Naïve Bayes For Radar Micro-Doppler Recognition

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Naïve Bayes For RadarMicro-Doppler Recognition

5. Test Strategy

Analysis Of Classifier at ±5%

Assessment Of Variance

7. Conclusions

The Pre Processing Steps

Performance Of Naïve Bayesian

Use Of Bhattacharyya Bound

6. Results

Datasets

Size

Correlation

Frames

Duration

Parameters

R

Pfa

Pdec

Pcc

Variance

Performance

Range

Max 95%

Step 5%

Min 45%

4. Performance Prediction

Assumptions

Sufficient Data To Estimate μ and Σ

Equal Prior Probabilities

Probability Distributions

Multivariate Guassians

Bhattacharyya Bound

Limits

Sub-Optimal

Better

Difficult

Chernoff

Binary

Cases

Vehicles vs Tracked

Tracked vs Personnel

Wheeled vs Tracked

Wheeled vs Personnel

Probability Of Error

2. Data

Test

Reference

Pre-Processing

Principal Component Analysis

Normalizing

Clutter

FFT

Power

Decibel

Absolute

Micro-Doppler

Unknown

Classes

Tracked

Personnel

Wheeled

Thales

3. Naïve Bayesian Classifier

Assumption

Validity

Not Perfect

Improved By PCA

Independence

Definition

1. Introduction

Review Of Relvant Papers

Motivation For Work

Background