Senior design final presentation master
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Transcript of Senior design final presentation master
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SENIOR DESIGN PROJECT
SPRING 2016
Christian Ladigoski
Advisor: Dr. David Weissman
DESIGN OF A FREQUENCY MODULATED – CONTINUOUS WAVE (FM-CW) PEDESTRIAN-RECOGNITION AUTOMOBILE
RADAR AND ALGORITHMS FOR USE IN FOG
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OUTLINE OF WORK• General Automobile Radar System Parameters
• Antenna Design, Response and Analysis
• Phased Array Design, Response and Analysis
• Design of the FM-CW Waveform
• Signal Processing
• Improved Performance in Fog
• Constant False-Alarm Rate Detection
• Pedestrian Recognition and Beam-scanning
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Figure 1. Concept Behind Pedestrian Recognition
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Figure 2. The many radar systems that currently exist in modern cars
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GENERAL RADAR SYSTEM PARAMETERS• Problem statement: Pedestrian detection is a problem that becomes complicated with the
presence of fog; on a clear day, there is typically no problem seeing up to 100 meters in front of your vehicle – however, visibility decreases a great amount when the intensity of fog rises.
• Operating Frequency: 77 Ghz Wavelength of 3.9mm/0.0039m
• Two options:
• 76-77 Ghz (Narrowband / Long Range) – This is our design choice
• 77-81 Ghz (Wideband / Short Range)
• Waveform: Linear Frequency-Modulated Continuous Sinusoidal Wave
• Phased Array Antenna (Two Uniform Rectangular Arrays)
• Maximum intended range: ~100m
• Antenna Azimuth Beamwidth: ~12 degrees
• Antenna Elevation Beamwidth: ~16 degrees
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Figure 3. Generalized Block Diagram of a Bistatic FMCW System
Time
Frequency
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FIG. PATCH ANTENNA & [20 X 36] ULA
Figure 4. Patch Microstrip Antenna Element with Substrate
Figure 5. Layout of a 20x36 Element Array
Substrate
Element
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ANTENNA DESIGN (1)• Patch Microstrip
• Operating Frequency: 77 GHz
• Length = 1.098 mm
• Width = 1.353 mm
• Height = 6.000 mm
• Dielectric Permittivity = 2.1 (Teflon)
• Dielectric Loss Tangent = 0.002
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Figure 6. Inset Feed Patch Antenna
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ANTENNA DESIGN (2)• In order to ensure reasonable power transfer to the antenna and account for return loss, impedance
matching is key.
• Implementation: MICROSTRIP LINE FEED
• Characteristic antenna impedance is approximately 177 ohms
• To match a 50 ohm transmission line, the inset feed would be placed at approximately L/3.1 into the center of the patch, where L = length of the element.
• Microstrip Line Feeding versus Coaxial Line Feeding
• Both struggle with thicker substrates
• Both are limited by narrow bandwidth
• Coaxial Line Feeding requires higher order modes due to asymmetries, which fosters cross polarization
• Microstrip Line Feeding is easier to manufacturer and cheaper, is easier to match (changing inset feed point)
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Figure 7. 3D Directivity Pattern of the 20 x36 Phased Array
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PHASED ARRAY (1)• Array is a matrix of patch elements, N x M (rows by columns) elements
• Design: 20 x 36, 1 Transmit Uniform Rectangular Array (URA), 1 Receive URA
• This translates to an elevation beam-width of approximately 12 degrees and an azimuth beam-width of approximately 16 degrees
• Element Spacing of approximately half of a wavelength (0.00195 Meters) to avoid deleterious effects that allow signals from unwanted directions from being observed
• Directivity describes the directionality of the beam of the array (a smaller solid angle equates to a higher directivity). Peak directivity of approximately 32 dBi at 0 degrees Azimuth / 0 degrees Elevation
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Figure 8. Azimuth Cut of Beam (Elevation Angle is 0 degrees)
Figure 9. Elevation Cut of Beam (Azimuth Angle is 0 degrees)
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LFM-CW WAVEFORM• Sample Rate = 150 MHz (Rate of transmission of identical pulses)
• Sweep Bandwidth = 300 MHz
• This yields a range resolution of 0.5m (Range resolution determines the ability to separate/resolve individual targets)
• Sweep Time = 2 microseconds
• Estimated with consideration that the maximum intended range is 100 meters.
• Sweep type: Double Triangular
• This was chosen not only to resolve ambiguity between range and Doppler but also to aid in a common FM-CW radar issue: ghost targets
• The sweep slope (2B/Tr) will differ for the second triangular sweep.
• We decided to test the feasibility of the waveform in detecting a moving target relative to a moving source.
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Figure 10. Principles of how FMCW Waveforms Work
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Figure 11. Double Triangular Sweep waveform
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Figure. Power Spectral Density of the return signal; The (narrowband) dechirped signal obtained from a target echo utilizating FMCW
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Figure 12. Range-Speed Response Pattern of the same target.Range is approximately 30 meters and speed (relative to source) is approximately 6.5 m/s.
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IMPROVED PERFORMANCE IN FOG (1)There are two things to consider that could disrupt the signal: atmospheric attenuation and weather conditions such as rain and fog.
This first graph shows atmospheric attenuation -- dependent on humidity, temperature, and frequency of the radar (77 GHZ).
Attenuation at 77GHZ (Appx 0 dB/km)
APPX 77GHZ
Figure 13. Atmospheric Attenuation vs Frequency
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IMPROVED PERFORMANCE IN FOG (2)The second causality includes attenuation caused by weather conditions -- in this case, fog.
Data was analyzed for medium density fog (at 300m visibility) and heavy density fog (at 50m visibility).
