OPTIMIZED INERTIAL NAVIGATION SYSTEM AND DNN FOR AIRCRAFTSHELICOPTERUAV

OPTIMIZED INERTIAL NAVIGATION SYSTEM AND DNN FOR AIRCRAFTS/HELICOPTER/UAV

PRESENTER INFORMATION Name Year & Dept. College Address Email ID Contact Linked. In profile - S. VARUN KUMAR - Third year, B. E Instrumentation and Controls - St. Joseph’s college of engineering, Chennai 600119 - 25/28 Meeran Sahib Street, Anna Salai, Chennai-600002. - svkvarun 43@gmail. com - +91 9840823036 - https: //www. linkedin. com/in/varun-kumar-a 00 b 02 b 9/ Ever since I could remember, I have had only one goal, which is to be a researcher. It was evident that the fascination and curiosity to know how technology could influence and drive our lives in many ways sparked my interest in the building new things. As a Junior majoring in Instrumentation and Control my ultimate career goal is to carry out research in the field of instrumentation and intelligent automation system. Dream, Dreams transform into thoughts And thoughts result in action - A. P. J. Abdul Kalam

PROBLEM STATEMENT Design a system which would ensure high navigation solution integrity even in an environment with poor or no GPS/Satellite navigation signal. The system can either be an independent sensor or may make use of existing self-contained systems DENSE FORESTRY MOUNTAINOUS REGIONS ROUGH DESERTS

IMPORTANCE OF THE PROPOSED PROJECT IN THE CONTEXT OF CURRENT STATUS v Between 2013 and mid-2016, there were nearly 80 incidents of aircraft GPS signal interference or malfunctions according to those filed on NASA’s Aviation Safety Reporting System (ASRS). v Pilots flying aircraft ranging from Cessna 172 s to Airbus A 300 s have been reporting these incidents — and the incidents are not limited to the U. S. Other more high-profile incidents around the globe also highlight the problem v The vast majority of incidents involve GPS-based navigation systems either experiencing a total loss of signal or more alarmingly misreporting the aircraft’s position. In around 50 cases, no immediate explanation is given for the malfunction. SOURCE: Guy Buesnel, PNT security technologist at Spirient Communications, a communications networks provider based in the U. K.

PROPOSED SOLUTION An inertial navigation system (INS) or inertial measurement unit (IMU) is a navigation aid that uses a computer, motion sensors and rotation sensors to continuously calculate via dead reckoning the position, orientation, and velocity of a moving object without the need for external references. v The proposed system measures both linear accelerations given by its accelerometer and angular velocity changes from its gyroscope and earth’s magnetism by magnetometer (9 DOF). v The initial position is provided by some outside system (in the case of this project, using a Global Positioning System (GPS) receiver when it is available). v Using a Raspberry Pi microcomputer as the base system and an inertial navigation system can be developed and operated. v Kalman Filtering with GPS and IMU data will be used to complete a “strap down solution” - a closed-loop system which can self-correct for error. v Moreover incorporating DNN into the flight controller can further enhance the path trajectory and navigation system. v Through this design the aircraft will be able to navigate through treacherous forestry and places where GPS tends to fail or is unavailable.

BLOCK DIAGRAM

SYSTEM DESIGN - HARDWARE LSM 9 DS 0 Inertial Measurement Unit Sky. Traq Venus 634 FLPx GPS BMP 280 Barometer Raspberry Pi 3 B Entire proposed IMS hardware cost is within >INR 6000

STANDLONE HARDWARE SOC Robotics

SETUP METHOD STRAPDOWN INERTIAL NAVIGATION SYSTEM A strap down INS is mainly comprised of three accelerometers and gyroscopes attached to the aircraft. Each accelerometer measures the motion of the aircraft in three directions of travel, while three gyroscopes are used to obtain information about the direction the aircraft is facing. With the information from these sensors, the heading, speed and position of the aircraft can be computed.

KALMAN FILTERING FOR OPTIMIZED NAVIGATION The Kalman filter is a set of mathematical equations that is based on dynamic model of system. These equations are used to make an estimate of the current state of a system and correct the estimation using any available sensor measurements.

