a Graeme Smith 18 éve
 629
 
                     
                    
                        Naïve Bayes For Radar Micro-Doppler Recognition
                        
                        
                            
                            
                         
                     
                 
             
            
                
                    
                    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