Fundamentals of Statistical Signal Processing: Estimation Theory

Sengupta, S. K.; Kay, S.

doi:10.2307/1269750

Cited by 351 publications

(589 citation statements)

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“…Another important factor to determine seawater communication channel characteristics, transition frequency f T (Hz), that helps in determining whether seawater behaves like conductor or insulator for a particular range of frequencies can be estimated by the given Equation (13). f T (Hz) can be computed only when (S/m) and (F/m) are known.…”

Section: Mathematical Modeling Of Path Loss Based On Em Communicationmentioning

confidence: 99%

“…Input y(t) in our case R is actual distance between T x and R x , while x(t) generated by the model given in Equation (5) based on is described as perturbation on input sequence of information y(t). Closeness is measured by minimizing LS error criteria 13 as represented in Equation (24). Here, in Equation (24), observational interval assumed between n = 0, 1, … , N − 1 and dependence of J (Jacobian matrix) is on via y(t).…”

Section: Nonlinear Least Square Approximation For Range Estimation Bamentioning

confidence: 99%

“…Here, g is p-dimensional matrix in Equation (25), whose inverse 13 can lead easily to computation of linear least square estimation (LSE) of A and thus non-LSE of as depicted in Equations (26) to (28).…”

Section: Nonlinear Least Square Approximation For Range Estimation Bamentioning

confidence: 99%

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Nonlinear least square approach for range estimation based on attenuation of EM waves in seawater using world ocean data from 1955 to 2012

Tahir

Piao

Jafri

2019

Int J Communication

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In this research paper, we estimated absorption loss (L ) and spreading loss (L o ) based on electromagnetic (EM) waves propagation, considering multiple seawater depth (1101) points from surface to 5500 m distributed uniformly with 5-m difference. Estimation of parameters aforementioned was done based on world ocean data (WOD13) from the National Center for Environmental Information (NCEI) averaged between 1955 and 2012 along decades for basic parameters of seawater T (C o ) and S (ppt) up to 5500-m depth vertically and also across (41 088) latitude/longitude points for all oceans that includes Indian, Pacific, Southern, Atlantic, and Arctic horizontally. We also computed another important factor that contributes in overall path loss (L UW ), loss due to polarization of EM fields between transmitter T x and receiver R x such as antenna polarization factor (L ). L (dB) along with L (dB) and L o (dB) by substituting into basic propagation model for EM waves in seawater helps us to predict efficiently accurate achievable range (R est ) using nonlinear least square (NLLS) approximation combined with Lambert transformation considering nonlinear exponential P T (dBm) decay. Moreover, predicted R est (m) helps us to minimize mean error (mean(e(t))) by adapting to actual range R (m) using NLLS approach. Simulation tool MATLAB has been used for implementation of NLLS approach and performance analysis.

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Section: Mathematical Modeling Of Path Loss Based On Em Communicationmentioning

confidence: 99%

Section: Nonlinear Least Square Approximation For Range Estimation Bamentioning

confidence: 99%

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Nonlinear least square approach for range estimation based on attenuation of EM waves in seawater using world ocean data from 1955 to 2012

Tahir

Piao

Jafri

2019

Int J Communication

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“…The Cramér-Rao lower bound theory gives the lower bound of the unbiased estimation error [26]. Based on this, we introduce the following lemma to obtain the lower bound of tracking error in Quasi-static Case:…”

Section: A Cramér-rao Lower Bound Of Tracking Errormentioning

confidence: 99%

“…We now perform some theoretical analysis on beam tracking problem. Based on the CRLB theory in [26], we introduce the following lemma to obtain the lower bound of tracking error:…”

Section: B Cramér-rao Lower Bound Of Tracking Errormentioning

confidence: 99%

Joint Beam and Channel Tracking for Two-Dimensional Phased Antenna Arrays

Liu

Sun

et al. 2018

Proceedings of the 2nd ACM Workshop on Millimeter Wave Networks and Sensing Systems

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The sparsity and the severe attenuation of millimeter-wave (mmWave) channel imply that highly directional communication is needed. The narrow beam produced by large array requires accurate alignment, which can be achieved by beam training with large exploration overhead in static scenarios.However, this training expense is prohibitive when serving fast-moving users. In this paper, we focus on accurate two-dimensional (2D) beam and channel tracking problem in mmWave mobile communication.The minimum exploration overhead of 2D tracking is given in theory first. Then the time-varying channels are divided into three cases: Quasi-static Case, Dynamic Case I and Dynamic Case II. We further develop three tracking algorithms corresponding to these three cases. The proposed algorithms have several salient features: (1) fading channel supportive: they can simultaneously track the channel gain and 2D beam direction in fading channel environments; (2) low exploration overhead: they achieve the minimum exploration requirement for 2D tracking; (3) fast tracking speed and high tracking accuracy:in Quasi-static Case and Dynamic Case I, the tracking error is proved to converge to the minimum Cramér-Rao lower bound (CRLB). In Dynamic Case II, our tracking algorithm outperforms existing algorithms with lower tracking error and faster tracking speed in simulation.DI,3 to smaller size antenna arrays when |s| 2 σ 2 β σ 2 z = 0dB, we compare the minimum CRLB and the CRLB achieved by

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Approximate Gaussian variance inference for state‐space models

Deka,

Goulet

2023

Adaptive Control & Signal

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SummaryState‐space models require an accurate knowledge of the process error () and measurement error () covariance matrices for exact state estimation. Even though the matrix can be, in many situations, considered to be known from the measuring instrument specifications, it is still a challenge to infer the matrix online while providing reliable estimates along with a low computational cost. In this article, we propose an analytically tractable online Bayesian inference method for inferring the matrix in state‐space models. We refer to this method as approximate Gaussian variance inference (AGVI) using which we are able to treat the error variance and covariance terms in the full matrix as Gaussian hidden states and infer them simultaneously with the other hidden states in a closed‐form manner. The two case studies show that the method is able to provide statistically consistent estimates for the mean and uncertainties of the error variance terms for univariate and multivariate cases. The method also exceeds the performance of the existing adaptive Kalman filter methods both in terms of accuracy and computational efficiency.

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Fundamentals of Statistical Signal Processing: Estimation Theory

Cited by 351 publications

References 0 publications

Nonlinear least square approach for range estimation based on attenuation of EM waves in seawater using world ocean data from 1955 to 2012

Nonlinear least square approach for range estimation based on attenuation of EM waves in seawater using world ocean data from 1955 to 2012

Joint Beam and Channel Tracking for Two-Dimensional Phased Antenna Arrays

Approximate Gaussian variance inference for state‐space models

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