{
  "$type": "site.standard.document",
  "description": "An aerial vehicle position estimation system estimates the current position of an aerial vehicle by executing a machine learning model N using time-series data related to the aerial vehicle flying in a predetermined flight route as an input value. The machine learning model N is composed of an…",
  "path": "/patents/1381374",
  "publishedAt": "2026-04-23T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "G01C21/12",
    "Fukuoka Institute of Technology"
  ],
  "textContent": "An aerial vehicle position estimation system estimates the current position of an aerial vehicle by executing a machine learning model N using time-series data related to the aerial vehicle flying in a predetermined flight route as an input value. The machine learning model N is composed of an input cross-attention layer C, a self-attention layer S, and an output cross-attention layer C, and has a function of using the time-series data as an input value as first array data D and reconstructing the first array data based on predetermined weighting coefficients to generate second array data D",
  "title": "AERIAL VEHICLE POSITION ESTIMATION SYSTEM AND AERIAL VEHICLE POSITION ESTIMATION METHOD"
}