kalman-filter

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

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

问题 I am working on accelerometer from an android phone. I wish to filter the horrible noise the accelerometer is returning recording the phone's moves. I was reading around on Kalman filter, because low pass are just not enough. But I don't have a model of the transition from ACCELERATION(k-1) to ACCELERATION(k) because it is the movements of the user. So I have no state transition matrix (H or F in different papers, the one that multiply Xk-1 in the equation Xk = H Xk-1 + B command+noise) I saw

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 6 years ago . 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. 回答1: GPS Data are already heavily Kalman filtered. This is done inside the GPS receiver. Dont expect an accuracy gain in position (lat / lon) if you create your

问题 I am well aware of the existence of this question but mine will differ. I also know that there could be significant errors with this approach but I want to understand the configuration also theoretically. I have some basic questions which I find hard to answer for myself clearly. There is a lot of information about accelerometers and gyroscopes but I still haven't found an explanation "from first principles" of some basic properties. So I have a plate sensor that contains an accelerometer and

问题 I am well aware of the existence of this question but mine will differ. I also know that there could be significant errors with this approach but I want to understand the configuration also theoretically. I have some basic questions which I find hard to answer for myself clearly. There is a lot of information about accelerometers and gyroscopes but I still haven't found an explanation "from first principles" of some basic properties. So I have a plate sensor that contains an accelerometer and

问题 I am well aware of the existence of this question but mine will differ. I also know that there could be significant errors with this approach but I want to understand the configuration also theoretically. I have some basic questions which I find hard to answer for myself clearly. There is a lot of information about accelerometers and gyroscopes but I still haven't found an explanation "from first principles" of some basic properties. So I have a plate sensor that contains an accelerometer and

问题 I have a video of a road/building and I want to create a 3D model out of it. The scene I am looking at is rigid and the drone is moving. I assume not having any extra info like camera pose, accelerations or GPS position. I would love to find a python implementation that I can adapt to my liking. So far, I have decided to use the OpenCV calcOpticalFlowFarneback() for optical flow, which seems reasonably fast and accurate. With it, I can get the Fundamental Matrix F with findFundamentalMat() .

问题 I am learning kalman filters for the purpose of trajectory prediction. Right now, I am able to track the ball. For my first practical attempt with prediction and kalman filter, I used an example of drawing lines as given here: Is there any example of cv2.KalmanFilter implementation? Here is the complete code: import cv2 import numpy as np import math cap = cv2.VideoCapture('videoplayback (1).mp4') loHue = 0 loSaturation = 50 loValue = 50 high_hue = 0 high_saturation = 255 high_value = 255

问题 I have questions on the behavior I am seeing with applying Kalman Filter (KF) to the following forecast problem. I have included a simple code sample. Goal: I would like to know if KF is suitable for improving forecast/simulation result for a day ahead (at t+24 hours), using the measurement result obtained now (at t). The goal is to get the forecast as close to measurement as possible Assumption: We assume the measurement is perfect (ie. if we can get the forecast matches the measurement