# Presentation: 890760

## Power-Aware Improvement in Signal Detection

**Scott D. Briles, Ph.D.**
**Military/Aerospace  Section Chair**
**presenting**

_**LANL team members**_
**Maya Gokhale Ph.D. , *****Project Lead***
**Scott Briles, Ph.D.**
**Patrick Shriver**
**Jayashree Harikuma  Ph.D. **

**International Signal Processing Conference**
**March 31 - April 3, 2003, Dallas, Texas**
**Military/Aerospace  Section**

## Presentation Outline

**Project Background **
**Problem Background**
**Satellite Application**
**Signal Detection**
**Probability of False Alarm vs. Energy**
**Website **

## Project Background

**Los Alamos National Laboratory contributed to the **_**Power Aware Computing/Communications**_** (PACC) program sponsored by DARPA**
**    ****Mission: "JIP: Just In time Power"**
**LANL provided its FORTÉ Satellite and it’s mission as an *****application***** for the development of unique concepts in power-aware computing in space**
**LANL developed the concept and researched the feasibility of  ****Algorithm Power Modulation**

**FORTÉ (Fast On-orbit Recording of Transient Events)**
**Conceptualized, computer-generated graphic**
**Launch August 1997**

## Remote-Sensing Satellite Application: Problem Background

**Ionosphere disperses the signal **
**low frequencies are delayed**
**Dispersion is governed by the total electron content (TEC) of the ionosphere**
**Need satellite-base signal detection and parameter estimation (TEC, time of arrival) **

**Remote-Sensing Satellite ****Application: Problem Background**

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- 10

**Time**

**Frequency**

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- x 10

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- x 10

- 7

**Desired to have a system that is aware of on-board power resources which can adapt on-orbit algorithms accordingly**

_**Signal Detection and Parameter Estimation**_

## Signal Detection Subsystem

**N of M **
**Triggers?**

_**Analog Trigger Box**_

**Detector**

**Sub-band Filter, fc1**

**TH  **

**Detector**

**Sub-band Filter,fc2**

**TH  **

**Detector**

**Sub-band Filter,fcM**

**THRESHOLD  **

**Detection and Time of Arrival, (TOA)**

**time-frequency domain detection**
**individual filter threshold**
**N of M filters must exceed threshold**
**Data Provided:**
** ****Center frequencies of Sub-band filters**
** ****time of arrival for each filter detection**

**Modulate Detection Thresholds and Signal-Processing Algorithms based on Satellite State-of-Health (thermal, power, vibration, etc) and Mission Needs**

## Power-Aware Signal Processing

_**Parameter Estimation Algorithms (TEC, TOA)**_

**Chirp Signal**

## Parameter Estimation Algorithms

**Parameter Estimation Algorithms**

**Estimate total electron content (tec) and TOA using Least Mean Squares (LMS) Fit and  analog trigger box data**
**Reduce influence of erroneous triggers  **
**Maximum-likelihood fit**
**Software Trigger Box on ****digitized waveform data**
** ****multiple short FFTs.  **
**Making a more refined trigger box through software**
**Compute Bank of Matched Filters on ****digitized waveform data**** **
**Searching for closest correlation to known parameters**
**Vary number of processors based on power availability **

## Power Measurement CollectionSeptember 2001

- Dynamic Energy Range:
- 100,000

|  | LMS | ML | ST | MF |
| --- | --- | --- | --- | --- |
| Current
(amps - peak) | 
2.06 | 
2.06 | 
2.18 | 
2.04 |
| Power
(watts - rms) | 
5.5 | 
5.6 | 
5.67 | 
5.0 |
| Execution Time | 
3.4 s | 
183s | 
8.34ms | 
470ms |
| Energy
(joules) | 
18.7e-6 | 
1.02e-3 | 
47.3e-3 | 
2.35 |

**Power Test Results:              ****Test Bench for RAD750**

## TEC Estimations

**10**

**1**

**0**

**2**

**4**

**6**

**8**

**10**

**12**

**Energy Expended (Joules)**

**RMS Error of TEC Estimate**** (x10**

** ****18**

**)**

**TEC Error versus Energy for Algorithms**

**Least Mean Squares **

**Maximum Likelihood **

**SW Trigger **

**Matched**

** ****Filter**

## Reducing False Alarms

_**Further analysis of parameter estimates from **__***in situ***__** algorithms can reduce false alarms**_
**May need to accept some loss in probability of detection**
**Different parameter-estimation algorithms have different performances in reduction of false alarms**
**GOAL:  False-alarm performance can be governed by energy usage**

## Signal Detection Theory

_**Single Channel Solution**_
**Envelop Detector**
**Additive White Gaussian Noise**
**Noise Riding Threshold ******
**Prob. of Detection & False Alarm**

