kalman-filter

问题 I am trying to develop motion detection application for Android. App should be able to track motion of phone in space and map it to motion on computer screen. I am using 3-axis accelerometer and since data is very noisy I am using Kalman filter. Internal state is 6 component vector [speed-x, speed-y, speed-z, accel-x, accel-y, accel-z] and measured state is 3 component vector [accel-x, accel-y, accel-z]. Filter works very well on measured values, but speed is still very noisy. Now I am

问题 I am new on OpenCV. I am always wish to learn new image processing technologies / Programming. I have seen few tutorial on Object detection, tracking, counting etc. I wish to learn the same and try to make my own similar project. With lot of searching on internet and papers. Finally i came to know about Kalman Filter for object tracking. I have used following codes as per following: Background Subtract Smoothing , Blur etc. filters. Find Contours Draw Rectangle and find Centroid. Apply Kalman

问题 I am new on OpenCV. I am always wish to learn new image processing technologies / Programming. I have seen few tutorial on Object detection, tracking, counting etc. I wish to learn the same and try to make my own similar project. With lot of searching on internet and papers. Finally i came to know about Kalman Filter for object tracking. I have used following codes as per following: Background Subtract Smoothing , Blur etc. filters. Find Contours Draw Rectangle and find Centroid. Apply Kalman

问题 I read some works about Kalman filter for CV object tracking but I can't find some reference about the choice of: 1)the process noise covariance Q; 2)Measurement noise covariance R. So far I have realized that the model is equation of motion (someone uses acceleration as state variable, others use position and speed only) but nobody is clear about Q and R choice including this example by mathworks: http://www.mathworks.it/it/help/vision/examples/using-kalman-filter-for-object-tracking.html

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

问题 I'm writing an application and my aim is to detect when a user is walking. I'm using a Kalman filter like this: float kFilteringFactor=0.6f; gravity[0] = (accelerometer_values[0] * kFilteringFactor) + (gravity[0] * (1.0f - kFilteringFactor)); gravity[1] = (accelerometer_values[1] * kFilteringFactor) + (gravity[1] * (1.0f - kFilteringFactor)); gravity[2] = (accelerometer_values[2] * kFilteringFactor) + (gravity[2] * (1.0f - kFilteringFactor)); linear_acceleration[0] = (accelerometer_values[0]

问题 I'm writing an application and my aim is to detect when a user is walking. I'm using a Kalman filter like this: float kFilteringFactor=0.6f; gravity[0] = (accelerometer_values[0] * kFilteringFactor) + (gravity[0] * (1.0f - kFilteringFactor)); gravity[1] = (accelerometer_values[1] * kFilteringFactor) + (gravity[1] * (1.0f - kFilteringFactor)); gravity[2] = (accelerometer_values[2] * kFilteringFactor) + (gravity[2] * (1.0f - kFilteringFactor)); linear_acceleration[0] = (accelerometer_values[0]

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

问题 I am following a probably well-known tutorial about Kalman filter. From these lines of code: figure; plot(t,pos, t,posmeas, t,poshat); grid; xlabel('Time (sec)'); ylabel('Position (feet)'); title('Figure 1 - Vehicle Position (True, Measured, and Estimated)') I understand that x is the true position, y is measured position, xhat is estimated position. Then, if we can compute x (this code: x = a * x + b * u + ProcessNoise; ), why do we need to estimated x anymore? 回答1: ... OK, after a second

问题 I am trying to implement Kalman filter. I only know the positions. The measurements are missing at some time steps. This is how I define my matrices: Process noise matrix Q = np.diag([0.001, 0.001] ) Measurement noise matrix R = np.diag([10, 10]) Covariance matrix P = np.diag([0.001, 0.001]) Observation matirx H = np.array([[1.0, 0.0], [0.0, 1.0]]) Transition matrix F = np.array([[1, 0], [0, 1]]) state x = np.array([pos[0], [pos[1]]) I dont know if it is right. For instance, if I see target