This document discusses spectrum estimation methods and their real-time implementation on TI DSPs. It introduces the autoregressive (AR) model and three estimation methods - periodogram, Burg, and M-covariance. It describes simulating these methods and comparing the estimated spectra to the true spectrum. It then discusses implementing the methods on a DSK6713 in real-time using separate model files for each method. A GUI is created using MATLAB to control the real-time model, enable reloading methods, and visualize the estimated spectra.

1. 0 - 1
© 2007 Texas Instruments Inc,
Content developed in partnership with
Tel-Aviv University
From MATLAB® and Simulink® to
Real Time with TI DSPs
Spectrum Estimation

2. Slide 2
© 2007 Texas Instruments Inc,
Preface
• Our Goal is to Estimate the Spectrum of stochastic
processes
• We are concentrating in AR-Processes
• 3 methods of Estimation will be discussed:
Periodogram, Burg and M-Covariance

3. Slide 3
© 2007 Texas Instruments Inc,
AR Basics
• An Auto-Regressive (AR) process is commonly described as
White Noise filtered by an all-pole LTI system:
 
N
jw
NN e
S
n
N


)
( )
(
1
jw
e
P 2
)
(
)
(
)
(
jw
N
jw
xx
e
P
e
S
n
X


• Frequency domain characteristics:
– The AR Process Spectrum is given by:
Where:
2
2
1 )
,...,
,
,
1
(
)
(
k
N
jw
XX
a
a
a
FFT
e
S







k
l
l l
n
a
n
n
p
1
)
(
)
(
)
( 


4. Slide 4
© 2007 Texas Instruments Inc,
AR Basics cont.
• Time Analysis of the process (of order k):
– every sample has correlation with at most k previous samples
– The autocorrelation function looks like:
– For every n<-k or n>k holds: 0
)
( 
n
RXX

5. Slide 5
© 2007 Texas Instruments Inc,
Estimation Methods
• 3 Methods:
– Periodogram
– Burg
– M-Covariance
• Our Goal:
– Given a finite buffer of samples of the stochastic process
estimate its spectrum
• Assumption:
– The process is mean Ergodic and Correlation Ergodic

6. Slide 6
© 2007 Texas Instruments Inc,
Periodogram
• The Periodogram block computes a nonparametric
estimate of the spectrum. The block averages the
squared magnitude of the FFT computed over
windowed sections of the input and normalizes the
spectral average by the square of the sum of the
window samples.

7. Slide 7
© 2007 Texas Instruments Inc,
The Modified Covariance Method
• The Modified Covariance Method block estimates
the power spectral density (PSD) of the input using
the modified covariance method. This method fits
an autoregressive (AR) model to the signal by
minimizing the forward and backward prediction
errors in the least squares sense. The order of the
all-pole model is the value specified by the
Estimation order parameter. To guarantee a valid
output, you must set the Estimation order
parameter to be less than or equal to two thirds the
input vector length. The spectrum is computed from
the FFT of the estimated AR model parameters.

8. Slide 8
© 2007 Texas Instruments Inc,
Burg Method
• The Burg Method block estimates the power
spectral density (PSD) of the input frame using the
Burg method. This method fits an autoregressive
(AR) model to the signal by minimizing (least
squares) the forward and backward prediction
errors while constraining the AR parameters to
satisfy the Levinson-Durbin recursion.

9. Slide 9
© 2007 Texas Instruments Inc,
Hands-On
• Simulation
• Implementation using the DSK6713
• GUI to handle the R-T implementation

10. Slide 10
© 2007 Texas Instruments Inc,
Simulation
• The coefficients are known for the model
• Internal generation of the true spectrum
• Generation of the AR signal using white noise and
all-poles filter
• Comparison between all 3 methods in the model (to
one another and to the true spectrum
• The results are presented using the frequency
domain

11. Slide 11
© 2007 Texas Instruments Inc,
The Simulation Environment
• Simulation involves the 3 methods simultaneously

12. Slide 12
© 2007 Texas Instruments Inc,
Real-Time Environment
• Based on the Simulation model
• R-T Implementation contains 3 model files, each
implements different method separately
• We will present the Top-Down Architecture of the
Real-Time solution

13. Slide 13
© 2007 Texas Instruments Inc,
Real Time Environment (cont.)
DSK6713
CODEC
TMS320C6713
All-pole
Filter
D/A
(Left)
Generate
Reference
Spectrum
A/D
(Left)
D/A
(Right)
 
t
n
Line In Line Out
Signal
Generator
Spectrum
Estimator
Oscilloscope
White Noise
PC
RTDX

14. Slide 14
© 2007 Texas Instruments Inc,
Real Time Environment (cont.)
• R-T model using Periodogram Estimation:

15. Slide 15
© 2007 Texas Instruments Inc,
GUI Functionality
• Using Matlab GUI and TI libraries we will show how
to build a gui that enables the user to control the
model easily
• The GUI involves RTDX calls to negotiate with the DSK in R-T
• The RTDX is a proprietary interface that enables the Host to
send/receive data to the dsk in R-T
• The GUI enables the user to perform the following operations:
– Reloading a model (3 optional Estimation methods)

16. Slide 16
© 2007 Texas Instruments Inc,
The System
Noise
Spectrum
Estimated
Spectrum