Underwater Magnetic Sensors Network
Abstract
1. Introduction
2. Review of Related Works
3. Problem Statement and Research Hypothesis
4. System Architecture and Control Algorithm
5. Tests and Measurements
5.1. Objectives of the Measurement Phase
- Validating the accuracy of the magnetic sensors after calibration.
- Evaluating tilt compensation algorithms to ensure reliable heading measurements.
- Analyzing the performance of the network in detecting magnetic anomalies under controlled underwater conditions.
5.2. Test Setup and Methodology
- A test tank equipped with magnetic field generators to produce controlled disturbances.
- Sensor nodes spaced at fixed intervals to assess spatial accuracy.
- Data logging tools for capturing sensor outputs at a sampling rate of 10 Hz.
- Calibration routines applied prior to the experiments to ensure accurate measurements.
5.3. Calibration Results
5.4. Tilt Compensation Results
5.5. Magnetic Anomaly Detection
5.6. Discussion and Observations
- Calibration significantly reduced offsets and noise, ensuring accurate magnetic field measurements.
- Tilt compensation effectively minimized heading errors, even at tilt angles up to 30°.
- The sensor network demonstrated high accuracy in detecting magnetic anomalies, with minimal error and reliable detection ranges.
- The system handled environmental factors such as noise and temperature variations well, ensuring robust performance.
- Ambient magnetic fields, though present, were effectively accounted for during calibration, ensuring accurate baseline measurements.
5.7. Real-Life Enviromental Test
5.8. Detailed Test Scenarios
- River Sections (S1, S2, S3): Focused on detecting activities on the water, such as small boats and swimmers. Included variations in speed, distance, and environmental conditions.
- Shoreline Sections (S6, S7, S8): Monitored movement along the riverbank, simulating personnel crossing at various points.
- Land Intrusions (S9, S10): Tested the detection of coordinated activities involving land-based movements in conjunction with water-based intrusions.
5.9. Results from Selected Scenarios
5.9.1. Scenario S3.1 (Daylight)
- Location: River Section 2, Shoreline Section 7, Land Section 10.
- Key Findings:
- –
- Detection on river: 10/10 attempts (100%).
- –
- Detection on shoreline: 10/10 attempts (100%).
- –
- Detection on land: 8/10 attempts (80%).
- Average Detection Rate: 93%.
- Overall Detection Effectiveness: 100%.
5.9.2. Scenario S4.2A (Nighttime)
- Location: River Sections 3–5, Shoreline Section 8, Land Section 13.
- Key Findings:
- –
- Detection on river: 24/24 attempts (100% across three sections).
- –
- Detection on shoreline: 6/6 attempts (100%).
- –
- Detection on land: 4/6 attempts (67%).
- Average Detection Rate: 93%.
- Overall Detection Effectiveness: 100%.
5.9.3. Scenario S8.2 (Nighttime)
- Location: Shoreline Section 7, Land Sections 10 and 11.
- Key Findings:
- –
- Detection on shoreline: 2/8 attempts (25%).
- –
- Detection on land: 15/16 attempts (94% across two sections).
- Average Detection Rate: 71%.
- Overall Detection Effectiveness: 100%.
5.10. Insights from Data
- Environmental Impact: Nighttime scenarios (e.g., S4.2A, S8.2) exhibited reduced detection rates on shoreline sections, likely due to low visibility and increased environmental noise Table 4.
- System Optimization: Implementation of coincidence-based rules significantly reduced false alarms and improved operator response time. Integration of geophonic sensors enhanced detection capabilities on shoreline sections.
- Scalability: The modular architecture of the sensor network facilitated seamless adaptation across various sections (P1–P4), allowing effective perimeter monitoring over extended areas.
5.11. Environmental Testing
Environmental Condition | Detection Rate (%) | Response Time (s) |
---|---|---|
Clear | 95 | 2.5 |
Rain | 85 | 3.0 |
Fog | 80 | 3.5 |
6. Discussion
7. Conclusions
8. Patents
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Axis | Pre-Cal. Offset | Post-Cal. Offset | Pre-Cal. Noise | Post-Cal. Noise |
---|---|---|---|---|
X | −55 | 0 | ±3 | ±0.5 |
Y | −60 | 0 | ±4 | ±0.7 |
Z | −50 | 0 | ±2.5 | ±0.4 |
Tilt Angle (°) | Raw Heading Error (°) | Compensated Error (°) |
---|---|---|
0 | 2.5 | 0.3 |
10 | 7.2 | 1.0 |
20 | 15.4 | 2.5 |
30 | 24.1 | 4.8 |
Applied Field (T) | Measured Field (T) | Error (T) | Detection Range (m) |
---|---|---|---|
50 | 49.5 | 0.5 | 2.0 |
100 | 99.1 | 0.9 | 3.5 |
150 | 148.2 | 1.8 | 5.0 |
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Adamczyk, A.; Klebba, M.; Wąż, M.; Pavić, I. Underwater Magnetic Sensors Network. Sensors 2025, 25, 2493. https://doi.org/10.3390/s25082493
Adamczyk A, Klebba M, Wąż M, Pavić I. Underwater Magnetic Sensors Network. Sensors. 2025; 25(8):2493. https://doi.org/10.3390/s25082493
Chicago/Turabian StyleAdamczyk, Arkadiusz, Maciej Klebba, Mariusz Wąż, and Ivan Pavić. 2025. "Underwater Magnetic Sensors Network" Sensors 25, no. 8: 2493. https://doi.org/10.3390/s25082493
APA StyleAdamczyk, A., Klebba, M., Wąż, M., & Pavić, I. (2025). Underwater Magnetic Sensors Network. Sensors, 25(8), 2493. https://doi.org/10.3390/s25082493