卡尔曼滤波用于GPS的android [英] Kalman Filter for GPS android
问题描述
要得到GPS更精确的数据，卡尔曼滤波被推荐。但是，我找不到任何教程如何实现卡尔曼滤波的GPS，机器人。
还有很多的东西，你可以通过谷歌和维基百科发现，但有一个基本的直觉可能是有用的。
实质上，卡尔曼滤波器装置反复施加两个步骤的过程：
- 请猜
- 使用的测量来更新猜测
一个卡尔曼滤波器形式化一个简单的想法：当你知道有多快你要去，你可以predict从上次的地理位置报道的GPS位置，然后当一个新的GPS报告是在更新。
我们将在这里谈两个变量：在的意思是，这是你最好的猜测，和您的不确定性，从而重新presents了猜测的准确性。在GPS方面，你会被谈论的GPS位置，误差幅度（如10米）。
随着你每次更新，你增加的不确定性一点，因为你是不是真的那么肯定的速度并没有改变。当一个新的测量进入时，在更新位置和确定性。
均值和不确定度可以重新presented作为贝尔曲线（正态分布），与X轴的变量和它具有在Y轴值的概率：
下面的μ（MU）是平均和σ（SIGMA）的不确定性。任何这样的曲线可以通过这两个值进行说明。
的技巧是，你实际上可以多两架贝尔曲线（您prediction和您的测量），并获得一个新的重新presenting的综合知识，你会做，当你得到一个新的GPS位置在已经做了一些predictions。你会发现算算这个维基百科等网站。
To get a more accurate data from GPS, Kalman filter is being recommended. But I can't find any tutorial how to implement Kalman Filter for GPS, android.
There’s a lot of stuff you can find through Google and Wikipedia, but having a basic intuition could be useful.
Essentially, a Kalman filter means repeatedly applying a two-step process:
- Make a guess
- Use a measurement to update the guess
A Kalman filter formalizes a simple idea: when you know how fast you’re going, you can predict your geolocation from the last reported GPS position, and then update when a new GPS report comes in.
We will talk about two variables here: the mean, which is your best guess, and your uncertainty, which represents the accuracy of that guess. In terms of GPS, you would be talking about the GPS location and the margin of error (e.g. 10 meters).
With every update you do, you increase your uncertainty a bit because you’re not really that sure the velocity hasn’t changed. When a new measurement comes in, you update the position and the certainty.
The mean and uncertainty can be represented as a Bell curve (a normal distribution), with the variable on the X axis and the probability of it having that value on the Y axis:
Here µ (mu) is the mean and σ (sigma) is the uncertainty. Any such curve can be described by these two values.
The trick is that you can actually multiple two bell curves (your prediction and your measurement) and get a new one representing the combined knowledge, which you’d do when you get a new GPS position after having done some predictions. You’ll find the math for this on Wikipedia and other sites.
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