Real Time Implementation of a Military Impulse Classifier

A real time military impulse classifier has been developed to distinguish between impulsive events, such as artillery fire, and non-impulsive events, such as wind or aircraft noise. The classifier operates using an artificial neural network (ANN) with four scalar metrics as inputs. This classifier has been installed into two prototype noise monitoring systems, which are capable of establishing an accurate record of impulse events. This record can be used to assist in processing noise complaints and damage claims. The system continually monitors sound levels with a microphone array and activates when the sound level exceeds a given threshold. Once activated, the system processes the data to determine the classification, as well as the approximate bearing of the event

Unmanned Aerial Vehicle Navigation Using Wide-Field Optical Flow and Intertial Sensors

This paper offers a set of novel navigation techniques that rely on the use of inertial sensors and wide-field optical flow information. The aircraft ground velocity and attitude states are estimated with an Unscented Information Filter (UIF) and are evaluated with respect to two sets of experimental flight data collected from an Unmanned Aerial Vehicle (UAV). Two different formulations are proposed, a full state formulation including velocity and attitude and a simplified formulation which assumes that the lateral and vertical velocity of the aircraft are negligible. An additional state is also considered within each formulation to recover the image distance which can be measured using a laser rangefinder. The results demonstrate that the full state formulation is able to estimate the aircraft ground velocity to within 1.3 m/s of a GPS receiver solution used as reference "truth" and regulate attitude angles within 1.4 degrees standard deviation of error for both sets of flight data

A Fault-Tolerant Multiple Sensor Fusion Approach Applied to UAV Attitude Estimation

A novel sensor fusion design framework is presented with the objective of improving the overall multisensor measurement system performance and achieving graceful degradation following individual sensor failures. The Unscented Information Filter (UIF) is used to provide a useful tool for combining information from multiple sources. A two-step off-line and on-line calibration procedure refines sensor error models and improves the measurement performance. A Fault Detection and Identification (FDI) scheme crosschecks sensor measurements and simultaneously monitors sensor biases. Low-quality or faulty sensor readings are then rejected from the final sensor fusion process. The attitude estimation problem is used as a case study for the multiple sensor fusion algorithm design, with information provided by a set of low-cost rate gyroscopes, accelerometers, magnetometers, and a single-frequency GPS receiver’s position and velocity solution. Flight data collected with an Unmanned Aerial Vehicle (UAV) research test bed verifies the sensor fusion, adaptation, and fault-tolerance capabilities of the designed sensor fusion algorithm

A KALMAN FILTERING TUTORIAL FOR UNDERGRADUATE STUDENTS

ABSTRAC

Indoor and Outdoor Classification Using Light Measurements and Machine Learning

This work presents an indoor/outdoor classification system which uses light measurements coupled with machine learning algorithms to predict whether the sensing system is indoors or outdoors. The system measures ultraviolet light, color temperature, luminosity, and red, green, blue, and clear components of light at one-minute intervals using an Arduino-based measurement system. Three machine learning algorithms – support vector machine, artificial neural network, and bagged tree – were trained and tested using experimentally collected sensor data from multiple locations, dates, and times. A comparison of these classifiers revealed superior classification performance of the bagged tree classifier (>99%) compared to the other two algorithms. Each of the presented classifiers offered high estimation performance (>96.9%) in all the considered cases with cross-validation. These results demonstrate the feasibility of using light measurements alone to predict indoor or outdoor condition, which has practical applications in psychology research