Signal and information processing for sonar

OpenStax

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Summary of basic rules for probability theory

0.1 Introduction

0.2 Deductive logic (boolean algebra)

0.3 Inductive logic (probability theory)

0.4 Prior probabilities

0.5 Decision theory

1 Decibel scale with signal processing applications

2 Foundations of probability theory: basic definitions

3 Review of probability theory

4 Review of linear algebra

6 Signal energy vs. signal power

7 Introduction to statistical signal processing

8 Introduction to random signals and processes

9 Introduction to stochastic processes

10 The gaussian process

11 Sampling and random sequences

12 Stationary and nonstationary random processes

13 Correlation and covariance of a random signal

14 Autocorrelation of random processes

15 Crosscorrelation of random processes

16 Cauchy-schwarz inequality

17 The q-function

Detection theory

Introduction to detection theory

1 Discrete-time detection theory

2 Hypothesis testing

3 The neyman-pearson criterion

4 White gaussian noise

5 The likelihood ratio test

6 The bayes risk criterion in hypothesis testing

7 Sufficient statistics

8 Detection in the presence of unknowns

9 Random parameters

10 Non-random parameters

11 Spectral detection

Estimation theory

Introduction to estimation theory

1 Time-delay estimation

Active sonar processing

Introduction to active sonar

1 Sonar receiver model

2 Active sonar detection in ambient noise

3 Properties of active sonar matched filtering

Source: OpenStax, Signal and information processing for sonar. OpenStax CNX. Dec 04, 2007 Download for free at http://cnx.org/content/col10422/1.5

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