Patent ID: 9606848
Date: 2017-03-28
CPC Classifications: G01S,G06F

Claim:
1. A computer-implemented method of determining a Kalman-filtered estimated value of a state variable associated with an object, the method comprising: (a) generating, using a processor, a plurality of sequential measurements of the state variable as a function of a corresponding received plurality of sequential sensor input samples detected by one or more sensors at a platform, each state variable measurement including a respective noise contribution; (a1) receiving, at the processor, a plurality of sequential platform physical motion and position measurements from a motion sensing device at the platform corresponding to the plurality of sequential sensor input samples; (b) generating, using the processor, a plurality of sequential state variable estimates as a function of the plurality of sequential platform physical motion and position measurements and a current Kalman-filtered estimated value of the state variable generated per a Kalman-filtering function; (c) generating, using the processor, a plurality of respective errors as a function of a difference between the sequential state variable measurements and the sequential state variable estimates; (d) selectively removing, using the processor, at least one sequential state variable measurement and the corresponding platform physical motion and position measurements from the plurality of sequential state variable measurements as a function of a predetermined condition applied to the corresponding respective errors and those remaining sequential state variable measurements defining a tailored plurality of sequential state variable measurements; (e) applying, using the processor, the Kalman-filtering function, in sequence, to said tailored plurality of sequential state variable measurements and corresponding platform physical motion and position measurements to update the current Kalman-filtered estimated value of the state variable; and (f) repeating steps (c)-(e) using the tailored plurality of sequential state variable measurements and corresponding platform physical motion and position measurements until the Kalman-filtered estimated value has converged, whereby the Kalman-filtering function is not applied to the selectively removed at least one sequential state variable measurement in step (f).