Wireless Node Location Clustering for Multipath Interference

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Solution Overview

Problem

In wireless communications, especially for IoT applications, multipath interference during RF signal exchange between devices impairs data reliability and location determination accuracy, particularly when determining the location of wireless nodes relative to reference points, due to factors like co-location of reference points and spatial arrangement, which can lead to skewing and reduced ranging capacity.

Innovation Solution

A wireless communications node (WCN) is configured to receive reference point coordinates from beacon advertisement messages, cluster these points, determine the most proximate cluster, calculate the estimated true range to these points, and use this information to accurately determine its coordinate location within a given space, thereby mitigating multipath interference and optimizing location determination.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If iterative measurements and recalculation are performed to improve location determination accuracy, then measurement precision improves, but use of energy increases

Engineering Contradiction:

Improvelocation determination accuracyVSAvoidbattery usage

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary clustering of reference points into groups based on their spatial relationships before the actual location determination process. This pre-organization allows the wireless communications node to quickly identify which cluster contains the most proximate reference points without performing exhaustive measurements on all reference points, thereby reducing iterative recalculation and energy consumption while maintaining accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The set of reference points is divided into multiple clusters, each representing a spatial group. Instead of processing all reference points uniformly, the system segments them into manageable clusters and only performs detailed ranging measurements on the cluster most proximate to the estimated location, significantly reducing the number of measurements required and thus energy usage

Inventive Principle:

Principle #1Segmentation

2Measurement precision

If the number of reference points is increased to improve location determination accuracy, then measurement precision improves, but device complexity increases

Engineering Contradiction:

Improvelocation determination accuracyVSAvoidprocessing capacity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

Reference points are segmented into multiple clusters based on their spatial distribution. This segmentation allows the system to manage a large total number of reference points while only actively processing one cluster at a time (the most proximate cluster), thereby maintaining high accuracy with reduced computational complexity at any given moment

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system uses a finite number of reference points arranged in clusters, performing measurements only on the subset of reference points within the most proximate cluster rather than all available reference points. This partial action approach provides sufficient accuracy for location determination without requiring the system to process every possible reference point, thus limiting computational complexity

Inventive Principle:

Principle #16Partial or excessive action

3Quantity of substance

If reference points are co-located to increase ranging capacity, then quantity of reference points effectively used increases, but measurement precision deteriorates due to skewing

Engineering Contradiction:

Improveranging capacityVSAvoidlocation determination accuracy

Core Design Contradiction:

Quantity of substanceVSMeasurement precision

Solution Approach 1:

Co-located reference points are segmented into distinct clusters, with each cluster treated as a separate entity for measurement purposes. The system identifies and selects the most proximate cluster for ranging measurements, thereby utilizing the increased quantity of reference points (through co-location) while avoiding the skewing problem by focusing measurements on a single, well-defined cluster rather than treating all co-located points as a mixed group

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS10887860B1Apparatus and method for optimizing wireless end node location determination via targeted proximity ranging to clusters of other wireless nodes

Publication Date: 2021.01.05 LINK LABS

AI summary

Provided are a wireless communications node (WCN) and a method therefor achieving optimized estimation of coordinate location of the WCN relative to wireless communications with a plurality of reference points (RPs). To do so, the WCN coordinates clustering of such RPs, and determines an estimated coordinate location of the WCN based on one or more RPs of selected ones of clusters each having a centroid thereof that is measured as being most proximate the WCN when compared with unselected clusters.