TECHNICAL FIELD

Embodiments presented in this disclosure generally relate to identifying a location of a client device using access point (AP) voting.

BACKGROUND

Current location techniques measure concurrent Angle-of-Arrival (AoA) using gridded AoA heat maps to achieve room-level (1-5 meters) or even sub-meter location accuracy. However, location techniques where a maximum likelihood (ML) solution is approximated over gridded AoA heat maps suffer from efficiency problems in terms of computation, storage, and message passing. Specifically, generating AoA heat maps is a computationally intensive task, even when their computation region is bounded by a received signal strength indicator (RSSI) model.

For a given a client device, a location technique generates a heat map for each AP through correlating the measured AoA phase vector with the ideal AoA phase vectors for the grid points within the interested region. The total number of computations needed to locate the client device is proportional to the number of antennas for each AP, the number of APs involved in the location, the heat map grid size, and the size of the interested region. The number of complex multiplications and adds that need to be performed can balloon quickly, which can use a significant portion of the system resources in the APs.