**Sub-Band Filter**
**H****BP****(f)**

**Low-Pass Filter**
**H****LP****(f)**

**|  |**

_**Multi-Channel Solution **_
**Double Threshold (N of 8)**
**Harrington’s Method**
**Optimize ********  **
**Greater latitude in ******** for large SNRs**

_**Single Channel of Analog Trigger Box **__**(one of eight)**_

## Signal Detection Theory

**Signal Detection Theory**

**Adapt  with Event Rate**

_**ROC of Analog Trigger Box**_

**Point of Investigation for Monte Carlo Experiments**

**DO Monte Carlo experiments to determine conditional probability density functions**

## Post-Processing Threshold Determination

**Use Conditional probability density functions**
**P(TEC | Signal Detected)**
**P(TEC | False Alarm)**
**Accept some lose in Probability of Detection**
**4% in experiment**
**Determine Thresholds in TEC values for each Algorithm to Reduce False Alarms**
**Least amount of red area and for set amount of blue area while still making sense**
**Combination of Rules and Heuristics to Set the Thresholds**

## Conditional Density Functions with thresholds for the four algorithms 

**Conditional pdfs generated from 100k-trial Monte Carlo experiment**

## Prob. of False Alarm ReductionVia LMS Algorithm’s TEC Estimate

**Probability Detection reduced 4% points to yield a factor of 2 reduction in Probability Of False Alarm**
**COST: 18.7 ********J (3.4 ********s)**

**Set Prob. of Detection Reduction  (0.04 )**
**93.6% of ****probability density**** ****(blue)**
**Examine Histogram for TEC-value **_**Thresholds to be applied to LMS Estimates**_
**Judge Effect on Prob. Of False Alarm from Histogram ****(Red-Shaded Area is 54% of probability density)**

## Prob. of False Alarm ReductionVia Peaks of Matched Filter 

**100**

**150**

**200**

**250**

**300**

**350**

**400**

**0**

**0.005**

**0.01**

**0.015**

**0.02**

**0.025**

**0.03**

**0.035**

**0.04**

**0.045**

**Estimated TEC Value**

**Probability Density**

**Histograms of Peaks of Matched Filter**

**Prob. of Detection**

**Prob. of False Alarm**

**For Matched Filter Algorithm:**
**Analysis Peaks of Matched Filter Output**
**For a 4 percentage point reduction in Probability of Detection receive in return approximately **_**ZERO False Alarms**_
_**Cost: 2.35 J **_
**(470 ms)**

**Threshold**

**Orders of Magnitude Improvement over LMS False Alarm Improvement**

## Monte Carlo Simulation

**Another 100k trials**
**Equal likely     signal+noise and noise alone**
**3 dB SNR**
**2 of 8 subband detections**
**Uniformly Random True TEC (Uniformly random TOA)**
**Full Receiver Model**
**Analog Trigger Box Outputs and Digitized Waveform**

**Conclusion: Post-Processing Reduces False Alarms**

## Implementation

**Analog Trigger**

**Least-Mean Squares Fit**

**Maximum-Likelihood Fit**

**Software Trigger Box**

**Bank of Matched Filter**

**Sub-band Filter **
**frequencies and**
**time of arrivals**

**Digitized Waveform**

**Simple threshold testing of an existing algorithm’s result provides means for false-alarm reduction.**

## Post Processing: Performance and Energy

**2/8**

**47.3 mJ**

**ST**

**LMS**
**ML**

**18.7 uJ**
**1.02 mJ**

**0**

**2.35 J**

**Matched**
**Filter**

## Post-Processing Conclusion

**Using existing parameter estimation algorithms, False-Alarm performance can be governed by energy usage**
**With the general relationship of the more energy expended the greater the reduction in false alarms**

**10**

**-2**

**10**

**-1**

**0.20**

**0.30**

**0.40**

**0.50**

**0.60**

**0.70**

**0.80**

**Probability of False Alarm**

**Probability of Detection**

**Post-Processing Improvement Requires power**

## Website & Acknowledgements

_**[http://www.daps.lanl.gov/pac/index.php](http://www.daps.lanl.gov/pac/index.php)**_
**Download of C code available**
**Interactive web-based data server**** **
**Generates data of simulated ionospheric-dispersed lightning signals after passing through receiver system ( including analog trigger box and analog-to-digital converters).**
**MATLAB****®**** Web Sever: Remote execution of MATLAB****®**** code**
**ACKNOWLEDGMENTS:**
**This effort is sponsored by the Defense Advanced Research Projects Agency (DARPA) .  We are indebted to JPL for the use of their Power PC test bench to measure algorithm power usage.**