|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
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|Mad Location Manager is a library for GPS and Accelerometer data "fusion" with Kalman filter|
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|Kalman Filter Example||42||6 years ago||1||Go|
This is library for GPS and Accelerometer data "fusion" with Kalman filter. All code is written in Java. It helps to increase position accuracy and GPS distance calculation on Android devices for the driver's and couriers' apps. And also, it may be used for precise tracking in on-demand services.
Project consists of 2 parts:
This module helps to increase GPS coordinates accuracy and also:
Use last version from link below (jitpack):
There is example application in current repository called "Sensor Data Collector".
Right now these sensors should be available:
It's possible to use just TYPE_ACCELEROMETER with high-pass filter.
Also it's possible to use Madgwick filter instead of rotation vector, but gyroscope and magnetometer sensors should be available in that case.
This is main class. It implements data collecting and processing. You need to make several preparation steps for using it:
<service android:name="mad.location.manager.lib.Services.KalmanLocationService" android:enabled="true" android:exported="false" android:stopWithTask="false" />
It's recommended to use start(), stop() and reset() methods, because this service has internal state. Use start() method at the beginning of new route. Stop service when your application doesn't use locations data. That need to be done for decrease battery consumption.
There are several settings for KalmanFilter. All of them stored in KalmanLocationService.Settings class.
There is an example in MainActivity class how to use logger and settings.
There are 2 ways of using GeoHash real-time filter :
It will calculate distance in 2 ways : Vincenty and haversine formula . Both of them show good results so maybe we will add some flag for choose.
Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe.
You can get more details about the filter here.
The filter is a de-facto standard solution in navigation systems. The project simply defines the given data and implements some math.
The project uses 2 data sources: GPS and accelerometer. GPS coordinates are not very accurate, but each of them doesn't depend on previous values. So, there is no accumulation error in this case. On the other hand, the accelerometer has very accurate readings, but it accumulates error related to noise and integration error. Therefore, it is necessary to "fuse" these two sources. Kalman is the best solution here.
So first - we need to define matrices and do some math with them. And second - we need to get real acceleration (not in device orientation).
First one is described in current project's wiki. But second one is little bit more complex thing called "sensor fusion". There is a lot information about this in internet.
Sensor fusion is a term that covers a number of methods and algorithms, including:
For real acceleration we need to know 2 things: "linear acceleration" and device orientation. Linear acceleration is acceleration along each device axis excluding force of gravity. It could be calculated by high pass filter or with more complex algorithms. Device orientation could be calculated in many ways:
Best results show Madgwick filter and ROTATION_VECTOR sensor, but Madgwick filter should be used when we know sensor frequency. Android doesn't provide such information. We can set minimum frequency, but it could be much higher then specified. Also we need to provide gain coefficient for each device. So best solution here is to use virtual ROTATION_VECTOR sensor. You can get more details from current project's wiki.
Feel free to send pull requests. Also feel free to create issues.
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