IFOGのランダムなエラーの抑制方法は,色彩のノイズスペクトル情報の分離に基づいています
Zhe Liang1, Zhili Zhang1, Zhaofa Zhou1
1Intelligent Control Laboratory, PLA Rocket Force University of Engineering, Xi'an 710025, China.
Micromachines
|August 28, 2025
まとめ
この研究は,光ファイバー・ジャイロスコップのランダムなエラーを減らすための新しい適応カルマンフィルターを導入します. この方法により,慣性ナビゲーションシステムの初期調整精度は48%向上します.
科学分野:
- エンジニアリング
- 計測装置
- シグナル処理
背景:
- 高精度の慣性ナビゲーションシステムは,正確な光ファイバー・ジャイロスコップ (FOG) データに依存しています.
- カルマンフィルタリングの自動回帰移動平均 (ARMA) モデルを使用したFOGエラー抑制の伝統的な方法は,色ノイズで課題に直面し,不正確な状態方程式と限られたスケーラビリティにつながります.
研究 の 目的:
- 静的条件下における光ファイバー・ジャイロスコップの高精度モデリングとランダムなエラーの抑制のための新しい方法を開発する.
- 伝統的なARMAベースのカルマンフィルタリングのFOG信号のカラーノイズの限界を克服するために.
主な方法:
- ランダムなエラーモデル形式を明確にするためにFOG信号ノイズの特性を徹底的に分析します.
- 正確なランダムエラーモデリングのための新しいモデル・オーダー決定基準の提案.
- 角度ランダムウォークエラーを抑制し,カラーノイズを処理するために,ノイズスペクトル情報解離を使用する適応カルマンフィルターの設計.
主要な成果:
- 提案された方法は,光ファイバー・ジャイロスクープの角度ランダムウォークを含むランダムエラーを効果的にモデル化し,抑制します.
- 適応カルマンフィルターは,伝統的なアプローチと比較して,色のノイズを処理する上で優れた性能を示しています.
- 5分間のFOGデータを使用した実験的検証で,初期調整の精度が平均48%改善されたことが示されています.
結論:
- 開発された適応カルマンフィルターは,特に静的な条件下でFOGのランダムなエラーをモデル化および抑制するための堅固なソリューションを提供します.
- このアプローチは従来の方法の原則的な限界を克服し,慣性ナビゲーションシステムの精度を高めます.
- この発見は,要求の高いアプリケーションにおけるFOGのパフォーマンスを改善するための貴重なスキームを提供します.
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