Laith R. Sahawneh

Bio: Laith R. Sahawneh is an academic researcher from Brigham Young University. The author has contributed to research in topics: Collision detection & National Airspace System. The author has an hindex of 10, co-authored 15 publications receiving 201 citations. Previous affiliations of Laith R. Sahawneh include University of Florida & American University of Sharjah.

Development and calibration of low cost MEMS IMU for UAV applications

Abstract: In this paper, an affordable and reliable low-cost inertial measurement unit (IMU) is designed using commercial off-the-shelf (COTS) components. Micro electro-mechanical systems (MEMS) accelerometers and rate-gyros are used, and a methodology for calibrating the resulting IMU is developed when the precise orientation of the IMU is unknown. The proposed methodology requires no precise mechanical platform for the accelerometer while as simple rotating table is utilized for the rate-gyros calibration. The proposed calibration methods utilize the fact that ideally the norm of the measured output of the accelerometer and gyro cluster is equal to the magnitude of applied force and rotational velocity, respectively. Least squares method is used to evaluate the accelerometers and gyros model coefficients.

Airborne Radar-Based Collision Detection and Risk Estimation for Small Unmanned Aircraft Systems

Abstract: Airborne collision detection is a difficult problem due to inherent noise, errors in prediction, and challenges associated with modeling the dynamics of the intruder aircraft. Moreover, onboard limited computational resources, fast closing speeds, and unanticipated maneuvers make it challenging to detect collision without creating too many false alarms. In this paper, an innovative approach is presented to quantify likely intruder trajectories and estimate the probability of collision risk for a pair of aircraft flying at the same altitude and in close proximity given the state estimates provided by an airborne radar sensor. The proposed approach is formulated in a probabilistic framework using the reachable set concept and the statistical data contained in the uncorrelated encounter model developed by Lincoln Laboratory, Massachusetts Institute of Technology. Monte-Carlo-based simulation is used to evaluate and compare the performance of the proposed approach with linearly extrapolated collision-detectio...

Real-Time Implementation of GPS Aided Low-Cost Strapdown Inertial Navigation System

Abstract: This work details the study, development, and experimental implementation of GPS aided strapdown inertial navigation system (INS) using commercial off-the-shelf low-cost inertial measurement unit (IMU). The data provided by the inertial navigation mechanization is fused with GPS measurements using loosely-coupled linear Kalman filter implemented with the aid of MPC555 microcontroller. The accuracy of the estimation when utilizing a low-cost inertial navigation system (INS) is limited by the accuracy of the sensors used and the mathematical modeling of INS and the aiding sensors' errors. Therefore, the IMU data is fused with the GPS data to increase the accuracy of the integrated GPS/IMU system. The equations required for the local geographic frame mechanization are derived. The direction cosine matrix approach is selected to compute orientation angles and the unified mathematical framework is chosen for position/velocity algorithm computations. This selection resulted in significant reduction in mechanization errors. It is shown that the constructed GPS/IMU system is successfully implemented with an accurate and reliable performance.

Chain-based Collision Avoidance for UAS Sense-and-Avoid Systems

Abstract: An Unmanned Aircraft System (UAS) collision avoidance algorithm for the Sense and Avoid (SAA) problem is proposed. The avoidance path is modeled using a physical analogy of a chain placed in a force field. This approach has the advantage that it continuously searches the space for a proper evasive maneuver to avoid an impending collision with detected intruding aircraft while observing the standard flight rules followed by manned aircraft. The algorithm also accounts for the uncertainties associated with the intruder aircraft’s state estimates computed by the detection algorithm. To examine the proposed avoidance algorithm we combined the electro-optical (EO) based Monocular Maneuverless PAssive Ranging System (M2PARS) technology developed by UtopiaCompression Corporation (UC) for detecting imminent collisions using passive imaging sensors and simulated the integrated model to predesigned encounter geometry scenarios in the Matlab/Simulink environment.

