現在多旋翼直昇機、空拍機越來越普及，應用越來越複雜、多元，也越趨於自動化飛行。多旋翼執行自動飛行任務時一定會用到 GPS（Global Positioning System）的地理位置資訊，然而失去 GPS 訊號就無法繼續執行任務。
本研究希望在失去 GPS 訊號時能透過 Flight Information Calculation ( FIC )演算法計算出讓多旋翼繼續執行任務的飛行速度、航向角度。另外，利用Dead Reckoning (DR)演算法計算出多旋翼飛行時移動的位置來判定因側風而產生的誤差，並針對這個部分提出自我修正的方法。FIC演算法所計算出的飛行資訊與多旋翼上掛載的動作感測器所記錄的實際飛行資訊做比較即可得出誤差，並將誤差加入下個時間點的FIC演算法中進行校正。
本研究讓多旋翼執行任務時即使暫時失去 GPS 訊號也能夠繼續執行任務，並且有著可信任的準確度。當多旋翼執行任務不在只是倚靠 GPS 訊號，可以執行的任務種類也會越來越多。在未來，可以加入其他感測器(例如距離感測器)可將多旋翼任務範圍擴展到無法收到 GPS 訊號的室內，讓多旋翼的應用更加豐富。

In recent years, drones become more popular with complex autonomous controlled missions, e.g., delivery services, area monitoring, aerial photographic, disaster response. Most of drone’s mission is a trajectory with set of location points that either pre-planned or on-demand determined. A drone will based on it current location from its Global Positioning System (GPS) and a given trajectory to complete a mission. Therefore, location information (i.e., GPS) is critical for successful executing a drone mission. However, weather or buildings can often results of lost of GPS signals. In the event of GPS signal lost, autonomous drone are pause, cancel, or require manual input for the mission.
In this work, we proposed a Flight Information Calculation (FIC) algorithm to recover in an event of lost GPS signals. FIC uses motion sensors (i.e., accelerometer, compass, and gyroscope) to determine its flying speed, heading angle, and flying time in order to estimate current location of drone. To avoid the effects of the crosswind, FIC contains Self-Correction method to further improve accuracy of location estimation. The records of the fight information and the flight settings according FIC are compared to determine the crosswind effect to drone. FIC will calibrate the flight settings by take crosswind effect into consideration when estimation the flight action to the next location point in the mission.
The experiments showed the FIC is able to provide accurate location estimation which allows drone to continue executing its mission without GPS signal. Without the need of constant GPS signal, a combination and complex mission (e.g., indoor and out door) can be assigned to autonomous drone. In the future, additional sensors (e.g., rangefinder, camera, and sonar) can be used to improve the accuracy of FIC.

[1] http://www.amazon.com/b?node=8037720011

[2] THOMPSON, Elizabeth A., et al. Software-defined GPS receiver on USRP-platform. Journal of Network and Computer Applications, 2012, 35.4: 1352-1360.

[3] CHEN, Jian, et al. Practicing a record-and-replay system on USRP. In:Proceedings of the second workshop on Software radio implementation forum. ACM, 2013. p. 61-64.

[4] SAYED, Ali H.; TARIGHAT, Alireza; KHAJEHNOURI, Nima. Network-based wireless location: challenges faced in developing techniques for accurate wireless location information. IEEE signal processing magazine, 2005, 22.4: 24-40.

[5] ZHOU, Qing-Li, et al. Dead reckoning and Kalman filter design for trajectory tracking of a quadrotor UAV. In: Mechatronics and Embedded Systems and Applications (MESA), 2010 IEEE/ASME International Conference on. IEEE, 2010. p. 119-124.

[6] JIMENEZ, Antonio R., et al. A comparison of pedestrian dead-reckoning algorithms using a low-cost MEMS IMU. In: Intelligent Signal Processing, 2009. WISP 2009. IEEE International Symposium on. IEEE, 2009. p. 37-42.

[7] FERREIRA, Sérgio, et al. Assessing the capacity of man-portable UAVs for network access point localization, using RSSI link data. In: Unmanned Aircraft Systems (ICUAS), 2014 International Conference on. IEEE, 2014. p. 355-364.

[8] VINCENTY, Thaddeus. Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations. Survey review, 1975, 23.176: 88-93.