地磁背景下基于傳感器陣列的磁偶極子目標跟蹤方法
doi: 10.11999/JEIT190236
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中國科學(xué)院電磁輻射與探測技術(shù)重點實驗室 北京 100190
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中國科學(xué)院大學(xué) 北京 100049
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3.
北京自動化控制設(shè)備研究所 北京 100074
Magnetic Dipole Object Tracking Algorithm Based on Magnetometer Array in Geomagnetic Background
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1.
Key Laboratory of Electromagnetic Radiation and Sensing Technology, Chinese Academy of Sciences, Beijing 100190, China
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2.
University of Chinese Academy of Sciences, Beijing 100049, China
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3.
Beijing Automation Control Equipment Institute, Beijing 100074, China
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摘要:
針對地磁背景下磁偶極子目標跟蹤過程中存在的地磁干擾與模型非線性的問題,該文提出一種基于差量磁異常的蒙特卡洛卡爾曼濾波(MCKF)跟蹤方法。新的跟蹤方法以傳感器陣列測量磁場的差量作為觀測信號,并利用蒙特卡洛卡爾曼濾波算法解決模型的非線性問題,實現(xiàn)磁偶極子目標的實時跟蹤。通過仿真跟蹤實驗,結(jié)果表明該文算法較傳統(tǒng)的擴展或無跡卡爾曼濾波算法在穩(wěn)定跟蹤過程中對目標特征參數(shù)的估計更精確;通過地磁背景跟蹤實驗,結(jié)果驗證了該文算法較傳統(tǒng)算法在低信噪比下的性能優(yōu)勢。
Abstract:In order to solve the problem of geomagnetic interference and model nonlinearity in the tracking process of magnetic dipole under geomagnetic background, Monte Carlo Kalman Filter (MCKF) tracking method based on differential magnetic anomaly is proposed in this paper. The new tracking method takes the difference of magnetic field measured by sensor array as the observation signal, and uses Monte Carlo Kalman Filtering (MCKF) algorithm to solve the nonlinear problem of the model to realize the real-time tracking of magnetic dipole targets. The simulation results show that the proposed method is more accurate than the traditional Extended Kalman Filter (EKF) or Untracked Kalman Filter (UKF) in the stable tracking process. The results of real geomagnetic background tracking experiments show that the proposed algorithm has better tracking performance under low SNR.
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表 1 不同跟蹤算法各方向投影軌跡跟蹤誤差(m)
跟蹤算法 時間區(qū)間 1~40點 41~80點 81~120點 120~160點 160~200點 x方向 EKF 0.0651 0.0127 0.0023 0.0057 0.0409 UKF 0.0689 0.0127 0.0024 0.0057 0.0410 MCKF 0.2143 0.0153 0.0024 0.0039 0.0218 y方向 EKF 0.0491 0.0113 0.0059 0.0100 0.0319 UKF 0.0512 0.0112 0.0061 0.0096 0.0318 MCKF 0.1237 0.0115 0.0042 0.0061 0.0158 z方向 EKF 0.0430 0.0089 0.0021 0.0042 0.0163 UKF 0.0436 0.0088 0.0022 0.0041 0.0164 MCKF 0.0456 0.0086 0.0022 0.0043 0.0198 下載: 導(dǎo)出CSV
表 2 不同蒙特卡洛樣本點數(shù)的算法各方向投影軌跡跟蹤誤差(m)
表2(a) x方向 蒙特卡洛樣本點數(shù) 時間區(qū)間 1~40 41~80 81~120 120~160 160~200 50 0.2817 0.0242 0.0035 0.0052 0.0264 100 0.2481 0.0194 0.0030 0.0046 0.0231 200 0.2143 0.0153 0.0024 0.0039 0.0218 400 0.2316 0.0159 0.0023 0.0039 0.0212 表2(b) y方向 蒙特卡洛樣本點數(shù) 時間區(qū)間 1~40 41~80 81~120 120~160 160~200 50 0.1658 0.0168 0.0049 0.0069 0.0192 100 0.1442 0.0141 0.0047 0.0063 0.0166 200 0.1237 0.0115 0.0042 0.0061 0.0158 400 0.1346 0.0118 0.0043 0.0059 0.0151 表2(c) z方向 蒙特卡洛樣本點數(shù) 時間區(qū)間 1~40 41~80 81~120 120~160 160~200 50 0.0569 0.0124 0.0031 0.0053 0.0242 100 0.0496 0.0105 0.0026 0.0046 0.0202 200 0.0456 0.0086 0.0022 0.0043 0.0198 400 0.0466 0.0090 0.0021 0.0042 0.0195 下載: 導(dǎo)出CSV
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