Scholarlink

A probabilistic-adaptive Kalman filter (pAKF) algorithm for the fault tolerant estimation of Unmanned Aerial Vehicles (UAV) dynamics in the presence of measurement faults is proposed. The proposed pAKF based on the evaluation of the posterior probability of the normal operation of the system, given for the current measurement. This probability is proposed to calculate via the posterior probability density of the normalized innovation sequence at the current estimation step. As a result, faults in the estimation system are corrected by the system, without affecting the good estimation behaviour. The developed pAKF algorithm is applied for the fault tolerant estimation of the UAV dynamics. The proposed pAKF algorithm is tested for the two different measurement malfunction scenarios; continuous bias at measurements and measurement noise increment.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Probabilistic-Adaptive Kalman Filtering for Fault-Tolerant Estimation of UAV States

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Chingiz Hajiyev

摘要

A probabilistic-adaptive Kalman filter (pAKF) algorithm for the fault tolerant estimation of Unmanned Aerial Vehicles (UAV) dynamics in the presence of measurement faults is proposed. The proposed pAKF based on the evaluation of the posterior probability of the normal operation of the system, given for the current measurement. This probability is proposed to calculate via the posterior probability density of the normalized innovation sequence at the current estimation step. As a result, faults in the estimation system are corrected by the system, without affecting the good estimation behaviour. The developed pAKF algorithm is applied for the fault tolerant estimation of the UAV dynamics. The proposed pAKF algorithm is tested for the two different measurement malfunction scenarios; continuous bias at measurements and measurement noise increment.