Detect and Avoid for Small Unmanned Aircraft Systems Using ADS-B

Abstract: With the increasing demand to integrate unmanned aircraft systems (UAS) into the National Airspace System (NAS), new procedures and technologies are necessary to ensure safe airspace operations and...

Improved multi-position calibration for inertial measurement units

Abstract: Calibration of inertial measurement units (IMU) is carried out to estimate the coefficients which transform the raw outputs of inertial sensors to meaningful quantities of interest. Based on the fact that the norms of the measured outputs of the accelerometer and gyroscope cluster are equal to the magnitudes of specific force and rotational velocity inputs, respectively, an improved multi-position calibration approach is proposed. Specifically, two open but important issues are addressed for the multi-position calibration: (1) calibration of inter-triad misalignment between the gyroscope and accelerometer triads and (2) the optimal calibration scheme design. A new approach to calibrate the inter-triad misalignment is devised using the rotational axis direction measurements separately derived from the gyroscope and accelerometer triads. By maximizing the sensitivity of the norm of the IMU measurement with respect to the calibration parameters, we propose an approximately optimal calibration scheme. Simulations and real tests show that the improved multi-position approach outperforms the traditional laboratory calibration method, meanwhile relaxing the requirement of precise orientation control.

Global Positioning Systems

Abstract: Introduction: Global Positioning Systems (GPS) offer many benefits in assisting school transportation operations achieve their mission. A variety of systems exist with varying capabilities. These systems offer not only positioning information in nearly real time and/or delayed time, but also vehicle position history, travel route, with speed and time, student identification, emergency alert, and automated accident notification. Recognizing that many systems are available that provide this information, and further, that this information is valuable in efficiently managing the school bus fleet, and further, that standardization of minimum information across the State of Michigan would be beneficial to school districts, the Pupil Transportation Advisory Committee recommends the Michigan Department of Education adopt the following as a recommended guideline for purchasing GPS equipment. ________________________________________________________________

MEMS based IMU for tilting measurement: Comparison of complementary and kalman filter based data fusion

Abstract: This research investigates real time tilting measurement using Micro-Electro-Mechanical-system (MEMS) based inertial measurement unit (IMU). Accelerometers suffer from errors caused by external accelerations that sums to gravity and make accelerometers based tilting sensing unreliable and inaccurate. Gyroscopes can offset such drawbacks but have data drifting problems. This paper presents a study on complementary and Kalman filter for tilting measurement using MEMS based IMU. The complementary filter algorithm uses low-pass filter and high-pass filter to deal with the data from accelerometer and gyroscope while Kalman filter takes the tilting angle and gyroscope bias as system states, combining the angle derived from the accelerometer to estimate the tilting angle. The study carried out both static and dynamic experiments. The results showed that both Complementary and Kalman filter were less sensitive to variations and almost no signal coupling phenomenon and able to obtain smooth and accurate results.

Sense and avoid for unmanned aircraft systems

Abstract: Sense and avoid (SAA) represents one of the main roadblocks to the integration of unmanned aircraft systems (UAS) operations by aviation authorities around the world. This tutorial outlines and reviews the substantial breadth of SAA architectures, technologies, and algorithms. Starting from a discussion about what constitutes a UAS and how it is different than manned aircraft, basic SAA definitions and taxonomies are discussed. The SAA process is dissected into three fundamental tasks, defined as sensing, detecting, and avoiding, which are discussed in detail. The tutorial concludes with a summary of the regulatory and technical issues that continue to challenge the progress on SAA, as a key component of reliable UAS operation in civil aviation authorities (CAAs) around the world.

Well-Clear Recommendation for Small Unmanned Aircraft Systems Based on Unmitigated Collision Risk

Abstract: Unmanned aircraft systems must demonstrate a capability to sense and avoid air traffic as part of a layered conflict management system to enable safe operations in the National Airspace System. Dur...