Targeting and Geolocation Accuracy / Electromagnetic Railgun Instrumentation
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1 Targeting and Geolocation Accuracy / Electromagnetic Railgun Instrumentation Track Chair: Jim Hughes Track Members: GET CONNECTED to LEARN, SHARE, AND ADVANCE.
2 An Automated Visual Scoring Algorithm for Assessing Line of Sight Gunfire Accuracy Chris Weiland, PhD John Busic, PE Jon Yagla, PhD Naval Surface Warfare Center Dahlgren Division Code G65
3 Line of Sight Example: 30mm Gun on Aircraft Aircraft orbiting target while shooting How do you score these tests? Typically 3-4 seconds round time of flight Ground is moving/aim point changing
4 Desired Features of an Visual Automated Scoring System Inexpensive Replace spotters with one video camera (on blimp, balloon, UAV, etc) Accurate Remove the human from round scoring to give repeatable, quantitative measurements Quickly Compute Results Video signal feeds directly into computer for real-time processing OR Video signal is recorded for later post-processing NSWCDD is working on algorithms for two systems: A line of sight system (subject of this talk) A non-line of sight system
5 Scoring LOS Gunfire When the trigger is pulled, there is a bullet flight time depending on altitude, bullet speed, etc Put crosshairs on target in Image 1 and pull the trigger Bullet impact occurs several seconds later in Image 100 We must account for this later translation/rotation to score the bullet impact relative to the original aim point
6 M=0.025 y/c y/c M=0 y/c y/c 0 0 Image Registration and Tracking Algorithm The motion correlation function is currently -0.4 used as a postprocessing -0.6 tool is based on an existing -0.6algorithm developed for Digital Particle Image Velocimetry (DPIV), an experimental method in fluid mechanics x/c First Mode A x/c Second Mode D Correlate Particles B in Flow Field E x/c First Mode x/c Weiland, C. and Vlachos, P. (2009) A mechanism for mitigation of blade-vortex interaction using Second leading Mode edge blowing flow control. Experiments in Fluids, Vol 47, No 3, pp
7 Image Registration and Tracking Algorithm
8 Using VASS to Track Post-Trigger Ground Motion Once we know how the images shift relative to one another, apply an affine transform to de-rotate/translate image 2 All images are now in the same coordinate system
9 Gaussian Surface FFT of Image Spectral Identification of Low-Signal Regions and Removal Correlation algorithm depends on regions of high spectral information If regions of low information are present, bad correlations follow To minimize this error, only correlate regions of high spectral intensity Multiply in complex plane Identify Mask Low-pass Filtered Image
10 Scoring Shots in Single-Fire Mode
11 VASS In Operation Land Based Targets Aerial images of a 30mm round exploding on ground impact Correlation algorithm used to de-rotate/de-translate target area when round is fired for comparison to future images Algorithm has already proven itself to auto-detect when and where rounds falls (20 shots auto-detected from over 20,000 images) Round Impact Round Impact
12 VASS In Operation Land Based Targets What type of information do we gather? Miss distance (miss in x & y coordinates) Time of flight (if shot time is known) Data can be scored in both normal or ground planes
13 Scoring Shots in Burst-Fire Mode
14 Scoring Shots in Burst-Fire Mode
15 Gunfire Errors by Type in Real Time Total error described as (for the i th shot): E(i) = f(i) + α(i) + β(i) f(i) due to bias errors Meteorological conditions Forward observer target identification α(i) due to correlated errors Systematic and time dependent errors Servo hunt Gun whip β(i) due to uncorrelated errors Performance variability of projectiles Random Gaussian distribution Precise identification of the fall of shot location can potentially eliminate bias errors in the fire control solution Precise identification of the fall of shot location in real-time can potentially reduce correlated errors If we can measure shot groups in time we can potentially account for both bias and correlated errors in the Fire Control Solution
16 Automated Video Scoring Algorithms Have Great Potential Works for both direct and indirect weapon systems If we can see the impact we can probably score the shot Naval guns Mortars/artillery Increase the accuracy of gunfire (LOS or non- LOS) Determine in real time where rounds fall in relation to target Errors in the gun system are treated without pre-action calibration Acknowledgements: This work was sponsored through the NSWC Dahlgren Laboratory Independent Applied Research program Mentors: John Busic and Dr. Jon Yagla
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