OPTIMIZED NAVIGATION DATA Blue line: IMU system, Blue dots: GPS Signals, Red line: Kalman estimated trajectory. v Kalman Filter is used to integrate two systems when the state of the system follows continuous state dynamics and the measurement of the second system is related to the estimates provided by the first system. v The second system is used to correct the state estimates provided by the first system to yield an increased accuracy in state estimation

DEEP NEURAL NETWORK BASED NAVIGATION SYSTEM A trail perception technique based on Deep Neural Networks can be formulated where a dataset of the features is provided for download which can be efficiently acquired and used to train and test the Deep Neural Network model of the aircraft

INSIGHTS OF DEEP NEURAL NETWORKS Deep Neural networks are a set of algorithms modelled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labelling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated Major features of DNN are, v Classification v Clustering v Predictive analysis

TIME SCHEDULE OF PROJECT ACTIVITIES -TENTATIVE PROJECT SPLIT UP IDEA ENHANCEMENT CALIBRATION AND TESTING DNN TRAINING PERIOD HARDWARE SETUP 0 0, 5 1 1, 5 MONTHS 2 2, 5 3

MULTITUDE OF PRECISE NAVIGATION OPTIONS Optimized IMU Based Navigation On Board GPS Trained DNN model based navigation

CONCLUSION v The given solution of OPTIMIZED INERTIAL NAVIGATION SYSTEM AND DNN FOR AIRCRAFTS/HELICOPTER/UAV proposed an IMU providing essential data for navigation v Using Kalman filtering, it is possible to combine those sensor data to help mitigate any errors within a single sensor to create a more reliable independent navigation system. v Combining barometer and z-axis acceleration data, it is possible to get a more reliable estimate of altitude changes. v Combining GPS and X and Y axis accelerometer data, it is possible to get reliable displacement estimate within a couple of meters of actual, real-world displacement. v Moreover incorporating DNN into the flight controller can further enhance the navigation system. v However, when GPS data is not available, such as in a heavily forested area, the system can rely entirely on INS sensor data and the trained DNN model for X and Y displacement to accurately determine the navigation route. Let Us Join Together In Bringing Our Soldiers Safely Home

REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. D. Titterton and J. Weston, Strapdown Inertial Navigation Technology, 2 nd ed. Reston, VA: AIAA, 2005. Inven. Sense. MPU-9150 Datasheet, 18 Sept. 2013. Web. http: //www. invensense. com/mems/ gyro/documents/PS-MPU-9150 A-00 v 4_3. pdf. Weiss and J. Rowberg. 9 Degrees of Freedom - MPU-9150 Breakout (2013), Git. Hubrepository. https: //github. com/sparkfun/MPU-9150_Breakout K. Townsend. Adafruit Unified BMP 085/BMP 180 Driver (2013), Git. Hub repository. https: //github. com/adafruit/Adafruit_BMP 085_Unified G. Henderson. Gordon’s Projects - Wiring. Pi (2011), Web. https: //projects. drogon. net/raspberry-pi/wiringpi/ Schmidt, G. T. , Strapdown Inertial Systems - Theory and Applications, “AGARD Lecture Series, No. 95, 1978. Bar-Itzhack, I. Y. , and Berman, N. , Control Theoretic Approach to Inertial Navigation Systems, “Journal of Guidance, Vol. 11, No. 3, 1988, pp. 237245. Grewal, M. S. , and Andrews, A. P. , Kalman Filtering: Theory and Practice using. MATLAB, John Wiley and Sons, New York, 2001. Grewal, M. S. , Weill, L. R. , and Andrews, A. P. , Global Positioning Systems, Inertial Navigation, and Integration, John Wiley and Sons, New York, 2001. Barbour, Neil and Howell, William C. (2014). Inertial navigation system. In Access. Science. Mc. Graw-Hill Education. http: //dx. doi. org/10. 1036/10978542. 342700. Eure, K. W. , Quach, C. C. , Vazquez, S. L. , & Hill, B. L. (2013). An application of UAV altitude estimation using a low-cost inertial navigation system No. NASA/TM-2013 -218144). Hampton, Virginia: NASA. Nitti, D. O. , Bovenga , F. , Chiaradia, M. T. , Greco, M. , & Pinelli, G. (2015). Feasibility of using synthetic aperture radar to aid UAV navigation. Sensors, 15(8) doi: 10. 3390/s 150818334. Rapoport, I. , & Brandes, A. (2010). Analysis of sculling motion errors caused by sensor transfer function imperfections in strapdown inertial navigation systems. Paper presented at the Position Location and Navigation Symposium (PLANS), 2010 IEEE/ION, doi: 10. 1109/PLANS. 2010. 5507184.

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