Bluetooth LE RSSI for proximity detection iOS

问题:

I'll start with the question.

Is the BTLE RSSI a good way to indicate two devices proximity to each other or not? does it only work with small devices like fobs etc?

The Issue:

I am currently looking at making an app that will use BTLE and allow connections based on proximity. In this regard it is much like the demo app that apple show in the Advanced Core Bluetooth keynote (When two devices are almost touching they then connect).

As I understand it the proximity is determined based on the RSSI value when the central discovers the peripheral. When I try this however with two iPads the signal seems too strong for this it is also too inconsistent to have an accurate stab at the proximity as it doesn't show very much correlation to the devices proximity.

I have tried the Apple sample code and that is similar in that the devices don't have to be close at all for the information to pass from one to another.

If only there was a way to reduce the signal strength of the peripheral devices advertisement....

Thanks in advance for any help.

回答1:

The experience of Matthew Griffin matches mine. However - when we can measure for a fair period of time two things have helped us calibrate this better.

We did have to wrap a simple (kalman) filter on the antenna orientation and the IMU to get a rough running commentary though - and this is not very CPU or battery light.

• Using the IMU you get a fair idea of the distance/direction of travel - and if this is over a short period of time - we assume the other 'side' is stationary. This helps a lot to get a value for 'current' orientation and 'callibrate current environment noise.
• Likewise - do the same for rotations/position changes.

We've found that in general a re-orientation of the device is a better way to get direction; and that distance is only reliable some up to some 30 to 600 seconds after a 'move' calibration' and only if the device is not too much rotated. And in practice once needs some 4-5 'other' devices; ideally not too mobile, to keep oneself dynamically calibrated.

However the converse is quite reliable - i.e. we know when not to measure. And the net result is that one can fairly well ascertain things like 'at the keyboard' and 'relocated'/moved away through a specific door/openning or direction. Likewise measuring a field by randomly dancing through the room; changing orientation a lot - does work well once the receiver antenna lobes got somewhat worked out after a stationary period.

回答2:

You are entirely correct about RSSI jumping wildly and randomly. You should retrieve your RSSI values every two seconds (any faster and you get a bunch of errors). Throw out the RSSI values that are more than a ~-40 decibel spike and use an aggregate of the past values before you declare your approximate range to the user.

As for your following statement, you are in luck.

If only there was a way to reduce the signal strength of the peripheral devices advertisement....

The service you are looking for is called the TX Power Service. Implementing this service on your peripheral will allow you to decrease the transmit power of the device. That way you can throttle down the range that the advertisement data is visible from. We unfortunately do not however, have access to this service on an iOS device. But if you are writing your own firmware for a BLE peripheral, this is the service you want.

回答3:

I've spent the last week dealing strictly with RSSI, trying to use Wifi and Bluetooth LE sensors for location triangulation and for distance conversion.

Unfortunately, what I have found is that RSSI is just too finicky and unreliable to consistently use to determine distance. In theory, the RSSI and distance behave according to the inverse square law (double the distance, and the RSSI will go down a fixed number of decibels), but in practice the RSSI is affected by uncontrollable factors such as weather (dry weather allows RF fields to travel better) and obstacles (any metal objects or humans in the path from one sensor to another will cause attenuation, and any metal objects closely positioned by one of the sensors will cause gain in the signal strength).

There are ways to try to compensate for this. This paper is one of the best papers I read on how to get accurate results, but the bottom line is that is an unreliable method unless you just want a general idea of where the device is.

回答4:

If I understand well, you are trying to implement similar functionality that to what seen in the WWDC demo and what apps like Bump implement. For that RSSI will be adequate. Test for appopriate threshold values (e.g. >-30) and you will be fine.