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Fundamentals of statistical signal processing: estimation theoryMarch 1993
- Author:
- Steven M. Kay
Abstract
No abstract available.
Cited By
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- Bazzi S and Xu W A simple over-the-air hardware calibration procedure in TDD systems 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), (1-6)
- Vieira J, Rusek F and Tufvesson F A receive/transmit calibration technique based on mutual coupling for massive MIMO base stations 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), (1-6)
- Hao Y, Song Z, Hou S and Li H Energy- and spectral-efficiency tradeoff in massive MIMO systems with inter-user interference 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), (553-557)
- Meyer F and Win M Data Association for Tracking Extended Targets MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM), (337-342)
- Ghods A, Severi S and Abreu G Localization in V2X communication networks 2016 IEEE Intelligent Vehicles Symposium (IV), (5-9)
- Ma J and Ping L Orthogonal AMP for compressed sensing with unitarily-invariant matrices 2016 IEEE Information Theory Workshop (ITW), (280-284)
- Ren G, Schizas I and Maroulas V Sparsity based multi-target tracking using mobile sensors 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (4578-4582)
- Zhang L, Stein M and Nossek J Asymptotic performance analysis for 1-bit Bayesian smoothing 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (4458-4462)
- Feng P, Wang W, Naqvi S, Dlay S and Chambers J Social force model aided robust particle PHD filter for multiple human tracking 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (4398-4402)
- Stein M, Bar S, Nossek J and Tabrikian J Performance analysis for pilot-based 1-bit channel estimation with unknown quantization threshold 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (4353-4357)
- Konar A and Sidiropoulos N Parametric Frugal sensing of autoregressive power spectra 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (4293-4297)
- Xu T, Hu Y, Zhang B and Leus G RSS-based sensor localization in underwater acoustic sensor networks 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (3906-3910)
- Xie Y, Janssen G and van der Veen A A practical clock synchronization algorithm for UWB positioning systems 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (3891-3895)
- Calvo-Fullana M, Matamoros J, Antón-Haro C and Fosson S Sparsity-promoting sensor selection with energy harvesting constraints 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (3766-3770)
- Suárez-Casal P, Fresnedo Ó, Castedo L and García-Frías J Parametric analog mappings for correlated Gaussian sources over AWGN channels 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (3761-3765)
- Zhai M, Feng H, Tan Y and Hu B Kalman filters with Bayesian quadratic game fusion in networks 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (3756-3760)
- Ferrante G, Geraci G, Quek T and Win M Group-blind detection with very large antenna arrays in the presence of pilot contamination 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (3701-3705)
- Zhang Y and Zhu W Energy-efficient pilot and data power allocation in massive MIMO communication systems based on MMSE channel estimation 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (3571-3575)
- Mollén C, Choi J, Larsson E and Heath R One-bit ADCs in wideband massive MIMO systems with OFDM transmission 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (3386-3390)
- Marano S, Matta V and Willett P One plus two may not equal two plus one in a social sensing network with unknown parameters 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (3321-3325)
- Bar S and Tabrikian J A risk-unbiased approach to a new Cramér-Rao bound 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2921-2925)
- Elnakeeb A and Mitra U On Training Sequence Optimization for Leaked MIMO OFDM Channels 2019 IEEE Global Communications Conference (GLOBECOM), (1-6)
- Li Y and Amarasuriya G Multiple Relay-Aided Massive MIMO NOMA 2019 IEEE Global Communications Conference (GLOBECOM), (1-6)
- Galappaththige D and Amarasuriya G Active Pilot Contamination Attack Detection in Sub-6 GHz Massive MIMO NOMA Systems 2019 IEEE Global Communications Conference (GLOBECOM), (1-6)
- Chen X, Liu A, Yu W, Cheng H, Shen K and Zhao M Distributed Pilot Design for Massive Connectivity in Cellular Networks 2019 IEEE Global Communications Conference (GLOBECOM), (1-6)
- Zhao Y, Shi J, Li Z, Hao B, Xue X and Si J Discrete Monotonic Optimization Based Sensor Selection for TDOA Localization 2019 IEEE Global Communications Conference (GLOBECOM), (1-6)
- Senigagliesi L, Baldi M and Gambi E Statistical and Machine Learning-Based Decision Techniques for Physical Layer Authentication 2019 IEEE Global Communications Conference (GLOBECOM), (1-6)
- Wan Q, Fang J, Chen Z and Li H Generalized Bussgang LMMSE Channel Estimator for One-Bit Massive MIMO Systems 2019 IEEE Global Communications Conference (GLOBECOM), (1-6)
Index Terms
- Fundamentals of statistical signal processing: estimation theory
Recommendations
Reviews
Michael L. Hilton
Estimation theory is concerned with extracting information, in the form of various parameters, from signals. Such parameter estimation plays an important role in many of today's digital signal processing applications. Kay's book is an excellent introduction to estimation theory. The book is split into two parts. The first part deals with classical estimation and has chapters on minimum variance unbiased estimation, the Cramer-Rao lower bound, linear models, linear unbiased estimators, maximum likelihood estimation, least squares estimation, and the method of moments. The second part deals with Bayesian estimation and has chapters on general and linear Bayesian estimators (including a discussion of Wiener filtering) and Kalman filtering. A summary chapter doubles as a guide for choosing which estimator to use in a particular situation. This book is aimed at both graduate students and practicing engineers. Readers are assumed to have a basic knowledge of signal processing, probability and random processes, and linear algebra. In the book's preface, Kay states his text is “an attempt to strike a balance” between theory and practice. He has achieved this goal. The mathematics behind parameter estimation is presented in a well-motivated, accessible manner. Kay takes the time to explain the significance of the problems being tackled and of the mathematical solutions. Theorems and results are stated and put to use in each chapter's main text, with the proofs given in appendices that follow each chapter. Several running examples, such as estimating a DC level in additive white Gaussian noise, are used throughout the book to illustrate the differences between estimation techniques. Many of the chapters have a section on signal processing applications, including adaptive beamforming, blind deconvolution, frequency estimation, and vehicle tracking. Each chapter also includes a set of exercises and a list of bibliographic references.
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