From: Static force field representation of environments based on agents’ nonlinear motions

Method used in | Segmentation/Reconstruction Type | Input features | Error measure | Output |
---|---|---|---|---|

[40] | Batch (top-down) | Data from etch machine |
ℓ
^{2} distance between line and data points
| Identification of states in a HMM |

[41] | Online (Sliding window + top down) | Data points series |
ℓ
^{2} distance between line and data points
| Signal’s piecewise linear representation |

[42] | Online (Slope change threshold) | Data points series | Distance between reference slopes | Signal’s piecewise linear representation |

[43] | Batch (bottom-up) | High frequency data points |
ℓ
^{2} distance between line and super-interval data
| Identification of events |

[44] | Online (furthest candidate segmenting point) | Data points series | Maximum vertical distance to a line | Signal’s piecewise linear representation |

[44] | Online (Backward segmentation) | Data points series | Maximum vertical distance to a line | Signal’s piecewise linear representation |

[45] | Offline (nonlinear state estimation) | Aircraft sensor data | Root-mean-square error | Aircraft path reconstruction |

[46] | Offline | Interactive Multiple Mode filter data | Distance to predefined modes of flight | Identification of modes of flight |

Proposed work | Online (sliding window and angle distributions) | Velocity vectors from bank of filters | Mahalanobis distance between angle and distribution | Velocity vectors linked to an attractive field |