The testing of unmanned vehicles presents a need for an independent device capable of accurately collecting position and orientation data. While commercial-off-the-shelf components could be pieced together to sense and record this information, this is an expensive, large, and heavy solution, not suitable for small or aerial vehicles. The micro wireless instrumented payload, or μWIP, was designed precisely for this purpose. The μWIP includes a GPS receiver, 3-axis accelerometer, 3-axis gyroscope, and 3-axis magnetometer which are used to measure an unmanned vehicle's position and orientation. The device also uses a secure digital card for data storage, and an 802.11b module to provide wireless connectivity. Additionally, the μWIP contains a on-board battery and the circuitry required to charge it. Firmware for the ARM7 processor was written to allow sensor calibration and data transmission, and a user interface was designed to run on a personal computer. The finished design is a tiny 3''x5''x1'', and weighs a mere 0.8 pounds including battery and antennas. It is capable of continuously streaming accurate GPS and inertial data over an 802.11b wireless network for over 5 hours. Having a bill of materials cost just over $600, the μWIP is also more cost effective than any alternative solutions. This thesis details the hardware and software design of the μWIP, as well as the initial testing, calibration, and evaluation of the device. / Master of Science

Thesis. 1976. M.S.--Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. / Microfiche copy available in Archives and Aero. / Includes bibliographical references. / M.S.

This study investigated the predictive value of time on-task and error scores on tests administered through Control Data Corporation PLATO interactive computer graphics simulation as predictors of errors and time on-task for inertial navigation system equipment operation. In addition, the correlation between simulated pass/fail error and time on-task scores, and subsequent pass/fail criteria using actual equipment was investigated.

International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / With the development of microcomputer technology, the application of strap-down inertial navigation on aircraft is used more frequently. The attitude measurement for miniature spacecraft is most important. Installing three-axis acceleration sensors and three-axis rate gyros on the spacecraft, the accelerations and attitudes can be obtained through the PCM/FM telemetry system. Then, the initial attitude of spacecraft is given through outside measurement and telemetry. Finally, in the ground station, the parameters of spacecraft attitude are given by using strapdown inertial navigation theory and quanternion differential equation for solving the attitude.

The ARIES Unmanned Underwater Vehicle (UUV) currently uses an Inertial Measurement Unit (IMU) with an inherent rotation rate error bias of 10 degrees/hour. Then need for a more accurate IMU for long term missions has led to the purchase of the Honeywell HG1700 IMU. The HG1700 is a ring laser gyroscope designed specifically as part of the navigation software in multiple U.S. missiles. The objective of this research is to perform numerous bench tests on the HG1700 to test its capabilities and to begin the process of implementing the IMU into the ARIES unmanned underwater vehicle. Specifically, the IMU is tested for correct setup configurations, angle of rotation accuracies, the rotation rate error bias, and positional accuracies. Also, guidelines for integrating the IMU with the current software in the ARIES vehicle are discussed.

The Combined Laser and Scan Sonar (CLASS) system is an extended range imaging system, incorporating both high-resolution laser images and high frequency sonar images. Both the laser and sonar images are collected simultaneously during testing to provide dual mode imagery of an underwater target, displaying both a 2D image of the target (laser image) and a 3D overlay of the target (sonar image). The laser component of the system is a Multiple Overlapping Field of view Serial Laser Imager (MOFSLI), capable of generating high-resolution sub-centimeter 2D images. MOFSLI generates the images by way of a near diffraction-limited 532 [nm] continuous wave (CW) laser beam being scanned over the target. Initial field tests resulted in high-quality images of the ocean floor, but also indicated the need for additional research on MOFSLI. In this thesis, we focus on the calibration of MOFSLI and on the evaluation of the image quality generated by this system, as a function of range, source power, receiver gain and water turbidity. This work was completed in the specialized underwater electrooptics testing facility located in the Ocean Visibility and Optics laboratory at Harbor Branch Oceanographic Institute (HBOI). Laboratory testing revealed the operational limits of the system, which functioned well until just beyond five attenuation lengths, where it becomes contrast limited due attenuation of target signal and the collection of non-image bearing backscattered photons. Testing also revealed the optimal settings of the system at given environmental conditions. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2015. / FAU Electronic Theses and Dissertations Collection

Inertial navigation is a relative navigation technique commonly used by autonomous vehicles to determine their linear velocity, position and orientation in three-dimensional space. The basic premise of inertial navigation is that measurements of acceleration and angular velocity from an inertial measurement unit (IMU) are integrated over time to produce estimates of linear velocity, position and orientation. However, this process is a particularly involved one. The raw inertial data must first be properly analyzed and modeled in order to ensure that any inertial navigation system (INS) that uses the inertial data will produce accurate results. This thesis describes the process of analyzing and modeling raw IMU data, as well as how to use the results of that analysis to design an INS. Two separate INS units are designed using two different micro-electro-mechanical system (MEMS) IMUs. To test the effectiveness of each INS, each IMU is rigidly mounted to an unmanned ground vehicle (UGV) and the vehicle is driven through a known test course. The linear velocity, position and orientation estimates produced by each INS are then compared to the true linear velocity, position and orientation of the UGV over time. Final results from these experiments include quantifications of how well each INS was able to estimate the true linear velocity, position and orientation of the UGV in several different navigation scenarios as well as a direct comparison of the performances of the two separate INS units.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Stephen G. Sifferlen. / M.S.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND AERO. / Bibliography: leaves 83-84. / by Steven Ray Hall. / M.S.

