Transcript Design of a Sensor Board for an Acoustic Traffic

Design of a Sensor Board for
an Acoustic Traffic Monitoring
System
Gina Colangelo
Tufts University
Masters’ Project
12/02/2008
Project Goal
To enable the study of a new technique for
traffic monitoring using an acoustic array
sensor network by:
defining the system architecture of the
sensor array board
deriving the system specifications of the
sensor array board
designing a PCB for the sensor array
board and the interface to the wireless link
Presentation Outline
Introduction to Traffic Surveillance Networks
Background on Passive Acoustic Sensors
System Architecture
System Specifications
Part Selection
Schematic Capture
PCB Layout
Future Goals
Technologies used for Rural Traffic
Surveillance
National Survey conducted in 2004 by ITS1 found:
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26 states use Inductive Loop Detectors (ILD)
21 states use Radar Detectors
11 states use Video Image Detectors (VIDS)
5 states use Acoustic Detectors
Vehicle info collected by the sensor networks include:
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1
Traffic Volume
Vehicle Speed
Vehicle Classification
Travel Time
Incident Occurrence
ITS or Intelligent Transportation System is a division of RITA which is part of the
U.S. Department of Transportation (DOT).
Inductive Loop Detectors (ILD)
How it works:
– 1 or more loops of wire are embedded under
the road & connected to a control box.
– When a vehicle passes over or rests on the
loop, inductance is reduced showing a
vehicle is present.
Benefits
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Established Technology
Not impacted by environmental conditions
Accurate in detecting vehicle presence
Performs well in both high and low volume
traffic
Disadvantages
– High Cost (up to $10k for initial costs)
– Invasive Installation
– Potential poor reliability due to improper
installation
– Not viable for certain locations and road
conditions
– Unable to directly measure speed or direction
Video Image Detection (VIDS)
Employs machine vision technology to
automatically analyze traffic data
collected w/ Closed Circuit Television
Systems (CCTV)
Benefits:
– Rapid Incident detection
– Wide area detection capabilities (multilane, multi-direction)
– Vehicle classification
– Estimates traffic queues and speed
– Installation does not require lane closures
– Can be integrated with other sensor
networks
Disadvantages:
– High Cost (initial cost > $50k)
– Higher Power than other sensor networks
– Light conditions can affect surveillance
Radar-based Roadside Sensor
How it works:
– Transmits radar pulses
– A portion of the energy is reflected or
scattered from the vehicle and roadway
back toward the sensor
– This energy is received and interpreted.
Benefits
– Low Power
– Most accurate technology for detecting
speed
– Traffic Count accuracy
– Easy installation
– Low cost
Disadvantages
– Accuracy can be affected by weather
conditions (hail, snow, rain)
– Directional Detection is poor
– Interference could occur with other RF
devices
Passive Acoustic Sensors
Passive Road-side Sensor that
receives sound waves from passing
vehicles.
Benefits:
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Low Power
Low Cost
Easy to Install
Directional and Multi-lane Detection
Accurately measures traffic count
Disadvantages:
– Accuracy affected by environment
factors
– Speed measurements are not as
accurate as other methods
Summary of Traffic Surveillance
Technologies Employed Today:
Technology
Traffic
Volume
(Moving)
Traffic
Volume
(Stopped)
Vehicle
Speed
Vehicle
Classification
Incident
Detection
Power
Cost
ILDs
A
A
C
D
C
B
C
Radar
A
A
A
C
C
B
A
VIDs
B
B
B
A
A
C
C
Passive Acoustic
B
C
B
B
C
A
A
Code: A – Excellent; B – Fair; C- Poor; D – Nonexistent; U – Unknown
Radar and Acoustic sensors are the least expensive to deploy and are the
lowest power.
VIDS collect the most information and the data processing possibilties are
endless.
Acoustic sensors are the least accurate, but the technology is relatively new.
