Towards airborne seismic. Thomas Rapstine & Paul Sava
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1 Towards airborne seismic Thomas Rapstine & Paul Sava
2 Research goal measure a strong motion signal from an airborne platform 2
3 Proposed method Monitor ground motion from an UAV using cameras Unmanned Aerial Vehicle (UAV) 3
4 Successful scenarios no drone motion known drone motion Rapstine, Sava, & Arias (2017) 4
5 Successful scenarios no drone motion known drone motion Rapstine, Sava, & Arias (2017) 5
6 Assumptions Past work: exact platform motion Present work: noisy observations of platform motion 6
7 Assumptions Past work: exact platform motion Present work: noisy observations of platform motion 7
8 Assumptions Past work: exact platform motion Present work: noisy observations of platform motion 8
9 Research question What is the character of noise in UAV motion observations? Is noise white? uncorrelated? Gaussian? noise character band-limited? sparse? 9
10 Research question UAV motion monitored by onboard sensors What type of noise is in observed UAV motion? Position: X, Y, Z ψ φ θ Orientation:,, Unmanned Aerial Vehicle (UAV) 10
11 Noise measurement experiment Experiment stationary motion sensor 1 hour of noise observations Measurements Position: X, Y, Z Orientation: φ, θ, ψ ~3,600,000 samples acquisition by Honeywell (2018) 11
12 Noise time series 12
13 Is noise white? Noise is not white 13
14 Is noise Gaussian? Positional noise is not Gaussian. 14
15 Is noise Gaussian? Orientational noise is not Gaussian. 15
16 Is noise band limited? X-position 16
17 Is noise band limited? Y-position 17
18 Is noise band limited? Z-position 18
19 Is noise band limited? Z-position more white higher noise less white lower noise 19
20 Is noise band limited? Z-position 5 Hz sensor modal frequency? Positional noise is not band limited 20
21 Is noise band limited? φ 21
22 Is noise band limited? φ sensor rotation? broadband signal observed at 900 seconds 22
23 Is noise band limited? θ 23
24 Is noise band limited? ψ 24
25 Is noise band limited? ψ sensor rotation? broadband signal observed at 900 seconds 25
26 Is noise band limited? ψ Orientation noise behavior less time variant than position noise 26
27 Is noise band limited? UAV motion observation noise is not band limited 27
28 Is noise correlated? Positional noise is correlated 28
29 Research question What is the character of noise in UAV motion observations? Is noise white? uncorrelated? Gaussian? band-limited? sparse? 29
30 Research question What is the character of noise in UAV motion observations? Is noise white? uncorrelated? Gaussian? band-limited? sparse? In what domain does signal appear sparse? 30
31 Is the noise sparse? 31
32 Is the noise sparse? 32
33 Is the noise sparse? Wavelets sparsely represent piecewise smooth functions noise signal reconstruction using 10% of wavelets 33
34 Concluding insight What is the character of noise in UAV motion observations? comparable to ground motion signal non-white non-gaussian time varying correlated sparse 34
35 Future steps Use data to discover sparse domains Key challenges Finding incoherent sparse domains for both signal and noise 35
36 Backup slides
37 Independent Component Analysis (ICA) Find a matrix that best separates observations into statistically independent sources by minimizing an objective function. min W φ(w ) = H (X) H (X Y ) information content difference after observations Entropy X = WY : separated source 1 and 2 (information content) Y = XM : observation of mixed sources H (X) = P(x i )log(p(x i )) (See Amari et al for derivation) x i X
38 Is noise correlated? 38
39 Is noise correlated? 39
40 Is noise correlated? 40
41 Seismic signal vs. platform motion Measure this. ground motion from a platform moving like this. UAV motion
42 UAV motion and ground motion are mixed from an onboard sensor IMU and from a camera. computer vision IMU = Inertial measurement unit
43 How are these signals different? ground motion UAV motion
44 How are these signals different? frequency spectra overlap! Disclaimer: individually rescaled frequency spectra for plotting. (UAV motion is actually much higher energy than ground motion)
45 Machine learning Given large amounts of data, learn a non-linear mapping from available inputs to valuable outputs A D X B Y E C Available inputs: features or data The machine: Non-linear mapping Valuable outputs: labels or predictions
46 Machine learning Given large amounts of data, learn a non-linear mapping from available inputs to valuable outputs A D X B Y E C Available inputs: features or data The machine: Non-linear mapping Valuable outputs: labels or predictions
47 Machine learning Given large amounts of data, learn a non-linear mapping from available inputs to valuable outputs A D X B Y E C Available inputs: features or data The machine: Non-linear mapping Valuable outputs: labels or predictions
48 Machine learning Given large amounts of data, learn a non-linear mapping from available inputs to valuable outputs A D X B Y E C Available inputs: features or data The machine: Non-linear mapping Valuable outputs: labels or predictions
49 Machine learning Given large amounts of data, learn a non-linear mapping from available inputs to valuable outputs A D X B Y E C Available inputs: IMU data LDV data Stereo vision data The machine: Non-linear mapping signal separation Valuable outputs: ground motion
50 Machine learning Given large amounts of data, learn a non-linear mapping from available inputs to valuable outputs A D X B Y E C Major requirement: require network that handles variably-sized inputs Recurrent Neural Networks (RNNs) do this :)
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