BLE Proximity Detection Using Signal Count and RSSI
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Solution Overview
Problem
Existing methods for determining the proximity of peripheral Bluetooth low energy (BLE) devices to a host device are inaccurate due to noise from the surrounding environment, which affects the reliability of received signal strength indicator (RSSI) measurements.
Innovation Solution
A system and method that involves receiving BLE signals from multiple peripheral devices, determining a count and threshold value for each device over a specified time interval, and calculating proximity based on the comparison of these values, utilizing both the frequency and RSSI of BLE advertising signals to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RSSI-based proximity determination is used, then the host device can estimate distance to peripheral devices, but environmental noise causes inaccurate measurements
Solution Approach 1:
The patent combines multiple measurement approaches (signal count frequency and RSSI values) into a unified proximity determination system. By merging these complementary methods, the system achieves more reliable proximity detection in noisy environments than either method could provide alone.
Solution Approach 2:
The system continuously monitors BLE signal characteristics over time and uses this feedback to dynamically adjust proximity determinations. By analyzing trends in signal count and RSSI values across multiple measurement cycles, the system compensates for environmental noise and improves measurement reliability.
2Measurement precision
If signal count frequency is used for proximity determination, then accuracy improves in noisy environments, but the system complexity increases
Solution Approach 1:
The host device's existing BLE communication infrastructure is used to serve multiple functions: both data communication and proximity determination. The same BLE signal reception mechanism that enables device communication also provides the signal count and RSSI data needed for proximity measurement, eliminating the need for separate dedicated hardware.
Solution Approach 2:
The system uses the peripheral devices' own advertising signals to provide proximity information. The peripheral devices continuously broadcast their presence through BLE advertising packets, and the host device leverages these self-generated signals from the peripherals to determine proximity without requiring additional active components from the peripheral side.
3Measurement precision
If multiple BLE signals are monitored over time, then proximity determination becomes more accurate, but the time required for determination increases
Solution Approach 1:
The system performs preliminary measurements of BLE signal characteristics during normal device operation before formal proximity determination is needed. By continuously monitoring and storing signal count and RSSI data in the background, the system prepares proximity information in advance, so when proximity determination is required, the data is already available or nearly available.
Solution Approach 2:
The system uses a sliding window approach where only the most recent N signal measurements are considered for current proximity determination. This partial action approach uses sufficient historical data to improve accuracy over single measurements, but limits the time span analyzed to maintain responsive proximity updates.
Data Source
AI summary
Systems, devices, and methods are disclosed. A device includes one or more processors and one or more non-transitory memory modules storing machine-readable instructions that, when executed, cause the one or more processors to receive a plurality of Bluetooth low energy (BLE) signals from a plurality of peripheral devices, and, for each of the plurality of peripheral devices, determine a count of the BLE signals received within a period of time. When executed, the machine-readable instructions cause the one or more processors to determine a threshold count value based on the count of each of the plurality of peripheral devices, and determine a proximity of the plurality of peripheral devices with respect to the device based on a comparison of the count of each of the plurality of peripheral devices and the threshold count value.