适用于iOS中RSSI的卡尔曼滤波器 [英] Kalman filter for RSSI in iOS

问题描述

我制作了一个iOS应用来定位信标.我注意到信标的RSSI值随时间随机波动.为了获得平滑的RSSI值，我尝试使用卡尔曼滤波器.

I've made an iOS App to range the beacons. I've noticed that the RSSI value from beacons is fluctuating randomly with time. In order to get smooth RSSI value, I am trying to use Kalman filter.

在卡尔曼滤波器方程中如此处所述，可以通过测量一系列RSSI值的方差来计算测量噪声(R)，并且可以认为过程噪声(Q)可忽略不计.但是，我无法弄清楚方程中误差方差(P)的估计的确切概念.

In Kalman filter equations as described here, the measurement noise (R) can be calculated by measuring variance from series of RSSI values and the process noise (Q) can be assumed as negligible. However, I couldn't figure out exact idea about the estimate of error variance (P) in the equation.

由于我的实际测量数据是一系列RSSI值，我应该如何实现卡尔曼滤波器?

Since my actual measurement data is the series of RSSI values, how am I supposed to implement Kalman filter?

推荐答案

基本上，误差方差(P)的估计取决于其自身的过去值和过程噪声(Q).由于过程噪声(Q)可以忽略不计或很小的值(0.00001)，并且P不依赖于实际测量，因此最终它变为固定值.此外，如果您的系统是可移动的，则可以将一系列RSSI值和移动作为输入变量.

Basically the estimate of error variance(P) depends on it's own past value and the process noise(Q). Since the process noise(Q) is negligible or very small value (0.00001) and P doesn't depend on actual measurement, eventually it becomes a fixed value. Moreover if your system is mobile, you can take series of RSSI values and your movement as input variables.

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