Particle filter for Pedestrian Dead Reckoning

In summary, the conversation is about using Particle filtering in Matlab to improve accuracy in calculating total distance traveled using a mobile accelerometer sensor. The participants are discussing how to implement particle filter in Matlab and integrate it with map data. They also mention using acceleration and orientation readings as measurements and ask for suggestions on developing a motion model. One participant mentions working on a similar project, but the thread is old and they inquire about any progress made.
  • #1
ravishah
5
0
Hi,
I am working on Pedestrian dead Reckoning using Particle filtering in matlab, I am using mobile accelrometer sensor to detect number of steps and stride length and calculating the total distance travelled. I want to use particle filter to enhance accuracy in my results. I have read the theory of particle filter but stuck at the point that how to implement it in matlab, How I can develop motion model, ,I have accelerometer and orientation reading as measurement. The output can be position of the object, anyody have some idea. And how to integrate it with map.
regards
ravi
 
Engineering news on Phys.org
  • #2
i am also doing work in this area, but this thread is old: are you still working on this? did you make any progress?
 

1. What is a Particle Filter?

A Particle Filter is a type of probabilistic algorithm used in statistics and machine learning to estimate the state of a system by combining multiple uncertain measurements. It is commonly used in applications such as navigation and tracking, including Pedestrian Dead Reckoning.

2. How does a Particle Filter work for Pedestrian Dead Reckoning?

In Pedestrian Dead Reckoning, a Particle Filter uses a set of particles to estimate the position and orientation of a pedestrian based on sensor measurements, such as step counts and direction changes. The particles represent possible states of the pedestrian's movements, and the filter uses Bayesian inference to update and refine these particles as new measurements are obtained.

3. What are the advantages of using a Particle Filter for Pedestrian Dead Reckoning?

Particle Filters are robust to non-linear and non-Gaussian systems, making them well-suited for Pedestrian Dead Reckoning, which involves complex and uncertain human movements. They also allow for real-time updates and can handle multiple sources of sensor data, making them a versatile choice for navigation and tracking applications.

4. What are the limitations of using a Particle Filter for Pedestrian Dead Reckoning?

One limitation of Particle Filters is that they can be computationally expensive, especially when dealing with large numbers of particles and complex systems. Additionally, they are sensitive to the initial distribution of particles, and errors in the sensor measurements can lead to inaccurate estimates.

5. How can the performance of a Particle Filter for Pedestrian Dead Reckoning be evaluated?

The performance of a Particle Filter for Pedestrian Dead Reckoning can be evaluated through metrics such as mean error, root mean square error, and the percentage of time that the estimated position is within a certain distance of the true position. It can also be compared to other navigation and tracking methods to determine its effectiveness in a specific application.

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