower bit-error rate and higher spectral efficiency. There are many challenges when designing high throughput SCMA message passing decoders to meet the standards expected from 5G networks. Particularly, the message passing algorithm (MPA) needs many exponential computations to calculate...
We consider the problem of parameter estimation under a sequential framework. Specifically we assume that an i.i.d. random process is observed sequentially with its common pdf having a random parameter that must be estimated. We are interested in designing a stopping time that will decide when is the best moment to stop sampling the process and an estimator that will use the acquired samples in order...
Condition monitoring data have been widely used to evaluate the health state and reliability, as well as estimate the remaining useful life (RUL) for degrading systems. Among various degradation modeling and RUL estimating methods, Wiener process based models is recognized by both scholars and engineers as the one of the most effect tools, and thus becomes very popular nowadays. In this paper, a prognostic...
This paper proposes eye-diagram estimation methods for voltage-and probability-dependent pulse amplitude modulation (PAM4) signal on stacked through-silicon vias (TSVs). To satisfy demands on a high-speed and small form factor, the number of TSVs and the data rate on the TSVs have been increased. The number of stacked TSVs is related to the electrical performance due to its electrical length. The...
A particular problem in satellite communications is the estimation of the current operating point of a bent-pipe transponder. Various special effects aggravate this estimation problem, among which the capture effect, gain compression, unknown attenuation in the transmission channel, or noisy received signals are the most prominent. In most practical cases, operators desire a fully blind estimator...
The computational capability of an Echo State Network (ESN), expressed in terms of low prediction error and high short-term memory capacity, is maximized on the so-called “edge of criticality”. In this paper we present a novel, unsupervised approach to identify this edge and, accordingly, we determine hyperparameters configuration that maximize network performance. The proposed method is application-independent...
In this paper, within the linear minimum mean square error (LMMSE) framework and with the Gaussian assumption, we propose a novel nonlinear estimation algorithm using a Hermite polynomial based uncorrelated conversion (UC), which is a nonlinear function of the original measurement while being uncorrelated with the original measurement. The UC can be regarded as a “new” measurement and additional information...
This paper addresses the problem of localizing an unknown number of static sources emitting unknown signals from time-difference of arrival (TDOA) measurements. Based on the framework of random finite sets and finite set statistics, we formulate the Bayesian estimation problem and develop a particle-based localization algorithm that overcomes the challenges related to the highly non-linear TDOA measurement...
This paper considers asymptotic perfect secrecy and asymptotic perfect estimation in distributed estimation for large sensor networks under threat of an eavesdropper, which has access to all sensor outputs. To measure secrecy, we compare the estimation performance at the fusion center and at eavesdropper in terms of their respective Fisher Information. We analyze the Fisher Information ratio between...
We propose a method for detecting and estimating multiple objects from multiple noisy images with partly overlapping observation areas. The goal is to detect the objects that are “locally” present in the individual observation areas and to estimate their states. Our method is based on a new closed-form expression of the marginal posterior probability hypothesis density (PHD) and admits a distributed...
Target tracking in a network of wireless cameras may fail if data are captured or exchanged asynchronously. Unlike traditional sensor networks, video processing may generate significant delays that also vary from camera to camera. Moreover, the continuous and rapid change of the dynamics of the consensus variable (the target state) makes tracking even more challenging under these conditions. To address...
One of the most important challenges in target tracking is the modeling of correlated and non-Gaussian random processes. In this paper, a new target tracking approach by means of particle filtering in environments with highly correlated sensors, is discussed. The goal is to provide an accurate model of dependency structure in multivariate observation likelihood function, with non-Gaussian marginals...
In this paper, single-target tracking using radar measurements is addressed. Recently, algorithms based on Bernoulli random finite sets have proved efficient in a cluttered environment. However, in Bayesian approaches, the choice of the motion model impacts the trajectory estimation accuracy. To select an appropriate set of motion models, a joint tracking and classification (JTC) algorithm can be...
This paper considers spatially coupled repeat-accumulate (SC-RA) coded interleave-division multiple-access (IDMA) systems with segmented interleavers. Each user employs an SC-RA encoder and a spreader followed by user-specific segmented interleavers. Each segment corresponds with a parity variable node in the SC-RA code's protograph. Since all the users' parity bits at the ends of the coupling positions...
Estimation of nonlinear stochastic systems is one of the important issues discussed by researchers. Indeed, the particle filter has emerged as a consistent technique to study nonlinear systems, and has been considered as a defiance issue relying on a proficient distribution allowing consequently an efficient number of particles. In this paper, a recursive estimation algorithm using particle filtering...
Beckmann distribution is a versatile mathematical model, which can be applied in performance analyses of radio frequency communications, free-space optical communications and underwater optical communications. However, the cumulative distribution function (CDF) of Beckmann distribution does not have a closed-form expression, which makes it challenging to derive closed-form outage probability expression...
A recursive algorithm for estimating the statistics of the Normal distribution is designed, making it adaptive in the sense that the forgetting factor is driven by data. A mechanism to suppress obsolete information is proposed, following the principles of Bayesian decision-making. Specifically, the best combination of two time-evolution model hypotheses in terms of the geometric mean is performed...
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