There is a slight offset from attenuation that is considered when detecting pedestrians.
Medium Fog = + 0.15 dB/kmHeavy Fog = + 1.5 dB/km
APPX 77GHZ
Attenuation at 1.5 dB/km
77GHZ
Figure 14. Fog attenuation vs Frequency
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Figure 15. Specific Attenuation Constant for Fog (“K”) with respect to Frequency
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Figure 16. Path Loss Due to Fog vs. Range
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Figure 17. SNR with FMCW Signal and Fog Attenuation
(We need a minimum SNR of 5 dB to detect at max range of 100 meters)
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SIGNAL PROCESSING – PEDESTRIAN RECOGNITION (1)
• To detect pedestrians, it is important to realize that the magnitude of their power return is typically much lower than that of a car, truck, etc. Likewise, their Doppler shift is typically expected to be much larger (relative to a moving car) than that of a moving vehicle nearly matching the source speed.
• Targets will then need to be classified based on two big factors:
• The magnitude of the returned power – this should give consideration to the fact that a pedestrian’s radar cross section (RCS) fluctuates greatly, and is decreased with the presence of fog. The expected range of a pedestrian RCS is from -5 dBsm (roughly 0.37m2 ) to 0 dBsm (1m2).
• Doppler spectrum – Doppler filters will need to be used.
• Classification and detection should be done within the time it takes for a car (going at a certain speed) to stop.
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CAR SPEED AND STOPPING DISTANCES• Stopping Distance For a Car Going…
• 80 MPH: 306 Feet or 93.3 Meters
• 60 MPH: 172 Feet or 52 Meters
• 40 MPH: 76.5 Feet or 23.3 Meters
• 20 MPH: 19.11 Feet or 5.8 Meters
• 10 MPH: 4.77 Feet or 1.45 Meters
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SIGNAL PROCESSING – PEDESTRIAN RECOGNITION (2)
Figure 18. Power Transmitted vs. Radar Cross Section of Pedestrian (Target to 100 meters)
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SIGNAL PROCESSING – PEDESTRIAN RECOGNITION (3)
Figure 19. Power Received vs. Radar Cross Section of Pedestrian (14.5W of Transmitted Power)
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SIGNAL PROCESSING – ANTENNA BEAM-SCANNING (1)• For our purposes, it is important to get an accurate direction-of-arrival estimate. Beam-
scanning is a method that will allow the array beam to be scanned over a region of interest and look for a target.
• We utilized beam-scanning to see the response when two targets are realized in a region of interest.
Figure 20. Two target case - one at 40° Az/9° El and another at -12° Az/-2° El.
Figure 21. Closely spaced targets, one at 0° Az/8° El and another at -2° Az/ 9° El.
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SIGNAL PROCESSING – ANTENNABEAM-SCANNING (2)• As shown in the previous figure, beam-scanning by conventional means fails to resolve
closely spaced targets – crucial for our purposes. Thus, we decided to utilize a Minimum Variance Distortionless Response (MVDR) method to beamscan.
Figure 22. Two target case - one at 40° Az/9° El and another at -12° Az/-2° El.
Figure 23. Closely spaced targets, one at 0° Az/8° El and another at -2° Az/ 9° El.
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SIGNAL PROCESSING – CONSTANT FALSE ALARM RATE (CFAR) DETECTION (1)
• A threshold is the level at which we expect the power of a return to be before being considered a target of interest. Threshold must be set at a “comfortable level” – that is, there must be a balance between probability of detection of a true target and probability of false alarm
• Threshold level:
• High: Low probability of detection, many missed targets
• Low: High probability of detection, many false alarms (Preferred)
• Must be made variable to account for interference, noise, cluttering, etc.
• Note:
• Further distances – influence of noise level is higher
• Closer distances – influence of clutter level is higher
• Therefore false alarm rate must take range into account
• (For our scenario, is it important to note that having higher probability of detecting noise, clutter and other interference above the threshold is preferred for safer driving conditions)
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SIGNAL PROCESSING – C.F.A.R DETECTION (2)HOW DOES CFAR WORK?
• Method: Cell-Averaging CFAR Detector
• The circuit will estimate the noise/clutter level around the target cell to determine if the target cell is of our interest or not.
• Circuit will determine if the power threshold is considered to be above a level expected from a target of interest.
• We tested varying thresholds as to see which would result in a small probability of false alarms while still realizing pre-set targets at 35, 55, and 57 meters from the radar source.
Figure 24. Concept of CFAR Detection
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Probably Detection at 0.9False Alarm Probability at 1e-6Targets at 35, 55 and 57 meters
Figure 25. Randomized Noise vs Range
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Figure 26. CFAR Detection with High Threshold
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Figure 27. CFAR Detection with Low Threshold
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SIGNAL PROCESSING – CFAR DETECTION 3
Figure 28. Beam-scanning from -45° to +45° Azimuth (two targets)
Figure 29. Two targets meeting the threshold determined by noise power
Targets
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ACKNOWLEDGEMENTS• Thank you to Dr. David Weissman, Professor Hausman as well as Hofstra University
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• Gaussian Noise - http://www.gaussianwaves.com/2015/06/how-to-generate-awgn-noise-in-matlaboctave-without-using-in-built-awgn-function/
• CFAR - http://www.mathworks.com/help/phased/examples/constant-false-alarm-rate-cfar-detection.html
• Fog Detection - https://www.itu.int/dms_pubrec/itu-r/rec/p/R-REC-P.840-3-199910-S!!PDF-E.pdf
• http://www.mathworks.com/help/phased/ref/fogpl.html
• https://www.itu.int/rec/R-REC-P.840-6-201309-I/en
REFERENCES