论文标题
基于计算光谱标准化的内存神经网络网络估计器，用于微无人机的GPS贬低的操作
Computationally Light Spectrally Normalized Memory Neuron Network based Estimator for GPS-Denied operation of Micro UAV
论文作者
论文摘要
本文解决了在GPS信息不可用的混乱环境中运行的无人机中的位置估计问题。提出了一种基于模型学习的方法，该方法采用了转子rpms并将其作为输入作为输入，并使用新型的光谱纳入记忆神经网络（SN-MNN）预测了无人机的一步位置。光谱归一化确保了稳定且可靠的预测性能。预测位置将转换为全局坐标框架，然后使用板载扩展的卡尔曼滤波器估算无人机的状态，然后将其他外围传感器（例如IMU，气压计，指南针等）等其他外围传感器的进程融合在一起。使用Micro-UAV从运动捕获设施收集的实验飞行数据用于训练SN-MNN。 PX4-ECL库用于使用所提出的算法来重播飞行数据，并将估计位置与实际地面真相数据进行比较。所提出的算法不需要任何额外的机载传感器，并且在计算上是光线的。将所提出的方法的性能与当前的最新GPS限制算法进行了比较，可以看出，所提出的算法对于位置估计值最少。
This paper addresses the problem of position estimation in UAVs operating in a cluttered environment where GPS information is unavailable. A model learning-based approach is proposed that takes in the rotor RPMs and past state as input and predicts the one-step-ahead position of the UAV using a novel spectral-normalized memory neural network (SN-MNN). The spectral normalization guarantees stable and reliable prediction performance. The predicted position is transformed to global coordinate frame which is then fused along with the odometry of other peripheral sensors like IMU, barometer, compass etc., using the onboard extended Kalman filter to estimate the states of the UAV. The experimental flight data collected from a motion capture facility using a micro-UAV is used to train the SN-MNN. The PX4-ECL library is used to replay the flight data using the proposed algorithm, and the estimated position is compared with actual ground truth data. The proposed algorithm doesn't require any additional onboard sensors, and is computationally light. The performance of the proposed approach is compared with the current state-of-art GPS-denied algorithms, and it can be seen that the proposed algorithm has the least RMSE for position estimates.
