Kalman filter iteratively performing time/measurement update of user/relative floor locations

摘要

Several systems and methods for location estimation in a multi-floor environment are disclosed. In an embodiment, the method includes performing wireless scanning so as to receive wireless signals from one or more access points from among a plurality of access points positioned at plurality of locations, respectively at one or more floors from among a plurality of floors within the multi-floor environment. A first set of RSSI measurements is computed corresponding to the wireless signals. Absolute floor location information is determined based on the first set of RSSI measurements and a pre-defined objective function. The pre-defined objective function is configured to maximize a probability of a user being located at a floor so as to receive the wireless signals. A user floor location is determined based on the absolute floor location information. The user location is estimated at least in part based on the user floor location.

主权项

1. A location estimation method comprising:
performing a wireless scanning so as to receive wireless signals from one or more wireless access point devices from among a plurality of access point devices positioned at plurality of locations, respectively, at one or more floors from among a plurality of floors within a multi-floor building; computing a first set of received signal strength indication (RSSI) measurements from the received wireless signals; determining absolute floor location information in the multi-floor building based on the first set of RSSI measurements and a pre-defined objective function, the pre-defined objective function maximizing a probability of a user being located at a floor from among the plurality of floors so as to receive the wireless signals from the first set of RSSI measurements, in which the probability is estimated based on a three-dimensional channel model including a plurality of channel model parameters; determining a relative floor location estimate based on information from a sensor; determining a user floor location based on the absolute floor location information; estimating the user location in the multi-floor building based at least in part on the user floor location and the relative floor location estimate using a Kalman filter iteratively performing a time and measurement update; identifying, subsequent to performing the wireless scanning, a primary set of access point devices and a secondary set of access point devices from among the plurality of access point devices, in which the primary set of access point devices correspond to those one or more access point devices from which wireless signals are received during the wireless scanning, and, the secondary set of access point devices correspond to those access point devices whose presence has been ascertained previous to the wireless scanning and from which wireless signals are not received during the wireless scanning; the method including computing a geometric measure based on locations associated with access point devices from among the identified primary set of access point devices and the secondary set of access point devices; generating an imaginary three-dimensional grid based on the geometric measure, the three-dimensional grid extending across one or more floors in the multi-floor building and including a plurality of grid points; computing a likelihood value at each grid point device of the plurality of grid point devices; identifying a set of grid point devices with associated likelihood values which when averaged maximizes the pre-defined objective function, the set of grid point devices associated with a floor location from among the one or more floors; and identifying floor location information associated with the set of grid point devices as the absolute floor location information, the absolute floor location information including an absolute floor location estimate and a first uncertainty estimate associated with the absolute floor location estimate.