In view of the difficulty of designing a high-order linear phase filter with prototype parameters directly, this paper proposes a new approach to rapidly construct a high order linear phase filter through cascading two same low-order linear phase filters. A twelve-order HTSC cascaded linear phase filter is fabricated on YBCO/LaAlO3/YBCO substrate with the dimension of 44.1mm ×31.4mm. The measured result shows that the filter has a 56 MHz passband at a center frequency of 1993 MHz. The minimum insertion loss in the passband is 0.59 dB. The group delay variation in passband is less than 5 ns over 78.5% of the filter bandwidth which is in good agree with the simulation result.

Abstract-Rotor unmanned aerial vehicles (UAV) usually adopt MEMS gyros, accelerometers and magnetometers to determine its navigation attitude. Because a MEMS gyro has a drawback of angle drift, its attitude data is often corrected by the data solved with accelerometers and magnetometers. This paper presents a static and scalar calibration method for accurate solving of attitude angles with MEMS triaxial accelerometers and triaxial magnetometers. Based on the facts that vector sum of triaxial outputs of accelerometers equals to the gravity acceleration, and vector sum of triaxial outputs of magnetometers equals to the geomagnetic vector, the error equations are established. Taking the sum of error squares as the objective function, a nonlinear least square method is applied to solve the optimal calibration parameters. A Kalman filter is used to suppress the random error of output signals of sensors. A precise triaxial turn table is used to vary the spatial attitude of the sensor module for data sampling. Output signals of sensors at 32 different attitudes are captured, and unknown calibration parameters are solved. It is found that the variation of the difference values between the attitude angles calculated with the calibrated parameters and the attitude angles indicated by triaxial turn table is around ±1º. It is proved that the proposed calibration method for MEMS triaxial accelerometers and triaxial magnetometers is accurate and feasible.

Reliable attitude information is desired for navigation and control of rotor unmanned aerial vehicles (UAV). Rotor UAV's attitude can be determined by fusing redundant data from MEMS MARG (Magnetic, Angular Rate, and Gravity) sensors with fusion techniques. The extended Kalman filter (EKF)-based fusion algorithms are commonly adopted. However, there exists a contradiction between convergence speed and noise suppression in EKF-based algorithms. This paper presents a novel fusion algorithm combining EKF with complementary filter to estimate the attitude of rotor UAV. Firstly, gyros' measurements of angular rates are corrected by measurements of accelerometers with a Mahony passive complementary filter. The corrected angular rates as well as the measurements of accelerometers and magnetometers are then input to an EKF to implement data fusion. Results of validation experiments show that the proposed fusion method can generate attitude angles accurately and fuse multi-sensor data efficiently.