Autonomous Detection of Objects from Range Data Measurements
Final technical rept.,
ARMY ARMAMENT RESEARCH AND DEVELOPMENT CENTER WATERVLIET NY LARGE CALIBER WEAPON SYSTEMS LAB
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The overall objective of this study is to provide autonomous detection of obstacles or objects within a given field of view using noisy range data measurements as might be obtained from a laser rangefinder. Specifically, the goal is to provide simplified and efficient computer procedures suitable for filtering and processing the range data to detect objects. The particular procedure studied involves a single term state vector range with adaptive procedures for handling objects on a sloped plane. The range data is processed by incrementally varying elevation angle for fixed azimuth angle. The edges of objects are detected using a Bayesian decision procedure on the filtered range data. Results are presented showing the minimum object size that can be detected as a function of false alarm rate, Bayesian decision criteria, measurement noise level, and covariances of the artificial noise levels added to the filter to minimize false alarms. The artificial noise covariances can be either in the form of system plant noise or measurement noise. Results indicate that the most efficient approach to minimizing false alarms in terms of minimizing detectable object size is to adjust the Bayesian decision criteria. The least efficient approach is to artificially add system noise covariances.
- Lasers and Masers
- Optical Detection and Detectors