Rancang Bangun Antena Rotasi Dengan Kalibrasi Berbasis Program Kalman Filter
Abstract
The Internet of Things (IoT) has changed how we interact with our environment. Low-Power Wide-Area Networks (LPWAN) such as LoRa play an important role in the IoT ecosystem due to their low power consumption, long-distance communication, and cost-effectiveness. However, detecting the azimuth and elevation angles of the transmitter is a challenge in LoRa communication. This paper proposes a rotary antenna system with an Inertial Measurement Unit (IMU) to precisely track azimuth and elevation angles at LoRa. The research carried out is to test the tool by comparing the algorithm without the Kalman Filter and the one with the Kalman Filter, where the test's success with the lowest error is up to 0.75% and 0%, respectively.
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References
N. Atanasov, J. Le Ny, and V. Kumar, "Inertial, vision, and encoder-based localization for visually impaired people," J. Field Robot., vol. 30, no. 3, pp. 399-419, May-Jun. 2013.
M. Caruso, A. Maravall, M. Jurado, and F. R. Rubio, "Attitude estimation using Unscented Kalman Filter for low-cost MEMS IMU," Sensors, vol. 12, no. 12, pp. 16874-16897, Dec. 2012.
A. Thien, R. M. K. H. Wong, A. O. Tay, and D. Zhu, "Inertial navigation systems for unmanned aerial vehicles," Measurement, vol. 45, no. 2, pp. 265-273, Feb. 2012.
H. Yu and H. Ahn, "Roll and pitch angle estimation for mobile robot using an improved complementary filter algorithm," J. Intell. Robot. Syst., vol. 67, no. 3-4, pp. 325-338, Nov. 2012.
K. Zhou and K. L. Chan, "Optimal design of a triaxial MEMS accelerometer for roll/pitch measurement," IEEE Trans. Instrum. Meas., vol. 57, no. 11, pp. 2485-2494, Nov. 2008.
A. Kos, M. Mihelj, and B. Murovec, "Position and orientation estimation using inertial sensors," Sensors, vol. 11, no. 9, pp. 9154-9178, Sep. 2011.
S. J. Julier and J. K. Uhlmann, "A new extension of the Kalman filter to nonlinear systems," in Proc. AeroSense: Int. Soc. Opt. Eng., Apr. 1997, pp. 182-193.
R. L. Jansson and T. Gustafsson, "Inclinometer with a Kalman filter for slope estimation of arbitrary objects," IEEE Trans. Instrum. Meas., vol. 55, no. 3, pp. 886-893, Jun. 2006.
T. Ristic, B.-T. Vo, and B.-N. Vo, "An adaptive multi-target tracking algorithm using random matrices," IEEE Trans. Aerosp. Electron. Syst., vol. 46, no. 1, pp. 32-53, Jan. 2010.
J. Crassidis and F. L. Markley, "Unscented filtering for spacecraft attitude estimation," J. Guid. Control Dyn., vol. 30, no. 2, pp. 496-503, Mar.-Apr. 2007.
S. Trimpe and R. D'Andrea, "Inertial sensing in fast and accurate state estimation for legged robots," J. Dyn. Syst. Meas. Control, vol. 133, no. 2, p. 021002, Mar. 2011.
F. Maarif, A. Fauzi, and S. Aris, "Implementation of Kalman filter to reduce noise in accelerometer sensor for measuring the angle of balancing robot," in Proc. Int. Conf. Intell. Technol. Its Appl., Dec. 2017, pp. 238-243.
J. Lee and C. C. Chung, "A hybrid IMU and magnetometer sensor fusion algorithm using Kalman filter," in Proc. IEEE Int. Conf. Adv. Robot. (ICAR), Jun. 2011, pp. 330-335.
Balanis, C. A. (2016). Antenna Theory: Analysis and Design. John Wiley & Sons.
Chen, C., Lin, C., & Su, J. (2017). High-accuracy antenna orientation tracking system using low-cost IMU sensors. IEEE Transactions on Industrial Electronics, 64(6), 4816-4825.
Collin, R. E. (2013). Antennas and Radiowave Propagation. McGraw Hill.
Croce, D., & Rinner, B. (2019). Low Power Wide Area Networks for Internet of Things: A Review. IEEE Internet of Things Journal, 6(1), 1-1.
Ge, Z., Chua, K. C., & Chin, F. Y. (2018). Low-cost, high-precision antenna orientation tracking using inertial sensors. IEEE Transactions on Industrial Electronics, 66(5), 3835-3845.
Moynihan, R., Breen, M., Walsh, D., & O'Hare, G. M. (2018). LoRa WAN and LoRaWAN: A review of emerging low-power wireless communication systems for industrial IoT. IEEE Access, 6, 883-892.