Abstract: An apparatus is provided for detecting and avoiding objects in real-time. The apparatus includes a first sensor that collects environmental data and a second sensor that collects external data corresponding to an external object within a detection range from the apparatus. The apparatus further includes a processor that, in real-time, calculates a minimum distance to avoid the external object, based at least in part on the environmental data and the external data, monitors the environmental data and the external data, and controls the apparatus to avoid the external object based on the calculated minimum distance and the monitored environmental data and external data.

Abstract: A rough initial estimate of Line of Sight range (“pseudo-range”) is generated essentially immediately following the detection of an object by a passive sensor on a vehicle. The data are combined with prior detection likelihood and prior performance models for the sensor. These comparisons generate a posterior probability distribution of pseudo-range estimates. A pseudo-range estimate is derived from the probability distribution and output for use in detect and avoid decision-making and action planning. The pseudo-range estimate can be updated to improve its accuracy, such as by using a recursive filter (e.g., a Kalman filter). Other information, such as current atmospheric data, or known (or likely) vehicular activity in the region and at the current time, can be used in addition to the vehicle's spatial and temporal location, to improve accuracy.

Abstract: A rough initial estimate of Line of Sight range (“pseudo-range”) is generated essentially immediately following the detection of an object by a passive sensor on a vehicle. The data are combined with prior detection likelihood and prior performance models for the sensor. These comparisons generate a posterior probability distribution of pseudo-range estimates. A pseudo-range estimate is derived from the probability distribution and output for use in detect and avoid decision-making and action planning. The pseudo-range estimate can be updated to improve its accuracy, such as by using a recursive filter (e.g., a Kalman filter). Other information, such as current atmospheric data, or known (or likely) vehicular activity in the region and at the current time, can be used in addition to the vehicle's spatial and temporal location, to improve accuracy.