Acoustic sensors for traffic monitoring has room for improvement:
– Improve accuracy
– Ability to detect idle traffic
– Intelligently process data (Vehicle Classification, Incident Detection)
Acoustic Array Sensor
Research has shown that
using arrays of acoustic
sensors narrows the detection
zone for improved SNR &
better accuracy
For this project the following
configuration will be evaluated:
– 4 microphone arrays per sensor:
2 arrays form a pair parallel to
the road
2 arrays for a pair orthogonal to
the road
Each array contains 12
microphones
– Sensors to be deployed road-side
about 10m above the road.
Sensor Network System Details
Sound detected by each
element in a single array
will be summed together
and amplified.
4 analog outputs will be
digitized separately and
processed.
Processed signals will be
transmitted to a gateway
node within the wireless
sensor network.
Signal Processing: Parallel Pair
A vehicle moves across the detection zone (D2 to D1):
– As a vehicle approaches, sound reaches D2 earlier than
D1. The time delta will be negative (τ<0).
– When a vehicle is at the center of the detection zone, τ=0.
– As a vehicle exits the detection zone, τ will be positive.
The rate of change or slope across the detection zone
corresponds to the vehicle speed.
To extract the time delay from the actual signals, the
cross correlation of D1 and D2 is calculated:
C12 ( )  D1 (t )  D2 (t )  s(t )  s(t  )  C(  )
Signal Processing: Orthogonal Pair
A vehicle moves across the
detection zone:
– As the vehicle approaches, sound
will reach D1 earlier than D2. The
time difference will be positive and
increasing (τ<0).
– When the vehicle is at the center
of the detection zone, τ will peak.
– As the vehicle exits the detection
zone, τ will be positive and
decreasing.
The sound map for vehicles in
lanes closer to the sensor
(smaller y) will have smaller
peaks than those in lanes
further from sensor.
System Architecture
2 Boards to form complete system:
– Sensor Board
Microphone Arrays
Summing Stage
Amplification Stage
– System Board
Analog-to-Digital Converter
Processor
RF Transceiver (ISM band of 2.4GHz)
eZ430-RF2480 Demo Kit from TI
chosen for System Board
– USB-based wireless demo tool
– MSP430F2274 Mixed-Signal
Microcontroller
– CC2480 2.4GHz ZigBee network
processor
– 2.4GHz Antenna
Sensor Board Architecture
For initial prototype, 1 sensor board per array to allow for
array spacing experimentation
Majority of System Gain implemented on Sensor board
to maximize SNR.
Clipping Circuit and Anti-Aliasing Filter may be needed to
condition signal for ADC.
System Board Architecture
MSP430 Mixed-Signal Microcontroller
– AUX Op-Amps can provide extra gain if needed
– On-chip 10-bit ADC can multiplex in 4 analog channels for
digitizing
– CPU can be programmed to compute cross-correlation functions
CC2480 – ZigBee Processor to transmit data to gateway
node to main control center over 2.4GHz ISM band
Determining Sensor Board Specs
Power Supply Requirements
Maximum Voltage Output Swing
Element Spacing
Summing Stage Configuration
System Gain
Anti-Aliasing Filter
Power Supply Requirements
2 Specifications need to be determined:
– Supply Voltages
– Maximum Current
Sensor Board needs to be portable solution
– Low power
– Battery operated
Reuse Battery Board included with eZ430RF2480 Demo Kit:
– 2 AAA Batteries in series: 3V supply
– Capacity of AAAs: 900-1155mA/h
Maximum Voltage Output Swing
Needs to be limited to Input Range of ADC.
– Depending of ADC front-end architecture, over voltage on inputs
can cause conversion errors and in some cases damage the
ADC.
10-bit ADC integrated on the MSP430 will be used.
– ADC input range is programmable from Vcc to Vss.
– For this project, ADC will be programmed to accept an input
range of 3V to 0V.
No Clipping Circuit needed on Sensor Board
– Amplifiers on Sensor board will not produce a voltage higher
than its supply voltage
– ADC on the MSP430 clips any signals greater than the
programmable upper input range limit.
– Benefits from using the same power source solution!
Element Spacing
Spacing b/t array elements will be
chosen to achieve the desired
detection angle.
Desired Detection Angle:
– Sensor board mounted 10m above road
(z=10m)
– Desired detection zone is 2.5m in any
direction at road level.
