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Recent Blog Posts

by Carol Politi TRX NEON sensor fusion and mapping algorithms augment GPS to provide both 2D and 3D information for...Read More
by Carol Politi TRX's fourth US indoor location patent recently issued ( Patent US 8751151 ).  (TRX has additional...Read More

NEON Sensor Fusion and Mapping Technology

Delivering accurate location for people moving about in GPS-denied areas – such as when indoors and underground – has not been realistic to date without the use of costly infrastructure or manual mapping of facilities.  Delivering seamless and ubiquitous indoor location is difficult, given that GPS is not available, compasses are highly inaccurate, floor plans may not be available, and relying upon accurately mapped infrastructure may not be a viable option.  The TRX NEON solution implements sensor fusion and mapping to deliver accurate 2D and 3D navigation maps, enabling location where GPS is unavailable, without relying upon pre-installed or networked infrastructure. Core to the TRX technology is the ability to dynamically produce distinct feature maps of indoor spaces, enabling fusion of information from multiple devices to populate maps that include building structural features (e.g., elevators, stairwells, hallways), magnetic features, RF features, and transitions.

NEON Sensor Fusion and Dynamic Mapping for Location 

NEON implements a suite of patented sensor fusion and dynamic mapping algorithms, which intelligently correlate information from a broad range of sensors (magnetic, accelerometer, gyroscope, light, pressure, RF, etc.).

NEON Indoor Location Algorithms

NEON sensor fusion has been designed to specifically support pedestrian navigation applications.  The intelligence of sensor fusion portion of the NEON Navigation Engine lies in part in its ability to isolate and select the sparse areas in which a degraded sensor’s (e.g., compass, GPS) estimates are accurate, and eliminate the rest. This distinguishes NEON from filters and systems that severely drift away from the truth in the presence of a majority of incorrect predictions from a sensor. 

NEON uses received sensor information to infer site maps (including structural, magnetic, and RF features, and other landmarks) dynamically as people move about an area or building.  Information from multiple people moving about in the same building is merged to deliver crowd-sourced enhancements, producing accurate navigation maps that enhance location estimates.  Ranging information (e.g., from known beacons), if available, can also be used to constrain location results. Such ranging Information includes ranging between people operating close together and between a person and a fixed RF node if infrastructure is available. 

Given known maps, feature or floor plan information, accuracy will be further increased as NEON will match location estimates and inferred maps and features to known floor plans and maps, and implement wall avoidance. User and third party constraints can also be accepted through the NEON API.