Yip, Wai Lee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 119-124). / Abstracts in English and Chinese. / Abstract --- p.i / 摘要 --- p.ii / Acknowledgement --- p.iii / Table of Content --- p.iv / List of Figure --- p.vi / List of table --- p.viii / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Background information --- p.2 / Chapter 1.2.1 --- Overview of positioning technologies --- p.2 / Chapter 1.2.2 --- Comparison between different positioning systems --- p.7 / Chapter 1.2.3 --- Recent works related to INS --- p.9 / Chapter 1.3 --- Objective --- p.11 / Chapter 1.4 --- Organization of thesis --- p.11 / Chapter Chapter 2 --- Literature Study --- p.13 / Chapter 2.1 --- Introduction to INS --- p.13 / Chapter 2.1.1 --- Coordinate Frames --- p.13 / Chapter 2.1.2 --- Gimbaled INS --- p.16 / Chapter 2.1.3 --- Strapdown INS --- p.17 / Chapter 2.1.4 --- Conventional algorithm of strapdown INS --- p.17 / Chapter 2.2 --- Inertial sensors --- p.19 / Chapter 2.2.1 --- Gyroscope --- p.19 / Chapter 2.2.2 --- Accelerometer --- p.20 / Chapter 2.3 --- Previous works --- p.22 / Chapter 2.4 --- GF-INS --- p.23 / Chapter 2.5 --- Summary --- p.25 / Chapter Chapter 3 --- Performance of MEMS accelerometer in position determination --- p.27 / Chapter 3.1 --- Basic principle --- p.27 / Chapter 3.2 --- Numeric integration --- p.28 / Chapter 3.3 --- Experimental setup --- p.30 / Chapter 3.3.1 --- MEMS Accelerometer --- p.30 / Chapter 3.3.2 --- Microcontroller --- p.32 / Chapter 3.3.3 --- System architecture --- p.33 / Chapter 3.3.4 --- Testing platform --- p.34 / Chapter 3.4 --- Initial calibration and filtering --- p.37 / Chapter 3.4.1 --- Convert ADC reading to acceleration --- p.37 / Chapter 3.4.2 --- Identify configuration error --- p.38 / Chapter 3.4.3 --- Implement low pass filter --- p.39 / Chapter 3.5 --- Experimental results --- p.40 / Chapter 3.5.1 --- Results --- p.40 / Chapter 3.5.2 --- Discussion --- p.43 / Chapter 3.6 --- Summary --- p.45 / Chapter Chapter 4 --- Performance Improvement --- p.46 / Chapter 4.1 --- Fuzzy logic based steady state detector --- p.46 / Chapter 4.1.1 --- Principle --- p.46 / Chapter 4.1.2 --- Experimental result --- p.48 / Chapter 4.2 --- Kalman filtering --- p.50 / Chapter 4.2.1 --- Discrete Kalman filter --- p.50 / Chapter 4.2.2 --- Combine with fuzzy logic based steady state detector --- p.52 / Chapter 4.2.3 --- Experimental results --- p.54 / Chapter 4.3 --- Summary --- p.58 / Chapter Chapter 5 --- Construction of GF-INS --- p.59 / Chapter 5.1 --- Principle of GF-INS --- p.59 / Chapter 5.1.1 --- Algorithm --- p.59 / Chapter 5.1.2 --- Comparing error of GF-INS and conventional INS --- p.66 / Chapter 5.1.3 --- Simulation study --- p.67 / Chapter 5.2 --- Experimental setup --- p.73 / Chapter 5.3 --- Experimental Results --- p.75 / Chapter 5.4 --- Summary --- p.81 / Chapter Chapter 6 --- Improvement on the GF-INS --- p.82 / Chapter 6.1 --- Configuration error compensation --- p.82 / Chapter 6.1.1 --- "Identify bias, scale factor and sensing direction error" --- p.83 / Chapter 6.1.2 --- Identify position error --- p.86 / Chapter 6.1.3 --- Compensator design --- p.89 / Chapter 6.1.4 --- Simulation --- p.91 / Chapter 6.2 --- Fuzzy rule based motion state detector --- p.97 / Chapter 6.2.1 --- Relation of data in different motions --- p.97 / Chapter 6.2.2 --- Fuzzy system --- p.99 / Chapter 6.2.3 --- Membership function training with gradient descent --- p.101 / Chapter 6.3 --- Experimental results and discussion --- p.104 / Chapter 6.3.1 --- Configuration errors --- p.104 / Chapter 6.3.2 --- Compensator --- p.106 / Chapter 6.3.3 --- Fuzzy rule based motion state detector --- p.107 / Chapter 6.3.4 --- Comparing the performance of both methods --- p.110 / Chapter 6.3.5 --- Comparing GF-INS and one dimensional INS --- p.112 / Chapter 6.3.6 --- Discussion --- p.113 / Chapter 6.4 --- Summary --- p.115 / Chapter Chapter 7 --- Conclusions and Future works --- p.116 / Reference --- p.119