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

0.1 Introduction

1 Discrete-time detection theory

2 Hypothesis testing

3 The neyman-pearson criterion

4 White gaussian noise

4.1 Validity of the white noise assumption

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

2.1 Introduction

2.2 Decision space

2.3 Sonar receiver model

2.4 Statistical model of the ping history

2.5 Active sonar likelihood function

2.6 Signal to noise ratio of the detector

2.7 A-priori assumptions

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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