Fast Transient Imaging
A Python framework revolving around reconstruction of impulse responses from AMCW lidar measurements.
MaximumEntropySpectralEstimate.fx File Reference
Include dependency graph for MaximumEntropySpectralEstimate.fx:

Macros

#define MOMENT_COUNT   1
 
#define MOMENT_COUNT   2
 
#define MOMENT_COUNT   3
 
#define MOMENT_COUNT   4
 
#define MOMENT_COUNT   5
 
#define MOMENT_COUNT   6
 
#define MOMENT_COUNT   7
 
#define MOMENT_COUNT   8
 

Functions

float GetMaximumEntropySpectralEstimate (float2 pTrigonometricMoment[MOMENT_COUNT+1], float Point)
 

Detailed Description

This header defines functions for reconstructing distributions with minimal Burg entropy matching given trigonometric moments. This is known as maximum entropy spectral estimate.

Macro Definition Documentation

#define MOMENT_COUNT   1
#define MOMENT_COUNT   2
#define MOMENT_COUNT   3
#define MOMENT_COUNT   4
#define MOMENT_COUNT   5
#define MOMENT_COUNT   6
#define MOMENT_COUNT   7
#define MOMENT_COUNT   8

Function Documentation

float GetMaximumEntropySpectralEstimate ( float2  pTrigonometricMoment[MOMENT_COUNT+1],
float  Point 
)

This function computes and returns the density at the given point of interest (in radians) for the distribution with minimal Burg entropy matching the given trigonometric moments. This is known as maximum entropy spectral estimate.

Note
The moments are not supposed to be normalized and the zeroth moment has to be provided.