Robust marine target state estimation via cooperative observation with air-mobile platforms: A new algorithmic approach
Year: 2025
Type: article
Abstract: Existing algorithms for systematic error reduction often assume constant or slowly varying errors and rely on fixed, homogeneous platforms, limiting their ability to handle sudden changes in systematic and attitude errors in motorized platforms. To address this, we propose a robust algorithm based on first-order Taylor expansion within a cooperative air-mobile detection system. By utilizing multi-platform observations, we derive a joint systematic error expression, enabling accurate target state estimation without directly estimating the error. A pseudo-measurement equation is also constructed to further improve estimation accuracy. Simulations under various error conditions—including consistent, inconsistent, and mutation errors—show that our algorithm outperforms existing methods and demonstrates greater robustness. Practical experiments confirm its effectiveness, achieving a 2–3x improvement in estimation accuracy. This method enables precise marine target state estimation even amid significant systematic and attitude errors.
Source: Ain Shams Engineering Journal
Authors: Qiuyang Dai, Faxing Lu, Junfei Xu, Yuxiang Zhou, Haoran Shi, Guoliang Cao
Institution: Naval University of Engineering
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,200
Funder: Naval University of Engineering
OpenAlex ID: W7077051777