View source for Qualcomm incorporated (20240133995). POSITIONING MODEL TRAINING BASED ON RADIO FREQUENCY FINGERPRINT POSITIONING (RFFP) MEASUREMENTS CORRESPONDING TO POSITION DISPLACEMENTS simplified abstract

=POSITIONING MODEL TRAINING BASED ON RADIO FREQUENCY FINGERPRINT POSITIONING (RFFP) MEASUREMENTS CORRESPONDING TO POSITION DISPLACEMENTS=


==Organization Name==

[[:Category:qualcomm incorporated|qualcomm incorporated]]


[[Category:qualcomm incorporated]]

==Inventor(s)==

[[:Category:Mohammed Ali Mohammed Hirzallah of San Diego CA (US)|Mohammed Ali Mohammed Hirzallah of San Diego CA (US)]][[Category:Mohammed Ali Mohammed Hirzallah of San Diego CA (US)]]

[[:Category:Srinivas Yerramalli of San Diego CA (US)|Srinivas Yerramalli of San Diego CA (US)]][[Category:Srinivas Yerramalli of San Diego CA (US)]]

[[:Category:Yun Chen of San Diego CA (US)|Yun Chen of San Diego CA (US)]][[Category:Yun Chen of San Diego CA (US)]]

[[:Category:Rajat Prakash of San Diego CA (US)|Rajat Prakash of San Diego CA (US)]][[Category:Rajat Prakash of San Diego CA (US)]]

[[:Category:Taesang Yoo of San Diego CA (US)|Taesang Yoo of San Diego CA (US)]][[Category:Taesang Yoo of San Diego CA (US)]]

==POSITIONING MODEL TRAINING BASED ON RADIO FREQUENCY FINGERPRINT POSITIONING (RFFP) MEASUREMENTS CORRESPONDING TO POSITION DISPLACEMENTS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240133995 titled 'POSITIONING MODEL TRAINING BASED ON RADIO FREQUENCY FINGERPRINT POSITIONING (RFFP) MEASUREMENTS CORRESPONDING TO POSITION DISPLACEMENTS

==Simplified Explanation==

The abstract describes a method for training a positioning model based on radio frequency fingerprint positioning measurements to estimate the position of a user equipment.

* Obtaining a first plurality of radio frequency fingerprint positioning measurements corresponding to known displacements of a user equipment.
* Training a positioning model using the obtained measurements and known displacements to estimate the position of the user equipment.

== Potential Applications ==
This technology can be applied in:
* Indoor navigation systems
* Asset tracking in warehouses
* Location-based services in smart buildings

== Problems Solved ==
This technology solves the following problems:
* Improving accuracy of indoor positioning systems
* Enhancing location-based services for users
* Optimizing asset tracking processes

== Benefits ==
The benefits of this technology include:
* Increased accuracy in estimating the position of user equipment
* Enhanced user experience in indoor navigation
* Improved efficiency in asset tracking operations

== Potential Commercial Applications ==
Commercial applications of this technology include:
* Integration into mobile applications for indoor navigation
* Implementation in smart home devices for location-based services
* Adoption in logistics companies for asset tracking solutions

== Possible Prior Art ==
One possible prior art for this technology could be the use of Wi-Fi fingerprinting for indoor positioning systems. This method involves collecting Wi-Fi signal strength data to estimate the position of a device indoors.

== Unanswered Questions ==

=== How does this technology compare to other indoor positioning methods? ===
Answer: This article does not provide a direct comparison with other indoor positioning methods, such as Wi-Fi fingerprinting or Bluetooth-based positioning systems.

=== What are the limitations of this technology in real-world scenarios? ===
Answer: The article does not address potential limitations of this technology, such as signal interference in crowded environments or the impact of building structures on radio frequency signals.


==Original Abstract Submitted==

in an aspect, a method performed by a network node includes obtaining a first plurality of radio frequency fingerprint positioning (rffp) measurements corresponding to a plurality of known displacements between positions of a user equipment (ue); and training a positioning model to provide a position estimate of the ue, wherein the training of the positioning model is at least based on the first plurality of rffp measurements and the known displacements between the positions of the ue corresponding to the first plurality of rffp measurements.



[[Category:G01S5/02]]