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1 EP3629234A1
PSEUDO-RANGE ESTIMATION FROM A PASSIVE SENSOR
Publication/Patent Number: EP3629234A1 Publication Date: 2020-04-01 Application Number: 19199796.4 Filing Date: 2019-09-26 Inventor: Clothier, Reece Alexander   Williams, Brendan Patrick   Hegarty cremer solene gabrielle doreen   Assignee: The Boeing Company   IPC: G06K9/00 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.
2 US2020104609A1
Pseudo-Range Estimation From A Passive Sensor
Publication/Patent Number: US2020104609A1 Publication Date: 2020-04-02 Application Number: 16/143,866 Filing Date: 2018-09-27 Inventor: Clothier, Reece Alexander   Williams, Brendan Patrick   Hegarty cremer solene gabrielle doreen   Assignee: The Boeing Company   IPC: G06K9/00 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.