WSN定位算法简介.pptVIP

Typical Self-Localization Algorithms N-hop multilateration primitive Localization Algorithm Improvement of AHLoS System Sufficient Conditions whether can be enlisted in collaborative multilateration Iterative positioning using an Extended Kalman Filter to improve positioning accuracy Three phases as Figure shows: ReTurner.D Find nodes with unique position solutions Compute Initial Position Estimates For all nodes Compute location estimates Compute estimate at each node Communicate Criteria met? YES NO Communicate results to central point Transmit estimates back to each unknown node Refine estimates of under-constrained nodes Done Done Centralized Computation Distributed Computation PHASE 1 PHASE 2 PHASE 3: Refinement PHASE 3: Refinement Typical Self-Localization Algorithms ReTurner.D Typical Self-Localization Algorithms Initial Estimates (Phase 2) Use the accurate distance measurements to impose constraints in the x and y coordinates – bounding box Use the distance to a beacon as bounds on the x and y coordinates a a a x U ReTurner.D Typical Self-Localization Algorithms Initial Estimates (Phase 2) Use the accurate distance measurements to impose constraints in the x and y coordinates – bounding box Use the distance to a beacon as bounds on the x and y coordinates Do the same for beacons that are multiple hops away Select the most constraining bounds a b c b+c b+c X Y U U is between [Y-(b+c)] and [X+a] ReTurner.D Typical Self-Localization Algorithms Initial Estimates (Phase 2) Use the accurate distance measurements to impose constraints in the x and y coordinates – bounding box Use the distance to a beacon as bounds on the x and y coordinates Do the same for beacons that are multiple hops away Select the most constraining bounds Set the center of the bounding box as the initial estimate a a a b c b+c b+c X Y U ReTurner.D Typical Self-Localization Algorithms Performance Evaluation Provided Location error: 1cm Anchor nodes ratio of 20% Performa