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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

Hyperspectral Image Target Detection Improvement Based on Total Variation

TL;DR: This work proposes a novel supervised target detection algorithm which uses a single target spectrum as the prior knowledge, and demonstrates that the proposed algorithm outperforms the other algorithms for the experimental data sets.
Posted Content

Robust Estimators for Variance-Based Device-Free Localization and Tracking

TL;DR: In this paper, two estimators were proposed to reduce the impact of the variations caused by intrinsic motion in a DFL system, such as branches moving in the wind and rotating or vibrating machinery.
Proceedings Article

Detection of malicious AIS position spoofing by exploiting radar information

TL;DR: This paper addresses the inference problem of whether the received AIS data are trustworthy with the help of radar measurements and information from the tracking system and proposes a generalized version of the sequential log-likelihood ratio test.
Journal ArticleDOI

Receptive field organization across multiple electrosensory maps. II. Computational analysis of the effects of receptive field size on prey localization.

TL;DR: In this paper, the receptive fields of Apteronotus leptorhynchus were calculated using the Fisher information (FI) method and compared with behavioral studies on prey detection.
Journal ArticleDOI

Expectation Propagation for Near-Optimum Detection of MIMO-GFDM Signals

TL;DR: It is shown that the resulting iterative MIMo-GFDM receiver with affordable complexity can approach optimum decoding performance and outperform MIMO-OFDM in a rich multipath environment.
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.