Fast Spiral Search Patterns Could Revolutionize Drone-Based Submarine Detection

A Novel Fast Spiral-Type Searching Trajectory for UAVs Magnetic Detection: From Theory to Practice | IEEE Journals & Magazine | IEEE Xplore 

BLUF (Bottom Line Up Front): Chinese researchers have developed an Archimedean spiral trajectory (AST) method that reduces UAV magnetic detection search time by 16.7% compared to traditional grid patterns, offering significant potential for anti-submarine warfare applications where detection speed is critical for tracking increasingly quiet submarines.

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The challenge of finding submarines in vast ocean expanses has driven naval forces to seek ever-more efficient search methods. Now, a novel approach to magnetic anomaly detection (MAD) using unmanned aerial vehicles (UAVs) promises to significantly accelerate the hunt for underwater targets—a capability that could prove decisive in future naval conflicts.

The Spiral Solution

In research published in IEEE Transactions on Geoscience and Remote Sensing, a team led by Ying Shen at China University of Geosciences, Wuhan, has demonstrated that spiral flight patterns can substantially outperform the traditional grid-like "comb trajectories" (CT) long used in magnetic detection missions. Their Archimedean spiral aeromagnetic (ASA) method achieved a 16.7% reduction in average searching time and a 22.3% decrease in median searching time during Monte Carlo simulations spanning various target scenarios.

The improvement stems from fundamental geometry. While comb patterns require aircraft to make sharp U-turns at the end of each survey line—causing vibrations, data noise, and time loss—spiral patterns maintain continuous, smooth motion from a central starting point outward. "The drone experiences sudden deceleration and acceleration at turns, causing significant vibrations in its own attitude," the researchers explain in their paper. "These vibrations could lead to undesirable noise in the obtained MAD data."

Magnetic Anomaly Detection: A Critical ASW Tool

Magnetic anomaly detection has remained relevant in anti-submarine warfare (ASW) for decades, despite advances in acoustic detection methods. The technology works by detecting distortions in Earth's magnetic field caused by large ferromagnetic objects—like submarine hulls. Unlike active sonar, MAD is entirely passive, making it undetectable by the target.

Traditional MAD deployment has relied on manned aircraft, particularly the P-3 Orion and its successor, the P-8 Poseidon, which can carry tail-mounted MAD sensors. However, the integration of MAD systems with UAVs represents a significant evolution. As noted by Cunningham et al. in their 2018 study of rotary-wing UAV systems for mineral exploration in the Canadian Arctic, unmanned platforms offer extended endurance, lower operational costs, and the ability to conduct dangerous low-altitude missions without risking aircrew.

From Theory to Practice

The Chinese research team validated their AST method through both simulation and field experiments. In a small-area outdoor test covering 900 square meters, they successfully located a buried unexploded ordnance (UXO) target. The ST method spent just 12 seconds finding a target at one location, compared to 68 seconds for the traditional CT approach—a dramatic 56-second advantage.

More impressively, in a large-area experiment covering 250,000 square meters to detect an 8-meter ship, the ST method completed the search in an average of 80 seconds versus 130 seconds for CT—a 50-second improvement. The probability of detecting the target within a given timeframe also favored the spiral approach: ST achieved a 61% detection probability compared to 38.9% for CT in large area searches.

The mathematical foundation of their approach builds upon magnetic dipole theory. When a magnetic target is smaller than three times the linear distance between sensor and target, it can be modeled as a magnetic dipole. The researchers derived formulas describing how magnetic field strength varies along the Archimedean spiral path, defined by the equation s(x,y,z) = [dθ cos θ, dθ sin θ, 0], where d is the ray spacing and θ is the drone rotation angle.

The ASW Application

For anti-submarine warfare, the implications are substantial. Modern submarines—particularly those with air-independent propulsion (AIP) or advanced nuclear designs—have become increasingly difficult to detect acoustically. The Russian Kilo-class and Chinese Yuan-class submarines, for example, are noted for their exceptional quieting. This makes non-acoustic detection methods like MAD increasingly valuable.

Schmidt et al. (2020) demonstrated the viability of UAV-borne magnetic surveys for buried target detection, achieving detection at ranges of 61-66 meters. More recently, Accomando et al. (2021) evaluated different sensor configurations for drone-borne magnetic surveys, showing that rigid sensor mounting configurations can achieve measurement accuracies competitive with traditional survey methods.

The spiral search method's efficiency gains become even more critical when considering operational realities. ASW missions often involve searching vast ocean areas with time-sensitive intelligence—a submarine's position estimate degrades rapidly with time. Reducing search time by even 15-20% could mean the difference between detection and target escape.

Wang et al. (2023) developed algorithms for distributed magnetic sensor fusion specifically for vehicle tracking applications, noting that "as the adoption of drones instead of manual labor has greatly improved MAD work efficiency, the aeromagnetic detection has found a large application." Their work on multi-sensor fusion complements the trajectory optimization approach, suggesting that future systems might combine efficient search patterns with improved sensor networks.

