Simulation-Based Resilience Quantification of an Indoor Ultrasound Localization System in the Presence of Disruptions
Abstract
:1. Introduction
1.1. Related Work
1.2. Structure of This Paper
2. Proposed Simulation Framework
2.1. Acoustic Simulation
2.2. Disruption Modeling
3. Localization System under Test
3.1. TDOA Estimation
3.2. Position Estimation
4. Resilience Quantification
- r1: Performance before disruption,
- r2: Performance after delay time,
- r3: Maximum performance loss,
- r4: Temporary performance level, during response phase while disruption continues,
- r5: Performance during the end of disruption, beginning of recovery phase,
- r6: Performance after total recovery.
- t1: Time of sudden disruption,
- t2: End of delay time, begin of protection phase,
- t3: Time to reach maximum performance loss,
- t4: Time when response-reaction occurs,
- t5: End of disruption, beginning of recovery,
- t6: End of complete recovery, when the pre-disruption state is restored.
- “5”: rate describes the rate of maximum performance loss during “protect phase”,
- “12”: rate is the rate of bounce back to the pre-disruption state during “response and recover phase”.
5. Evaluation and Discussion
5.1. Setup and Disruptions
5.2. Single Disruptions
5.3. Multiple Disruptions
5.4. Time-Dependent Resilience Curves for Linear Loss Function
- 1.
- Consecutive chirps according to Equation (2) are spaced by 50 ms.
- 2.
- The computations are conducted with no delay between chirps.
- 3.
- Disruptions appear and disappear instantaneously, which is a simplification but very powerful for comparison of pure resilience response and recovery behavior. The duration of the disruptions is 5 s.
- 4.
- A uniform backward-looking moving average as in Equation (11) is plotted for instances in each case corresponding to a window of 1.5 s.
- 5.
- The first sender position initialization is random. Then, initialization is carried out using the estimation of the previous time step.
5.5. Resilience Quantification of Indoor Localization System
- 1.
- Barrier: in every case.
- 2.
- Noise: Sum of decreasing level.
- 3.
- Receiver malfunction: Sum of decreasing level.
6. Experimental Results
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ASSIST | acoustic self-calibrating system for indoor smartphone tracking |
CPS | cross power spectrum |
RFID | radio frequency identification |
RIR | room impulse response |
SNR | signal-to-noise ratio |
TDOA | time difference of arrival |
TOA | time of arrival |
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Loss Function | Barrier (Figure 5) | Noise (Figure 6) | Receiver Malfunction (Figure 7) |
---|---|---|---|
Linear | 4.2 m | 2.2 m | 0.12 m |
Arc-tangent | 1.9 m | 0.2 m | 0.12 m |
Loss Function | Barrier + Noise (Figure 8) | Noise + Receiver Malfunction (Figure 9) | Receiver Malfunction + Noise (Figure 10) |
---|---|---|---|
Linear | 6.4 m | 1.9 m | 2.1 m |
Arc-tangent | 1.8 m | 0.2 m | 0.2 m |
Loss Function | Resilience (Rsl) |
---|---|
Linear | 0.634 |
Arc-tangent | 0.936 |
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Jain, A.K.; Schott, D.J.; Scheithauer, H.; Häring, I.; Höflinger, F.; Fischer, G.; Habets, E.A.P.; Gelhausen, P.; Schindelhauer, C.; Rupitsch, S.J. Simulation-Based Resilience Quantification of an Indoor Ultrasound Localization System in the Presence of Disruptions. Sensors 2021, 21, 6332. https://doi.org/10.3390/s21196332
Jain AK, Schott DJ, Scheithauer H, Häring I, Höflinger F, Fischer G, Habets EAP, Gelhausen P, Schindelhauer C, Rupitsch SJ. Simulation-Based Resilience Quantification of an Indoor Ultrasound Localization System in the Presence of Disruptions. Sensors. 2021; 21(19):6332. https://doi.org/10.3390/s21196332
Chicago/Turabian StyleJain, Aishvarya Kumar, Dominik Jan Schott, Hermann Scheithauer, Ivo Häring, Fabian Höflinger, Georg Fischer, Emanuël A. P. Habets, Patrick Gelhausen, Christian Schindelhauer, and Stefan Johann Rupitsch. 2021. "Simulation-Based Resilience Quantification of an Indoor Ultrasound Localization System in the Presence of Disruptions" Sensors 21, no. 19: 6332. https://doi.org/10.3390/s21196332
APA StyleJain, A. K., Schott, D. J., Scheithauer, H., Häring, I., Höflinger, F., Fischer, G., Habets, E. A. P., Gelhausen, P., Schindelhauer, C., & Rupitsch, S. J. (2021). Simulation-Based Resilience Quantification of an Indoor Ultrasound Localization System in the Presence of Disruptions. Sensors, 21(19), 6332. https://doi.org/10.3390/s21196332