pykalman | 易学教程

Multiple regression with pykalman?

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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

Using PyKalman on Raw Acceleration Data to Calculate Position

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问题 This is my first question on Stackoverflow, so I apologize if I word it poorly. I am writing code to take raw acceleration data from an IMU and then integrate it to update the position of an object. Currently this code takes a new accelerometer reading every milisecond, and uses that to update the position. My system has a lot of noise, which results in crazy readings due to compounding error, even with the ZUPT scheme I implemented. I know that a Kalman filter is theoretically ideal for this

Kalman filter with varying timesteps

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问题 I have some data that represents the location of an object measured from two different sensors. So, I need to do sensor fusion. The more difficult issue is that the data from each sensor, arrives at essentially a random time. I would like to use pykalman so fuse and smooth the data. How can pykalman handle variable timestamp data? A simplified sample of the data will look like this: import pandas as pd data={'time':\ ['10:00:00.0','10:00:01.0','10:00:05.2','10:00:07.5','10:00:07.5','10:00:12

OpenCV Kalman Filter python

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问题 Can anyone provide me a sample code or some sort of example of Kalman filter implementation in python 2.7 and openCV 2.4.13 I want to implement it in a video to track a person but, I don't have any reference to learn and I couldn't find any python examples. I know Kalman Filter exists in openCV as cv2.KalmanFilter but I have no idea how to use it. Any guidance would be appreciated 回答1: The kalman.py code below is the example included in OpenCV 3.2 source in github. It should be easy to change

Using PyKalman on Raw Acceleration Data to Calculate Position

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This is my first question on Stackoverflow, so I apologize if I word it poorly. I am writing code to take raw acceleration data from an IMU and then integrate it to update the position of an object. Currently this code takes a new accelerometer reading every milisecond, and uses that to update the position. My system has a lot of noise, which results in crazy readings due to compounding error, even with the ZUPT scheme I implemented. I know that a Kalman filter is theoretically ideal for this scenario, and I would like to use the pykalman module instead of building one myself. My first

How to use Kalman filter in Python for location data?

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[EDIT] The answer by @Claudio gives me a really good tip on how to filter out outliers. I do want to start using a Kalman filter on my data though. So I changed the example data below so that it has subtle variation noise which are not so extreme (which I see a lot as well). If anybody else could give me some direction on how to use PyKalman on my data that would be great. [/EDIT] For a robotics project I'm trying to track a kite in the air with a camera. I'm programming in Python and I pasted some noisy location results below (every item also has a datetime object included, but I left them

Kalman filter with varying timesteps

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I have some data that represents the location of an object measured from two different sensors. So, I need to do sensor fusion. The more difficult issue is that the data from each sensor, arrives at essentially a random time. I would like to use pykalman so fuse and smooth the data. How can pykalman handle variable timestamp data? A simplified sample of the data will look like this: import pandas as pd data={'time':\ ['10:00:00.0','10:00:01.0','10:00:05.2','10:00:07.5','10:00:07.5','10:00:12.0','10:00:12.5']\ ,'X':[10,10.1,20.2,25.0,25.1,35.1,35.0],'Y':[20,20.2,41,45,47,75.0,77.2],\ 'Sensor':

How to use Kalman filter in Python for location data?

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问题 [EDIT] The answer by @Claudio gives me a really good tip on how to filter out outliers. I do want to start using a Kalman filter on my data though. So I changed the example data below so that it has subtle variation noise which are not so extreme (which I see a lot as well). If anybody else could give me some direction on how to use PyKalman on my data that would be great. [/EDIT] For a robotics project I'm trying to track a kite in the air with a camera. I'm programming in Python and I