Low-Cost Onboard Multisensor State Estimator Based on Global Mixed Sampling Optimization Filter

Long-term convergent and accurate state estimation (attitude, velocity, and position) is critical to unmanned aerial vehicles (UAVs). However, the measurement accuracy and noise of multiple low-cost sensors onboard are poorer compared with high-precision sensors. To improve the solution accuracy and reliability of UAVs' state estimation based on the low-cost sensors, a two-step multisensor fusion estimator [federated mixed sampling Kalman filter (FMSKF)] is proposed. First, a multisensor integrated navigation model array is designed depending on the different sensors, including the gyroscope, accelerometer, magnetometer, global positioning system (GPS) module, and barometer. Then, a three-stage mixed sampling Kalman filter is proposed to obtain the local convergent state vector, which is difficult to meet the flight accuracy requirements. Therefore, a multisensor global information optimization filter is proposed to obtain the global convergent navigation solution parameters. Finally, the simulation and flight experimental results and detailed analysis demonstrate that the proposed algorithm can improve the state estimation accuracy, filtering robustness, and obtain desirable navigation performance.

Automated summary

Long-term convergent and accurate state estimation is crucial for unmanned aerial vehicles. However, low-cost sensors often have poorer measurement accuracy and noise compared to high-precision sensors. To improve the state estimation accuracy and reliability of UAVs using low-cost sensors, a two-step multisensor fusion estimator called federated mixed sampling Kalman filter (FMSKF) is proposed. The proposed algorithm utilizes a multisensor integrated navigation model array and a mixed sampling Kalman filter to optimize the state estimation and achieve desirable navigation performance.