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Inertial-Navigation-Aided Single-Satellite Highly Dynamic Positioning Algorithm.

Chengkai TangChengkai TangYi ZhangHoubing Song
Published in: Sensors (Basel, Switzerland) (2019)
Nowadays, research on global navigation satellite systems (GNSS) has reached a certain level of maturity to provide high-precision positioning services in many applications. Nonetheless, there are challenging GNSS-denial environments where a temporarily deployed single-satellite positioning system is a promising choice. To further meet the emergency call of highly dynamic targets in such situations, an augmented single-satellite positioning algorithm is proposed in this paper. First, the initial location of the highly dynamic target is found by real-time displacement feedback from the inertial navigation system (INS). Then, considering the continuity of position change, and taking advantage of the high accuracy and robustness of the unscented Kalman filter (UKF), target location is through iteration and fusion. Comparing this proposed method with the least-squares Newton-iterative Doppler single-satellite positioning system and the pseudorange rate-assisted method under synthetic error conditions, the positioning error of our algorithm was 10 % less than the other two algorithms. This verified the validation of our algorithm in the single-satellite system with highly dynamic targets.
Keyphrases
  • machine learning
  • deep learning
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  • emergency department
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  • neural network