– Detection Angle can be calculated:
 2.5m 
  0.245radians
10
m


 360 
  0.245radians 
  14
2



  tan1 
Experiment to Determine
Element Spacing
Several 3x4 element arrays
bread-boarded w/ summing
stage:
– Array 1: 1.75cm x 1.75cm
– Array 2: 2cm x 2cm
– Array 3: 2.5cm x 2.5cm
Detection Angle measured by
moving a 4kHz sound source
across the array from a fixed
distance.
Vpk-pk measured at summer
output with Oscilloscope
Results of Element Spacing
Experiments
Measuring Detection Zone 4x3 Array (2cm x 2cm)
z = 43inches
Measuring Detection Zone 4x3 Array (1.75cm x 1.75cm)
z = 43inches
trial 2
3dB_point
140
100
120
80
60
40
20
trial 2
3dB_point
average
100
80
60
40
20
0
0
-20 -18 -16 -14 -12 -10 -8
-6
-4
-2
0
2
4
6
8
10 12 14 16 18 20
Measuring Detection Zone 4x3 Array (2.5cm x 2.5cm)
z = 43inches
trial 1
trial 2
3dB_point
average
180
160
140
120
100
80
60
40
20
0
-20 -18 -16 -14 -12 -10 -8
-6
-4
-2
0
2
4
6
x location (inches from origin)
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-20 -18 -16 -14 -12 -10 -8
-6
-4
-2
0
2
4
6
x location (inches from origin)
x location (inches from origin)
Measured Vpk-pk (mV)
trial 1
average
Measured Vpk-pk (mV)
Measured Vpk-pk (mV)
trial 1
120
10 12 14 16 18 20
8
10 12 14 16 18 20
Summing Stage Configuration
Original Design used 1 op-amp to sum all 12 elements
New Design sums the elements in 2 stages:
– First stage sums the 3 rows of 4 elements separately
– Second stage sums the 3 outputs of the 1st stage
New Design allows for more gain without large R
– Improved Rise/Fall Times (τ = RC)
– Increased Bandwidth – less Gain per stage (f3dB = GBW/Gain)
Determining System Gain
Experiments conducted to estimate
the amount system gain required in
the real traffic monitoring
environment.
Microphone array prototype board is
not portable, and output cannot be
stored.
A portable digital sound recorder was
used to collect field samples:
– 2 Omni-Directional Electret Condenser
Microphones
– Mic positions can be configured from 45º
to 135º (90º was chosen)
– 7 Pre-Amp Levels
– Recorded sound clips saved as *.WAV
System Gain Experiment
Step 1 – Calibrate the digital recorder to the microphone array in a
controlled environment with same sound source for each Pre-Amp
Level.
– Measured in Lab w/ 4kHz sound source
– Array and Recorder in same location for the measurement
Step 2 – Collect Field Data for each applicable Pre-Amp level
– 5-10s traffic samples were recorded from a height of 25-30ft from the
road
– Average of 5 vehicles passed the recorder during each sound clip
including motorcycles, small cars, and SUVs.
Step 3 – Download and process *.WAV files to determine peak
sound levels
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MATLAB code was written to analyze these files
The start and end of each file was removed to eliminate interference
The 2 microphone outputs were summed
The max peak and rms levels were calculated.
Step 4 – Relate the peak sound levels from digital recorder back to
the microphone array and determine the required system gain.
System Gain Results
Array Output = 100mVpk-pk
PreAmp Level 3 optimal in field
Digital Recorder Peak and RMS Levels from 4kHz Signal in Lab vs. Pre-Amp Level
To calculate Vpk-pk at the array
output the following formulas were
used:
1.
Sound_ Src _ Ratio 
max_ peak _ sumtraffic
Digital Recorder Peak and RMS Levels from Traffic Sound Clips
max_ peak _ sum4 kHz
2.
Array _ Out4kHz  0.1Vpk  pk
Calculating the Sound Source Ratio
3.
