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doi: 10.3934/mfc.2022014
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## Adaptive attitude determination of bionic polarization integrated navigation system based on reinforcement learning strategy

 1 School of Information Science and Technology, North China University of Technology, Beijing 100144, China 2 Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, China, 210096

*Corresponding author: Tao Du

Received  March 2022 Revised  April 2022 Early access May 2022

The bionic polarization integrated navigation system includes three-axis gyroscopes, three-axis accelerometers, three-axis magnetometers, and polarization sensors, which provide pitch, roll, and yaw. When the magnetometers are interfered or the polarization sensors are obscured, the accuracy of attitude will be decreased due to abnormal measurement. To improve the accuracy of attitude of the integrated navigation system under these complex environments, an adaptive complementary filter based on DQN (Deep Q-learning Network) is proposed. The complementary filter is first designed to fuse the measurements from the gyroscopes, accelerometers, magnetometers, and polarization sensors. Then, a reward function of the bionic polarization integrated navigation system is defined as the function of the absolute value of the attitude angle error. The action-value function is introduced by a fully-connected network obtained by historical sensor data training. The strategy can be calculated by the deep Q-learning network and the action that optimal action-value function is obtained. Based on the optimized action, three types of integration are switched automatically to adapt to the different environments. Three cases of simulations are conducted to validate the effectiveness of the proposed algorithm. The results show that the adaptive attitude determination of bionic polarization integrated navigation system based on DQN can improve the accuracy of the attitude estimation.

Citation: Huiyi Bao, Tao Du, Luyue Sun. Adaptive attitude determination of bionic polarization integrated navigation system based on reinforcement learning strategy. Mathematical Foundations of Computing, doi: 10.3934/mfc.2022014
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##### References:
Illustration of DQN
Variation of geomagnetic field intensity under geomagnetic interference
Comparison decision action under geomagnetic interference
Attitude estimation errors under geomagnetic interference
Variation of polarization angle under polarization interference
Comparison decision action under polarization interference
Attitude estimation errors under polarization interference
(a) Polarization angle under polarization interference. (b) Geomagnetic field intensity under geomagnetic interference
Comparison of decision-making actions when the magnetometer is disturbed and polarization is blocked
Attitude estimation errors under the magnetometer is disturbed and polarization is blocked
Standard deviation of attitude angle for decision comparison in experiment 1
 Method Pitch(°) Roll(°) Yaw(°) Complementary filter without DQN 0.4958 0.5057 0.5866 Complementary filter with DQN 0.4790 0.5022 0.3540
 Method Pitch(°) Roll(°) Yaw(°) Complementary filter without DQN 0.4958 0.5057 0.5866 Complementary filter with DQN 0.4790 0.5022 0.3540
Standard deviation of attitude angle for decision comparison in experiment 2
 Method Pitch(°) Roll(°) Yaw(°) Complementary filter without DQN 0.5450 0.5078 0.4700 Complementary filter with DQN 0.4640 0.5031 0.4241
 Method Pitch(°) Roll(°) Yaw(°) Complementary filter without DQN 0.5450 0.5078 0.4700 Complementary filter with DQN 0.4640 0.5031 0.4241
Standard deviation of attitude angle for decision comparison in experiment 3
 Method Pitch(°) Roll(°) Yaw(°) Complementary filter without DQN 0.4966 0.5052 0.6005 Complementary filter with DQN 0.4735 0.5031 0.3822
 Method Pitch(°) Roll(°) Yaw(°) Complementary filter without DQN 0.4966 0.5052 0.6005 Complementary filter with DQN 0.4735 0.5031 0.3822
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