kalman-filter

问题 I try to Stabilize video with a Kalman filter for smoothing . But i have some problems Each time, i have two frames: one current and another one. Here my workflow: Compute goodFeaturesToTrack() Compute Optical Flow using calcOpticalFlowPyrLK() Keep only good points Estimate a rigid transformation Smoothing using Kalman filter Warping of the picture. But i think there is something wrong with Kalman because at the end my video is still not stabilized and it's not smooth at all, it even worse

问题 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. 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. Since my

问题 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. 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. Since my

问题 I have three gyroscope values, pitch, roll and yaw. I would like to add Kalman filter to get more accurate values. I found the opencv library, which implements a Kalman filter, but I can't understand it how is it really work. Could you give me any help which can help me? I didn't find any related topics on the internet. I tried to make it work for one axis. const float A[] = { 1, 1, 0, 1 }; CvKalman* kalman; CvMat* state = NULL; CvMat* measurement; void kalman_filter(float FoE_x, float prev_x

问题 I have been through the concept of 1D Kalman filter, but, they mostly concentrate on the equations formed from Gaussian distributions where they used the equations in the picture Gaussian Distribution equations (they can be found in the following links: Pyata 1D Kalman Filter, 1D Kalman Filter, Sensor Fusion). I have several questions: Question 1: How can I form predict and update states with other distributions? (for example, Bradford distribution) I looked into Bradford distribution and

问题 I am using a Kalman filter to estimate various Dynamic and Arbitrage free Nelson-Siegel models for yield curves. I give some starting values to optim and the algorithm converges just fine. However, when I want to calculate standard errors using the Hessian supplied by the optim algorithm, I get NaN's due to nonpositive values on the diagonal of the Variance covariance matrix. I think it is because I have a highly nonlinear function with many local optima, however it keeps happening for all

问题 I'm looking for a way to generalize regression using pykalman from 1 to N regressors. We will not bother about online regression initially - I just want a toy example to set up the Kalman filter for 2 regressors instead of 1, i.e. Y = c1 * x1 + c2 * x2 + const . For the single regressor case, the following code works. My question is how to change the filter setup so it works for two regressors: import matplotlib.pyplot as plt import numpy as np import pandas as pd from pykalman import

来源： https://stackoverflow.com/questions/25744984/implement-a-kalman-filter-to-smooth-data-from-deviceorientation-api

来源： https://stackoverflow.com/questions/25744984/implement-a-kalman-filter-to-smooth-data-from-deviceorientation-api

来源： https://stackoverflow.com/questions/22598585/how-to-should-i-implement-a-kalman-filter-to-get-accurate-accelerometer-data