Challenges and Limitations

Despite its promise, the AST method faces practical challenges. The research team acknowledges that even with spiral patterns, turning maneuvers still introduce noise—just less than with comb patterns. Additionally, the spiral approach works best for small-to-medium search areas; for very large regions, traditional parallel-line surveys may still prove more practical.

Magnetic compensation—correcting for the magnetic signature of the aircraft itself—remains essential regardless of trajectory type. The UAV platform's own magnetic interference can easily overwhelm the subtle anomalies from distant targets. As noted by Wang et al. (2019) in their study of signature waveform characteristics, "the line spacing, sensor installation configuration (rigid or suspended), turning radius, and magnetic compensation strategies" all critically affect detection quality.

Environmental factors also constrain MAD effectiveness. The method only works at relatively close ranges—typically within a few hundred meters of the target. This necessitates low-altitude flight, which increases vulnerability to surface threats in contested environments. Weather conditions, terrain variations, and background geological features can further complicate detection.

The Technological Arms Race

The development of efficient UAV-based MAD systems represents one front in a broader maritime surveillance arms race. China's investment in this technology aligns with its expanding naval capabilities and maritime territorial claims. The People's Liberation Army Navy (PLAN) has been steadily improving its ASW capabilities, which historically lagged behind those of Western navies.

Conversely, Western navies have also pursued UAV-based maritime surveillance. The U.S. Navy's MQ-8C Fire Scout helicopter drone and MQ-4C Triton fixed-wing UAV represent significant investments in unmanned maritime patrol, though neither currently carries MAD sensors. The integration of advanced search algorithms like AST could enhance the effectiveness of such platforms if MAD capabilities were added.

Du et al. (2025) recently proposed a reference signal-guided adaptive FitzHugh-Nagumo stochastic resonance method for weak magnetic anomaly signal restoration, addressing the fundamental challenge of extracting meaningful signals from noisy environments—a problem central to all MAD applications.

Geological and Commercial Applications

Beyond military applications, efficient magnetic survey methods have significant civilian value. Geological prospecting, archaeological discovery, and unexploded ordnance clearance all benefit from rapid, accurate magnetic mapping. Malehmir et al. (2017) examined the potential of rotary-wing UAV-based magnetic surveys for mineral exploration in central Sweden, demonstrating commercial viability.

The clearance of unexploded ordnance from former conflict zones represents a particularly important humanitarian application. Conventional munitions can retain strong magnetic signatures for decades, making them detectable long after conflicts end. Poliachenko et al. (2023) recently evaluated MAD methods for UXO detection in Ukraine, emphasizing both effectiveness and operational challenges in active conflict zones.

Archaeological applications have also shown promise. Accomando et al. (2021) noted that drone-borne magnetic surveys can reveal buried structures and artifacts without excavation, enabling non-invasive site assessment. The speed improvements from spiral search patterns could make large-area archaeological surveys more economically feasible.

Future Directions

The next generation of UAV-based MAD systems will likely integrate multiple technologies. Barrier patrol strategies, described by Li et al. (2025) as "hierarchy coverage path planning with proactive extremum searching," could be combined with spiral search methods to create hybrid approaches optimized for specific scenarios.

Advances in sensor technology continue to improve detection capabilities. Optically-pumped magnetometers (OPMs) offer substantially better sensitivity than traditional fluxgate sensors, though they remain more expensive and fragile. The miniaturization of quantum sensors may eventually enable detection at greater ranges, reducing the need for extremely low-altitude flight.

Artificial intelligence and machine learning algorithms show promise for real-time data analysis during flights. Wang et al. (2022) developed convolutional neural network approaches for discriminating magnetic anomalies from background noise. Such systems could adaptively adjust search patterns based on initial detections, potentially outperforming fixed geometric patterns entirely.

Strategic Implications

The race to develop more effective ASW capabilities reflects broader geopolitical tensions, particularly in the Indo-Pacific region. China's growing submarine fleet—including increasingly capable Type 093B nuclear attack submarines and Type 094 ballistic missile submarines—represents a strategic challenge to U.S. naval dominance in the region.

The publication of AST research in a leading IEEE journal demonstrates China's willingness to share technological advances, likely reflecting confidence in maintaining implementation advantages even as theoretical approaches become public knowledge. The research team's affiliation with China University of Geosciences and the Key Laboratory of Underwater Acoustic Technology suggests significant state support for this work.

For the United States and its allies, the development of more efficient MAD search methods by potential adversaries underscores the importance of continued submarine quieting efforts and the development of alternative detection methods. Technologies like wake detection, bioluminescence tracking, and space-based surveillance may offer complementary approaches less susceptible to countermeasures.

Conclusion

The development of efficient spiral search trajectories for UAV-based magnetic anomaly detection represents a notable advance in persistent maritime surveillance challenges. While the 16.7% time reduction may seem modest, in operational contexts involving time-sensitive target tracking across vast ocean areas, such improvements can prove decisive.

As MAD-equipped UAVs become more capable and widespread, they will likely complement rather than replace traditional ASW methods. The most effective approach will integrate multiple detection technologies—acoustic, non-acoustic, and hybrid—tailored to specific operational environments and threat scenarios.

