Reduce GPS data error on Android with Kalman filter and accelerometer

RTK stands for Real Time Kinematics, for those who (like me) wondered.

With some hackery you can get an RTKLIB compatible GPS for as low as $30, but one should expect to pay no less than $200-$400 for a ready made solution with an useable antenna. Costs go up fast from there.

"raw" data from the GPS chipset has already been through a Kalman filter... its how they form a 'fix' based on constant measurement inaccuracies.

Of course the difference here is that the extra accelerometer data is involved. It's been a few years since I last worked on GPS chipsets, but I would be surprised if they didn't now incorporate one for this exact reason.

edit: They do: https://www.broadcom.com/products/wireless/gnss-gps-socs/bcm...

"Broadcom's second-generation sensor integration technology, adding accelerometer, gyroscope, magnetometer and altimeter outputs to the positioning engine"

'almost' equally well is a bit qualitative; one of the main advantages of a kalman filter is that it's predictive and so doesn't suffer from the lag that a simple low-pass filter has. But as you say, a car is not a missile so the degree to which the performance of a LPF is worse than a KF may not be significant.

In the case of sat nav software, the software is at its most important when users aren't moving in a smooth way.

Sure, 99.9% of gps measurements on a sat nav are users going in a straight line - but it's when navigating complex junctions with many turns, traffic circles etc that users most need the sat nav's guidance.

Kalman filters do exactly that. Add in accelereometer and you have a basic INS.

Calculating a position (and time) from the GPS data is basically a case of throwing it in a matrix and calculating the fix with the least squared error, and many receivers do just just. However, many (most?) actually use a Kalman filter, which takes into account the estimated error from the previous fix (and calculates a new estimated error to be taken into account for the next fix). So what you're suggesting is already done inside the receiver.

This is also the ideal place to add in additional data such as from an accelerometer. If you only have the calculated position (and speeds, which are useful for figuring out how far you're actually likely to have moved since the last fix) but not the error estimates to work on then you've thrown away much of the useful data.

Hmm, you don't have to convert the geohash to a (base-32) string - int64 may be just as fine.

Perhaps they can take advantage of the better locality of Z index compared to flat index, and/or they want to store higher precision and do lookups with varying precisions (grid sizes)?

1. It won't be working on stock devices, unfortenately. It's library to integrate to your android application

2. Extra calculations don't consume as much battery as GPS and with this technique we use GPS less often. In other hand we use accelerometer and magnetometer. I tested this by eye :) and didn't find big difference between GPS only solution and presented solution. But as written in article - it doesn't accumulate coordinates.

3. Android already has similar filters. They use Kalman filter and many interesting things. See here : https://android.googlesource.com/platform/frameworks/native/... and other files. They did really huge work here :)

Yes, accelerometer's "drift" became a world of pain for me, but android developers did great work for that and all we need to do is to add calibration step to application and use their "linear acceleration" sensor.

This principle is already sometimes used for determining the position of UAVs (e.g. Quadcopters). So absolute position is provided by GPS and short term movements can be calculated from accelerometers and other sensors.

Of course sensor data can only provide a relative position. After longer intervals of solely relying on the relative sensor-based position small errors can accumulate which makes "synchronization" with GPS necessary to retain accuracy.

The power consumption of computing the kalman filter is negligible compared to the power consumption of the gps module.

I thought that ios and android did this already for a long time.