Sensor fusion of spectroscopic data and gyroscope accelerations for a direction indication in handheld radiation detection instruments Christian Henke, Elmar Jacobs, Nikolai Teofilov, Peter Henke and Marcus J. Neuer innoRIID GmbH, Am Eichenbroich 19, 41516 Grevenbroich, Germany
Data injection
Introduction
Problem: Determine the direction of radiation, using only one detector. Gyroscope
Solution: Using an accelerometer, build additionally into the instrument, we record the motion path of the detector and use this information to reconstruct the original direction of incoming radiation. To do this, a sensor fusion [1] concept is applied in combination with a Kalman filter to fuse [2] the data from gyrometer and scintillator.
Magnetic compass, Accelerometer
Training cycle (offline)
Smart Sensor “Direction”
3’’ x 1’’ NaI(Tl) Scintillator
z
y
(b)
∆r d D
∆α
x R
∆α
Count Rate [a.u.]
(a)
Evaluation • •
Figure 3: Schematic overview of the sensor fusion as1 it can be used in the final software (black rectangle) and the training loop (blue-grey) used to train the neural network.
Tests and Outlook
Figure 1: Axis defintion for motion description.
Accelerometer bias and scale factor Non-orthogonality of gyros Gyro drift and scale factors Additional noise Table 1: List of noise effects that are generated by the gyroscope sensor.
Error assessment Readjustment of neural weights
Time t [1/20 s]
Figure 2: Movement of detector leads to distinct pattern of count rate, depending on the position of the source.
Motion data coming from gyroscope
Laboratory trials (conducted with prototype in 2016)
I I I
Indicate reasonable and consistent results Show that the use of a Kalman filter is mandatory, as otherwise the results are too noisy Provided new training data to improve performance
User experience testing (will begin in January 2017)
I I I
New approach requires dedicated, new localization user interface Interface studies are ongoing Means of augmented reality are considered right now
1
(c)
(d)
(b)
Angular directions of motion: α, β, γ Gyrometer provides data about angular velocities: dα dβ dγ vα = , vβ = , vγ = (1) dt dt dt Gyrometer provides data about accelerations: d2α d2β d2γ aα = 2 , aβ = 2 , aγ = 2 (2) dt dt dt Data according to the coordinate α is assumed to be panning in the x − y plane or in other words a rotation around the z-axis.
Direction Indicator Icon
(a)
(a)
φ
(b)
(c) 90° waving cone
φ(c)
User Interface displaying the direction to source
Incoming radiation angle
Context: Radioisotope identification instruments often feature only one detector. Yet, users want to find sources more efficiently and some tool is needed to point towards the source.
Labelled directions Predefined hand motion Corresponding spectroscopic responses
Count rate / a.u.
• • •
(d)
Time / 20 / s
Figure 4: Waving motion of the detector (left), yielding several count rate response shapes (right). Based on the shape of the response, the position of the source can be determined.
References
Kalman Filter
Correlation c / a.u.
Figure 5: Learned curve for indicator No. 1, the direct correlation between time series and waving motion.
[1] G. Heredia, A. Ollero, M. Bejar, and R. Mahtani, “Sensor and actuator fault detection in small autonomous helicopters,” Journal of Mechatronics, DOI:10.1016/j.mechatronics.2007.09.007, vol. 18, pp. 90–99, 2008.
Table 1 shows the various occurences of noise in the sensor and within the integrations, Z t0 ~v (t) = ~a(t)dt, (3)
[2] F. Olsson, M. Kok, K. Halvorsen, and T. B. Sch¨on, “Accelerometer calibration using sensor fusion with a gyroscope,” in 2016 IEEE Statistical Signal Processing Workshop (SSP), pp. 1–5, June 2016.
t0−∆t
[4] Y. Zhibo, Y. Chunshan, and D. Zili, “Guaranteed cost robust modified covariance intersection fusion kalman filter for multi-sensor system with uncertain noise variances and random missing measurements,” in 2016 12th World Congress on Intelligent Control and Automation (WCICA), pp. 3201–3207, June 2016.
these noise has to be treated by means of a Kalman filter [3, 4]. Let the state measurement given by α ~ = (α, vα, aα) and let ξ~ be the state space estimate, ξ~k = Aξ~k−1 + ω ~k (4) ~yk = Cξ~k + ~νk (5)
[3] Y. Wu, Q. Zhang, and Z. Shen, “Kalman filtering with multiplicative and additive noises,” in 2016 12th World Congress on Intelligent Control and Automation (WCICA), pp. 483–487, June 2016.
This Poster
innoRIID
Researchgate profile
This Kalman Filter was implemented in Python for initial prototyping tests and in C++ for deployment.
http://www.innoriid.com
corresponding author:
[email protected]
