MODEL SET ADAPTATION BY PROBABILITY MASS DIFFUSION
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Abstract
A method of performing a sequence of measurements, z, R; M; (t1,t2), of at least one parameter and recursively performing predictions. The method comprising the steps of—based on at least on a first measurement instance (M (tk); (k)), predicting the outcome (x, P) for at least two models (C, S); —after a subsequent measurement instance (M (tk+Tp)(k+Tp)) updating the models (C, S) for the corresponding point in time, whereby the prediction made on the basis of the first measurement instance is updated in the light of the subsequent measurement instance; and—re-arranging at least one model (C, S) for the subsequent measurement instance (tk+Tp) (k+Tp), whereby one updated model influences another updated model. For a model set comprising at least one complementary (C) model and at least one sub (S) model, under the step of rearranging the S model never influences the C model. For a model set comprising exclusively complementary (L, N, R) models, under the step of re-arranging, for a given pair of models within the model set (L, N, R), a model having a higher probability (μ) influences a model having a lesser probability, but wherein a model having a lesser probability (μ) never influences a model having a higher probability.
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Citations
43 Claims
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1-15. -15. (canceled)
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16. A method of performing a sequence of measurements (z, R;
- M;
(t1,t2)t1, t2)) of at least one parameter (Pos;
Vel;
x, P) and recursively performing predictions of at least the same or at least another parameter (Pos, Vel;
x, P), the prediction method being based on, for a number of prediction periods, for instance, corresponding to each possible measurement instance (tk,tk+Tp), defining a model set (PDF) having at least two alternative models (PDF) having respective different mean values (xip, . . . ), respective covariance matrices (Pip, . . . ) and corresponding respective probabilities (μ
ip, . . . ), the models (PDF) approximating possible outcomes, for instance, corresponding to various maneuvers in a two-dimensional plane, the model set (PDF) having at least one complementary (C) model and at least one sub (S) model, the method having the steps of;based on at least on a first measurement instance (M(tk);
(k)), predicting the outcome (x, P) for at least two models (C, S);after a subsequent measurement instance (M(tk+Tp) (k+Tp)) updating the models (C, S) for the corresponding point in time, whereby the prediction made on the basis of the first measurement instance is updated in the light of the subsequent measurement instance; re-arranging at least one model (C, S) for the subsequent measurement instance (tk+Tp) (k+Tp), whereby one updated model influences another updated model, and wherein the step of re-arranging the S model never influences the C model. - View Dependent Claims (17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 41)
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28. A method of performing a sequence of measurements (z, R;
- M;
(t1,t2)t1, t2)) of at least one parameter (Pos;
Vel;
x, P) and recursively performing predictions of at least the same or at least another parameter (Pos, Vel;
x, P), the prediction method being based on, for a number of prediction periods for instance corresponding to each possible measurement instance (tk,tk+Tp), defining a model set (PDF) comprising at least two alternative models (PDF) having respective different mean values (xip, . . . ), respective covariance matrices (Pip, . . . ) and corresponding respective probabilities (μ
ip, . . . ), the models (PDF) approximating possible outcomes, for instance corresponding to various maneuvers in a two-dimensional plane, the model set having exclusively complementary (L, N, R) models, the method comprising the steps of;based on at least on a first measurement instance (M(tk);
(k)), predicting the outcome (x, P) for at least two models (C, S);after a subsequent measurement instance (M(tk+Tp) (k+Tp)) updating the models (C, S) for the corresponding point in time, whereby the prediction made on the basis of the first measurement instance is updated in the light of the subsequent measurement instance; re-arranging at least one model (C, S) for the subsequent measurement instance (tk+Tp) (k+Tp), whereby one updated model influences another updated model, wherein for a given pair of models within the model set (L, N, R), a model having a higher probability (μ
) influences a model having a lesser probability, and wherein the step of re-arranging, for a given pair of models within the model set (L, N, R), a model having a lesser probability (μ
) never influences a model having a higher probability. - View Dependent Claims (29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43)
- M;
Specification