International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Tactical unmanned aerial vehicles (UAV’s) can deliver real-time battlefield video directly to the soldier providing unprecedented situational awareness. The video communications system must be compact, lightweight, secure, and easy to deploy without a complicated ground station. Pacific Microwave Research, Inc. is developing a system capable of providing reliable and secure video communications to handheld terminals throughout the theater. PMR’s Coded Orthogonal Frequency Division Multiplex (COFDM) video transmission system is designed for tactical video transmission in battlefield or Military Operations in Urban Terrain (MOUT) environments. Using digital modulation coding, the system provides a very robust link in the mobile environment.

Water is a major factor limiting cotton production and farmers must aim to optimise crop water use through timely irrigation scheduling decisions. Airborne video imagery when calibrated with a low density of ground based observations, offers the potential for near real time monitoring of crop condition, through sequential coverages of entire cotton fields. Using commercially available video equipment mounted on a light aircraft images were acquired of field experiments that were established in commercial cotton fields to test if the imagery could monitor changes in crop condition. Ground data collected from these experiments were used to evaluate green, red, near infrared and thermal band imagery for irrigated crop management. Prior to acquiring imagery, a ground radiometer study was conducted to investigate if canopy reflectance changed with the onset of crop water stress. Canopy reflectance decreased in the near infrared and green bands during the five day period prior to the crop's normal irrigation date. Red reflectance increased only after the crop irrigation was due, when the crop was suffering from water stress. The greatest change in canopy reflectance was in the near infrared region, attributable in part to a decrease in ground cover caused by canopy architectural changes including leaf wilting. The results of this experiment were used to select spectral filters for the video cameras. A range of crop conditions were identified in the imagery including; crop waterlogging, wheeltrack soil compaction, crop nitrogen status, different varieties, crop maturity, canopy development, soil moisture status, cotton yield and nutgrass weeds. Thermal imagery was the most successful for distinguishing differences in the crop soil moisture status. Near infrared imagery was most closely related to crop canopy development and is recommended for monitoring crop growth. Linear relationships were found between spectral responses in the imagery, crop reflectance (%) and crop temperature measured on the ground. Near infrared reflectance linearly increased, while spectral responses in the green, red and thermal bands exhibited an inverse relationship with plant height and ground cover. Imagery collected early in the season was affected by the soil background. Final lint yield was related to imagery in the red band. As the soil moisture level declined, crop temperature increased while reflectance in the green band decreased. To ensure an accurate relationship between soil moisture and thermal imagery, separate calibration equations are recommended for different stages in the season. Green, red and near infrared imagery were affected by the sun angle that caused one side of the imagery to appear brighter than the other. This problem was greatest in the green and red bands, but was not evident in the thermal imagery. Changes in solar radiation and air temperature on some occasions caused greater variation to the imagery between flights, than changes in crop condition per se. Therefore, it is not aIways possible to directly determine the soil moisture status from canopy temperature. Further research is required to correct imagery for environmental variables such as solar radiation, air temperature and vapour pressure deficit. Thermal imagery offers many improvements to current irrigation scheduling techniques including the facilitation of locating more representative ground sampling points. Thermal imagery also enables cotton fields on a farm to be ranked according to their soil moisture status. This then provides farmers with a visual picture of the crop water status across the whole farm, which is not possible using conventional ground scheduling techniques. At this stage, airborne video imagery will not replace soil moisture data collected for irrigation scheduling, however offers potential to enhance irrigation scheduling methods by addressing the problem of crop variability within cotton fields.

In this thesis, multiple on-road vehicle tracking problem is explored, with greater consideration of road constraints and interactions between vehicles. A comprehensive method for tracking multiple on-road vehicles is proposed by making use of domain knowledge of on-road vehicle motion. Starting with raw measurements provided by sensors, bias correction methods for sensors commonly used in vehicle tracking are briefly introduced and a fast but effective bias correction method for airborne video sensor is proposed. In the proposed method, by assuming errors in sensor parameter measurements are close to zero, the bias is separately addressed in converted measurements of target position by a linear term of errors in sensor parameter measurements. Based on this model, the bias is efficiently estimated by addressing it while tracking or using measurements of targets that are observed by multiple airborne video sensors simultaneously. The proposed method is compared with other airborne video bias correction methods through simulations. The numerical results demonstrate the effectiveness of the proposed method for correcting bias as well as its high computational efficiency. Then, a novel tracking algorithm that utilizes domain knowledge of on-road vehicle motion, i.e., road-map information and interactions among vehicles, by integrating a car-following model into a road coordinate system, is proposed for tracking multiple vehicles on single-lane roads. This algorithm is extended for tracking multiple vehicles on multi-lane roads: The road coordinate system is extended to two-dimension to express lanes on roads and a lane-changing model is integrated for modeling lane-changing behavior of vehicles. Since the longitudinal and lateral motions are mutually dependent, the longitudinal and lateral states of vehicles are estimated sequentially in a recursive manner. Two estimation strategies are proposed: a) The unscented Kalman filter combined with the multiple hypothesis tracking framework to estimate longitudinal and lateral states of vehicles, respectively. b) A unified particle filter framework with a specifically designed computationally-efficient joint sampling method to estimate longitudinal and lateral states of vehicles jointly. Both of two estimation methods can handle unknown parameters in motion models. A posterior Cramer-Rao lower bound is derived for quantifying achievable estimation accuracy in both single-lane and multi-lane cases, respectively. Numerical results show that the proposed algorithms achieve better track accuracy and consistency than conventional multi-vehicle tracking algorithms, which assumes that vehicles move independently of one another. / Thesis / Doctor of Philosophy (PhD)