Array _ Outtraffic  Sound_ Src _ Ratio Array _ Out4kHz
Total System Gain Needed = 4 x 3.5 = 14
Estimated Array Output Vpk-pk and Recommended Gain Settings
Anti-Aliasing Filter
Anti-aliasing filter should be placed before
ADC
– Prevents harmonics, spurs and broadband
noise outside of Nyquist from aliasing back inband
– Improper filtering leads to a decrease in SNR,
a reduced dynamic range and an increase in
unwanted spurs.
ADC input bandwidth/channel:
– clock range: 450kHz - 1.5MHz
– Nyquist BW: 225 – 750kHz
– 4 chs muxed, BW per ch = 56.25 – 187.5kHz
Frequencies of Interest < 10kHz
Filter Design
– Low-pass Butterworth – flat pass- & stop-band
– F3dB = 20kHz for flatter phase in pass-band
– 1st order provides ~20dB attenuation at
FADC/2.
Since microphones have a frequency roll-off
response, 1st order should be adequate.
Summary of Board Specifications
Parameter
Specification
Power Supply
3V only (2 AAA batteries)
Current Dissipation
As low as possible
Microphone Spacing
2.25cm x 2.25cm (detection angle ~ 16º)
System Gain
14 or 11dB (10*log(14))
Maximum Output Swing
0-3V
Low Pass Filter
f3dB = 20kHz, Order = 1
Schematic - Circuit Design
Architecture is finalized
Active Components have been selected:
– Analog Devices AD8544 Quad Rail-to-Rail OpAmp
Single Supply Operation: 2.7V to 5.5V
GBWP ~ 1MHz
Low supply current 45uA/amplifier
– Emkay MD9745APZ-F Omni-Directional Microphone
Operating Voltage: 2.0V to 10.0V
Frequency Range: 100Hz to 10kHz
S/N Ratio: > 55dB
Altium Designer 6 software chosen for PCB Schematic
and Layout
Schematic Page 1
Schematic Page 2
PCB Layout
4 Layer Board
– 2 Signal Layers
– GND and Power layer
Board Size is 4’’ by 2.5’’
All symbol footprints are either finalized or
common footprints
PCB Layout – Top Layer
PCB Layout – Bottom Layer
PCB Layout – Layer 2 (VCC)
PCB Layout – Layer 3 (VSS)
Prototype Results (Breadboard)
Current Dissipation
Theoretical
Actual
20
Freq Response Normalized to 0dB (dB)
– 2.6mA from single 3V supply
(original design 15mA from 5V,
+9V, and -9V supplies)
– Sensor board can operate for
approx 400hrs before recharging
Sensor Board Frequency Response
0
-20
-40
-60
-80
-100
-120
1000
10000
100000
1000000
Frequency (Hz)
Frequency Response:
Frequency Response
1st Order Filter Solo
Normalized Frequency
Response (dB)
– Op-Amp 1: f3dB= GBWP/Gain =
1MHz/3 = 333kHz
– Op-Amp 2: f3dB = 1MHz/5 =
200kHz
– Microphone: f3dB = 10kHz
– LPF: f3dB = 20kHz
10
5
0
-5
-10
-15
-20
-25
-30
-35
-40
100
1st Order Filter in System
1000
10000
Frequency (Hz)
Note: Using 1 stage summer, f3dB=1MHz/15 = 66kHz
Single Mic
100000
Future Work
Fabricate, Assemble, and Test Sensor Board
PCB
Interface Sensor board with Main system board
– Configure ADC on MSP430
– Write code to perform cross-correlation and store
results
Perform experiments on Array Spacing
Collect Field Data with this Portable prototype
References
Chen, Shiping, Ziping Sun, and Bryan Bridge.
“Traffic Monitoring Using Digital Sound Field
Mapping.” IEEE Transactions on Vehicular
Technology 50.6 (2001): 1582-1589.
Intelligent Transportation Systems. 2008.
http://www.itsdeployment.its.dot.gov/
ITS Decision, Project of the California Center for
Innovative Transportation at UCal Berkeley.
2007. http://www.calccit.org/itsdecision/
Acknowledgements
Yuping Dong
– PhD Student conducting research in this area
Professor Chang
– Project Advisor
Tufts Wireless Lab