Spectral Response Appliance for Wireless Device Location
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Solution Overview
Problem
Existing wireless device location systems face challenges in accurately determining the location of devices in spaces with limited interior barriers, such as convention halls or warehouses, due to imprecision in received signal strength indication (RSSI) measurements and multipath reflections, which require additional access point sensors to increase accuracy at a high cost.
Innovation Solution
A spectral response appliance that collects frequency-domain data from wireless access point devices to derive location parameters, using frequency-selective fading data to determine the location of a wireless device without the need for additional sensors, by treating spectral features as additional independent sources of input for pattern-matching systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional AP sensors are placed to increase location accuracy, then location accuracy is improved, but system cost increases due to high costs of AP sensors
Solution Approach 1:
The patent introduces spectral response data as an intermediary element that mediates between the existing AP sensors and the location determination process. By analyzing spectral magnitude signatures and frequency-selective fading characteristics of WiFi signals, the system extracts additional location information without requiring more physical sensors. This intermediary spectral analysis layer enables improved location accuracy while avoiding the cost of adding more AP sensors.
Solution Approach 2:
The patent changes the parameter being analyzed from simple RSSI (received signal strength indication) to spectral response characteristics including frequency-domain magnitudes and spectral magnitude signatures. By transforming the analysis from time-domain signal strength to frequency-domain spectral features, the system extracts more information from the same signals, improving location accuracy without additional hardware costs.
2Device complexity
If RSSI-based location systems are used in spaces with limited interior barriers, then system simplicity is maintained, but location precision deteriorates due to multipath reflections and signal attenuation
Solution Approach 1:
The patent transitions from analyzing signals in the time domain (RSSI) to the frequency domain (spectral response). By applying Fast Fourier Transform to convert time-domain WiFi signals into frequency-domain spectral magnitude signatures, the system adds a dimensional transformation that reveals location information hidden in frequency-selective fading patterns, thereby improving precision without complicating the physical system architecture.
Solution Approach 2:
The patent substitutes the mechanical approach of adding more physical sensors with a signal processing approach. Instead of increasing hardware complexity by deploying additional AP sensors, the system replaces the simple RSSI measurement mechanism with spectral analysis processing, using mathematical transformations to extract location information from existing signals.
3Ease of operation
If standard WiFi signals are used for location determination, then ease of operation is maintained, but measurement precision deteriorates due to imprecision in RSSI measurements
Solution Approach 1:
The patent performs preliminary spectral analysis on WiFi signals before using them for location determination. By pre-processing the signals through Fast Fourier Transform to extract spectral magnitude signatures and frequency-selective fading characteristics, the system prepares enhanced location data in advance. This preliminary action transforms ordinary WiFi signals into information-rich spectral profiles that provide precise location measurements while maintaining ease of operation.
Solution Approach 2:
The patent creates a spectral copy or representation of the original WiFi signal in the frequency domain. Instead of directly using the time-domain RSSI measurement, the system generates a spectral magnitude signature that copies and transforms the signal's essential characteristics into a more informative format, preserving the original signal's location information while enhancing measurement precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides more precise and accurate location estimates within spaces with limited barriers, enhancing location determination without the need for additional access points, thereby reducing system costs and improving location accuracy.
Implementation Method 1
using frequency-selective fading data to determine the location of a wireless device
Data Source
AI summary
A non-transitory processor-readable medium storing code representing instructions to be executed by a processor includes code to cause the processor to receive from a wireless access point (WAP) device frequency-domain data associated with signals received at the WAP device from a wireless device during a time period. The code includes code to determine multiple frequency-domain magnitudes associated with the frequency-domain data for the time period to define a spectral magnitude signature associated with the frequency-domain data. Each frequency-domain magnitude from the multiple frequency-domain magnitudes is uniquely associated with a frequency bin from multiple mutually-exclusive frequency bins associated with the frequency domain data. The code also includes code to identify a spectral response deviation associated with the spectral magnitude signature and send a location identifier associated with a location of the wireless device based on the spectral response deviation.