16.322 Stochastic Estimation and Control, Fall 2004 Prof vander velde Estimation We wish to estimate the values of a set of parameters which may be static dynamic based on all available information measurements prior knowledge physical constraints The measurements may be direct inferential(e.g. airspeed indicator-infers airspeed from dynamic pressure measurement) of varying quality Discussion Problem formulation tic or dy rameters static affords some simplifications but consider the whole problem: what is to be done with the estimates? All information should be used better quality information weighted more heavily than poorer Value of measurements depends both on noise and sensitivity to least certain parameters poorer quality radar gh quality radar Suppose you have a radar looking at satellites overflying your position. Having a second location 90 degrees around the world would clarify parameters that are not were characterized by a single sensor Include the knowledge you have prior to the current set of measurements Physical constraints add information-use all known16.322 Stochastic Estimation and Control, Fall 2004 Prof. Vander Velde Page 3 of 8 Estimation We wish to estimate the values of a set of parameters which may be - static - dynamic based on all available information - measurements - prior knowledge - physical constraints The measurements may be - direct - inferential (e.g. airspeed indicator – infers airspeed from dynamic pressure measurement) - of varying quality. Discussion Problem formulation - static or dynamic parameters - static affords some simplifications but consider the whole problem: what is to be done with the estimates? All information should be used - better quality information weighted more heavily than poorer Value of measurements depends both on noise and sensitivity to least certain parameters. Suppose you have a radar looking at satellites overflying your position. Having a second location 90 degrees around the world would clarify parameters that are not were characterized by a single sensor. Include the knowledge you have prior to the current set of measurements. Physical constraints add information – use all known