Description: Signal Detection and Estimation 关于信号检测与估计的一本英文电子书，本书首先介绍了一些信号的随机过程，然后详细地介绍了一些信号检测和估计的方法。很好的一本外文书，大家一起分享。-Signal Detection and Estimation。This book is primarily designed for the study of statistical signal detection andparameter estimation. In the first,it present concepts on probability and random variables. Then,it present Statistical Decision Theory，Parameter Estimation.It is really a good book,share with all of you. Platform: |
Size: 4384768 |
Author:何通 |
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Description: 噪声中的信号检测主要介绍信号检测的统计理论的原理与应用。着重介绍在实践中特别有用的那些原理，并应用它们来解决数字通信、雷达以及声呐中遇到的检测问题。-Noise in the signal detection introduces the statistical theory of signal detection theory and application. Focuses particularly useful in practice those principles and apply them to solve the digital communications, radar and sonar detection problems encountered. Platform: |
Size: 6749184 |
Author:houhj |
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Description: 压缩包是统计信号处理中雷达信号检测中的恒虚警处理方法，包括慢门限和快门限的原程序和结果-Archive is a statistical signal processing radar signal detection CFAR methods, including slow shutter limit threshold and the original procedures and results Platform: |
Size: 1024 |
Author:王广辉 |
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Description: 《统计信号处理基础 估计与检测理论》，该书本书是一部经典的有关统计信号处理的权威著作。全书分为两卷，分别讲解了统计信号处理基础的估计理论和检测理论。-" Statistical signal processing based estimation and detection theory" , the book This book is a classic authority on statistical signal processing work. The book is divided into two volumes, respectively, to explain the basis of statistical signal processing, estimation theory and detection theory. Platform: |
Size: 15525888 |
Author:big163 |
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Description: 基于蒙特卡洛方法的主动声纳信号检测性能分析.主动声纳信号检测性能的分析上,目前在计算机仿真中一般假定混响包络的统计特性符合瑞利分布模型。基于此模型,已经有了较完善的理论。然而,在现代高分辨声纳系统中,混响包络的统计特性并不符合瑞利分布模型。此时在接收机工作特性分析时存在大量繁琐的公式推导。因此该文采用蒙特卡洛(M onte Carlo)统计试验方法,实现对瑞利分布混响背景下的主动声纳信号检测性能分析。结合对接收机工作特性曲线的仿真,得出了检测概率的理论值和仿真结果的误差曲线。误差曲线表明,蒙特卡洛方法在主动声纳信号检测的性能评估中是可行的。 更多还原
-Active sonar signal detection performance analysis, computer simulation is generally assumed that the statistical properties of the reverberation envelope distribution model of Rayleigh active sonar signal detection performance analysis based on the Monte Carlo method. Based on this model, has been a better theory. However, in the modern high-resolution sonar system, the statistical characteristics of the reverberation envelope does not comply with the Rayleigh distribution model. At this point in the receiver operating characteristic analysis, there is a lot of tedious formula derivation. In this paper, using the Monte Carlo (M onte Carlo) statistical test methods, active sonar signal detection performance analysis reverberation background Rayleigh distribution. The receiver operating characteristic curve simulation obtained the error curve of the theoretical value and the simulation results of the detection probability. The error curve shows that the Monte Carlo method in the assessm Platform: |
Size: 175104 |
Author:cooldog |
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Description: Solution Manual to Fundamentals of Statistical Signal Processing - Detection Theory
By Steven M.Kay Platform: |
Size: 5568512 |
Author:Giuseppe |
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Description: 统计信号处理基础估计与检测理论，这是本很好的书，特别适合无基础的，老师经常推荐此书-Estimated based on statistical signal processing and detection theory, this is a very good book, especially for non-basic, teachers often recommend this book Platform: |
Size: 14891008 |
Author:李文 |
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Description: 统计信号处理基础完整
经典教材习题解答
作者手写扫描
包含检测理论与估计理论两个部分-Complete statistical signal processing base
Classic textbook exercises answers
Author scanned handwritten
Consists of two parts detection theory and estimation theory Platform: |
Size: 10460160 |
Author:breeze_bit |
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Description: 本书将系统介绍语音信号处理的基础、原理、方法和应用。全书共分十二章，其中第2章介绍了语音信号处理的基础知识，如语音、语言学、汉语语音学、发音与听觉器官、语音信号的数学模型、语音信号的 统计特性分析等；第 章介绍了语音信号特征分析和处理技术， 包括时域分析、频域分析，同态分析、线性预测分析、音调检测和共振峰检测方法等。在第4章介绍了矢量量化技术；在第5章介绍了隐马尔可夫模型技术； 在第6章介绍了人工神经网络在语音信号处理中的应用技术等。本书在每一章后面都附有课外思考题， 并且在全书的最后附有语音处理实用程序。-This book introduces the basis, speech signal processing principle, method and application. The book is divided into twelve chapters, the second chapter introduces the basic knowledge of speech signal processing, such as speech, linguistics, Chinese phonetics, sound and auditory organs of speech signals, the mathematical model of speech signals, the statistical characteristic analysis chapter introduces the character of speech signal analysis and processing technology, including time domain analysis, frequency domain analysis, homomorphic analysis, linear prediction analysis, tone detection and resonance peak detection method. In the fourth chapter introduces the vector quantization technology in the fifth chapter introduces hidden Markov model techniques in the sixth chapter, introduces the application of artificial neural network in speech signal processing application technology. The book is accompanied by extracurricular thinking questions in each chapter, and in the end with sp Platform: |
Size: 8122368 |
Author:将为人父 |
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Description: 基于贝叶斯准则，用matlab仿真出二元与四元数字信号在加性高斯白噪声干扰下的统计检测的方法与性能，并与理论结果比较-Bayesian criteria using matlab simulation methods and properties of the binary and quaternary digital signal in additive white Gaussian noise of statistical tests and compared with the theoretical results Platform: |
Size: 4096 |
Author:廖为城 |
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Description: This paper proposes a novel, highly effective spectrum
sensing algorithm for cognitive radio and white space
applications. The proposed spectral covariance sensing (SCS)
algorithm exploits the different statistical correlations of the received
signal and noise in the frequency domain. Test statistics are
computed the covariance matrix of a partial spectrogram
and compared with a decision threshold to determine whether a
primary signal or arbitrary type is present or not. This detector is
analyzed theoretically and verified through realistic open-source
simulations using actual digital television signals captured in the
US. Compared to the state of the art in the literature, SCS
improves sensitivity by 3 dB for the same dwell time, which is a
very significant improvement for this application. Further, it is
shown that SCS is highly robust to noise uncertainty, whereas
many other spectrum sensors are not.-This paper proposes a novel, highly effective spectrum
sensing algorithm for cognitive radio and white space
applications. The proposed spectral covariance sensing (SCS)
algorithm exploits the different statistical correlations of the received
signal and noise in the frequency domain. Test statistics are
computed the covariance matrix of a partial spectrogram
and compared with a decision threshold to determine whether a
primary signal or arbitrary type is present or not. This detector is
analyzed theoretically and verified through realistic open-source
simulations using actual digital television signals captured in the
US. Compared to the state of the art in the literature, SCS
improves sensitivity by 3 dB for the same dwell time, which is a
very significant improvement for this application. Further, it is
shown that SCS is highly robust to noise uncertainty, whereas
many other spectrum sensors are not. Platform: |
Size: 386048 |
Author:elizeu |
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Search - statistical signal detection