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Fundamentals Of Statistical Signal Processing

Steven Kay
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The article was published on 2001-03-16 and is currently open access. It has received 7058 citations till now. The article focuses on the topics: Statistical signal processing.

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Citations
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Journal ArticleDOI

Hybrid Data Fusion and Tracking for Positioning with GNSS and 3GPP-LTE

TL;DR: Analyzing the performance in a fairly realistic manner by taking into account ray-tracing simulations to generate a coherent environment for GNSS and 3GPP-LTE shows the ability of this approach to compensate the lack of satellites by additional TDOA measurements from a future 3G PP-L TE communications system.
Patent

Methods and apparatus for generating and communicating wireless signals having pilot signals with variable pilot signal parameters

TL;DR: In this article, a receiver receives a channel-affected version of the wireless signal, and produces a corrected signal by applying corrections to the received signal based on estimated channel perturbations within the received signals.
Journal ArticleDOI

Iterative channel estimation and data detection in frequency-selective fading MIMO channels†

TL;DR: The receiver presented in this paper performs channel estimation and multiuser detection and decoding in an iterative manner, leading to significant Improvement of the overall receiver performance, compared to other schemes.
Journal ArticleDOI

A comparative study of local quantum Fisher information and local quantum uncertainty in Heisenberg XY model

TL;DR: In this paper, a comparative study between LQFI and LQU was conducted for the quantum Heisenberg XY model and the isotropic XY model submitted to an external magnetic field and it was shown that LFI reveals more quantum correlations than LQU.
Journal ArticleDOI

Decentralized Random-Field Estimation for Sensor Networks Using Quantized Spatially Correlated Data and Fusion-Center Feedback

TL;DR: This work proposes a Bayesian framework for adaptive quantization, fusion-center feedback, and estimation of the random field and its parameters, and derives a simple suboptimal scheme for estimating the unknown parameters.
References
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Book

Adaptive Filter Theory

Simon Haykin
TL;DR: In this paper, the authors propose a recursive least square adaptive filter (RLF) based on the Kalman filter, which is used as the unifying base for RLS Filters.
Journal ArticleDOI

Fundamentals of statistical signal processing: estimation theory

TL;DR: The Fundamentals of Statistical Signal Processing: Estimation Theory as mentioned in this paper is a seminal work in the field of statistical signal processing, and it has been used extensively in many applications.
Book

Probability, random variables and stochastic processes

TL;DR: This chapter discusses the concept of a Random Variable, the meaning of Probability, and the axioms of probability in terms of Markov Chains and Queueing Theory.
Book

Probability, random variables, and stochastic processes

TL;DR: In this paper, the meaning of probability and random variables are discussed, as well as the axioms of probability, and the concept of a random variable and repeated trials are discussed.
Book

Discrete-Time Signal Processing

TL;DR: In this paper, the authors provide a thorough treatment of the fundamental theorems and properties of discrete-time linear systems, filtering, sampling, and discrete time Fourier analysis.