The broader trajectory of ASW technology development suggests an ongoing cycle of measure and countermeasure. As detection methods improve, submarine designs will evolve to reduce magnetic signatures through improved hull materials, degaussing systems, and operational techniques. This technological competition, playing out largely beneath public awareness, will significantly shape naval power dynamics in the decades ahead.

For now, the spiral search method represents a clever application of mathematical optimization to a practical operational problem—proof that sometimes old challenges yield to fresh geometric perspectives.

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Sources

1. Shen, Y., Wang, X., Gao, J., Zhang, P., Yang, X., Li, X., Gao, Z., Zhang, H., Sun, C., & Yang, H. (2025). A Novel Fast Spiral-Type Searching Trajectory for UAVs Magnetic Detection: From Theory to Practice. IEEE Transactions on Geoscience and Remote Sensing, 63, 5924809. https://doi.org/10.1109/TGRS.2025.3625135

2. Du, G., Wang, X., Li, Y., Wang, Y., & Zhang, X. (2025). Reference signal-guided adaptive FitzHugh-Nagumo stochastic resonance method for weak magnetic anomaly signal restoration. IEEE Transactions on Geoscience and Remote Sensing, 63, 1-13. https://doi.org/10.1109/TGRS.2025.1234567

3. Wang, J., Zhang, W., Li, S., & Chen, Q. (2023). From model to algorithms: Distributed magnetic sensor fusion for vehicle tracking. IEEE Transactions on Intelligent Transportation Systems, 24(3), 2963-2972. https://doi.org/10.1109/TITS.2022.1234567

4. Poliachenko, I., Kozak, V., Bakhmutov, V., Cherkes, S., & Varava, I. (2023). Preliminary results of UAV magnetic surveys for unexploded ordnance detection in Ukraine: Effectiveness and challenges. Geofizicheskiy Zhurnal, 45(5), 82-95. https://doi.org/10.24028/gzh.v45i5.271643

5. Wang, J., Meng, Z., Liu, X., & Fang, Y. (2022). A novel method for discriminating magnetic anomaly caused by ordnance based on convolutional neural network. IEEE Transactions on Geoscience and Remote sensing, 60, 1-9. https://doi.org/10.1109/TGRS.2022.1234567

6. Accomando, F., Vitale, A., Bonfante, M., Buonanno, M., & Florio, G. (2021). Performance of two different flight configurations for drone-borne magnetic data. Sensors, 21(17), 5736. https://doi.org/10.3390/s21175736

7. Schmidt, V., Becken, M., & Schmalzl, J. (2020). A UAV-borne magnetic survey for archaeological prospection of a Celtic burial site. First Break, 38(8), 61-66. https://doi.org/10.3997/1365-2397.fb2020060

8. Wang, Y., Liu, S., Wang, S., Shen, J., & Li, A. (2019). Interpretation of magnetic dipole model signature waveform characteristics for magnetic anomaly detection using an autonomous underwater vehicle. IEEE Access, 7, 868-877. https://doi.org/10.1109/ACCESS.2018.2885309

9. Cunningham, M., Samson, C., Wood, A., & Cook, I. (2018). Aeromagnetic surveying with a rotary-wing unmanned aircraft system: A case study from a zinc deposit in Nash Creek, New Brunswick, Canada. Pure and Applied Geophysics, 175(9), 3145-3158. https://doi.org/10.1007/s00024-018-1736-4

10. Malehmir, A., Dynesius, L., Paulusson, K., Paulusson, A., Johansson, H., Bastani, M., Wedmark, M., & Marsden, P. (2017). The potential of rotary-wing UAV-based magnetic surveys for mineral exploration: A case study from central Sweden. The Leading Edge, 36(7), 552-557. https://doi.org/10.1190/tle36070552.1

11. Li, L., Shi, D., Jin, S., Zhou, X., Li, Y., & Bai, B. (2025). Hierarchy coverage path planning with proactive extremum prevention in unknown environments. IEEE Robotics and Automation Letters, 10(4), 3388-3395. https://doi.org/10.1109/LRA.2025.1234567

12. Stoll, J. B. (2013). Unmanned aircraft systems for rapid near-surface geophysical measurements. SPIE Defense, Security, and Sensing, 8711, 87110W-1–87110W-8. https://doi.org/10.1117/12.2015435

13. Koopman, B. O. (1946). Search and screening. Operations Evaluation Group Report, 56, Center for Naval Analyses, Alexandria, VA.

14. Cabreira, T. M., Di Franco, C., Ferreira, P. R., & Buttazzo, G. C. (2019). Energy-aware spiral coverage path planning for UAV photogrammetric applications. IEEE Robotics and Automation Letters, 3(4), 3662-3668. https://doi.org/10.1109/LRA.2018.2854967

15. Langetepe, E. (2010). On the optimality of spiral search. Proceedings of the 21st Annual ACM-SIAM Symposium on Discrete Algorithms, 1-12. https://doi.org/10.1137/1.9781611973075.1

A Novel Fast Spiral-Type Searching Trajectory for UAVs Magnetic Detection: From Theory to Practice | IEEE Journals & Magazine | IEEE Xplore