US 12,294,970 B2
System and method for diagnosing sensor performance of an ultra wide band sensor localization
Jinzhu Chen, Troy, MI (US); Zijun Han, Rochester Hills, MI (US); Fan Bai, Ann Arbor, MI (US); Aaron Adler, Rochester Hills, MI (US); and John Sergakis, Bloomfield Hills, MI (US)
Assigned to GM GLOBAL TECHNOLOGY OPERATIONS LLC, Detroit, MI (US)
Filed by GM Global Technology Operations LLC, Detroit, MI (US)
Filed on Oct. 26, 2022, as Appl. No. 18/049,902.
Prior Publication US 2024/0172164 A1, May 23, 2024
Int. Cl. H04W 64/00 (2009.01); H04B 1/69 (2011.01)
CPC H04W 64/00 (2013.01) [H04B 1/69 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method of diagnosing sensor performance of an ultra wide band (UWB) sensor localization for a vehicle, the method comprising:
receiving sensor signals from at least four UWB anchors and a UWB tag for a time period, the sensor signals representing anchor coordinates and real-time distances between the tag and each anchor;
aligning the sensor signals at an aligned timestamp during the time period by way of:

OG Complex Work Unit Math
where tsi is an initial timestamp of the time period, k is a number of timestamps of the time period, fi is a fixed data uploading frequency, ti is the aligned timestamp, tsi+k/fi is at an upper limit thereof to define aligned data;
determining intersections of the aligned data based on the anchor coordinates and the real-time distances by way of:

OG Complex Work Unit Math
where Np is total number of intersections, k is an iteration, CN2 is an overall number of groups, and NoIk is a number of intersections between a pair of non-concentric circles of the aligned data to define points of intersections;
clustering the points of intersections by way of:

OG Complex Work Unit Math
where ϵ is a distance threshold between each point, P is a distance ratio threshold, n is a number of sensors, and Rî is an average distance value of the aligned data from the UWB anchors, Min(P) is a minimum points threshold, and m is a number of signals from the UWB anchors to define at least one cluster of points of the UWB anchors;
calculating a clustering quality of each of the at least one cluster by way of:

OG Complex Work Unit Math
where ρ is the clustering quality, e is error between a number of points in the at least one cluster, and Nc is a number of points in the at least one cluster;
determining a geometric center of the at least one cluster by way of:

OG Complex Work Unit Math
where oc is the geometric center of the at least one cluster, pi is a point of the at least one cluster, and n is a number of intersections of the at least one cluster;
calculating a clustering variance of each of the at least one cluster by way of:

OG Complex Work Unit Math
where δ is the clustering variance, pi is points of the at least one cluster, oc is a geometric center of each cluster to define a sensed location of the tag for each cluster;
finding a clustering contribution of each anchor by way of the intersections of the aligned data for the at least one cluster when one of the clustering quality is below a normal quality and the clustering variance is above a normal variance to define a first contribution low of one of the anchors; and
determining an erratic anchor based the contribution low.