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I'll tell you some knowledge shear about KALMAN FILTERS
These things all about Self-Driving Cars ðŸš¨ðŸš¨

Kalman filtering, also known as linear quadratic estimation (LQE)

about Kalman Filter for Computer Vision:- estimates the underlying states based on Noisy data. Kalman filter tries to filter out the noise. The applicability of the Kalman filter is very vast.


Kalman Filters types:-
1) linear Kalman Filter (KF)
2) Extended Kalman Filter (EKF)
3) unscented Kalman Filter (UKF)

1) Linear Kalman Filter :-
-Assumes Linear Discrete or Continuous System Dynamics
-Uses Linear Covariance Prediction/Update Equations
-Does not work with non-linear systems

2) Extended Kalman Filter (EKF) :-
-Assumes Non-Linear Discrete or Continuous System Dynamics
-Uses a Linear Covariance Prediction/Update Equation, but the linearrelationship is approximated from the non-linear dynamics
-Works well with non-linear systems that are smooth and the estimated state is close to the true state




3) unscented Kalman Filter (UKF):-
-Assumes Non-Linear Discrete or Continuous System Dynamics
-Uses the non-linear system to calculate the Prediction/Update Equation, making it a better approximation than the EKR (Rather than linearizing the system dynamics, it calculates a linear approximation of the uncertainty coverences).
-Works on non-linear systems that are slightly more non-linear then what the EKF can handle, since it is better at approximating the non-linear systems.

It's uses to sensor fusion and computers Vision tracking and localization...

A few examples?

• Your LiDAR sensor reports that it sees a pedestrian 12.8 meters ahead, but your Computer Vision system reports 11.9 m, and your RADAR says 13.4 m. This is a Sensor Fusion problem, and it's solved using a Kalman Filter.

• Your object tracker reports a pedestrian running at 8 km/h, but then sees sudden changes in the position, affecting the speed to 7 and then 12 km/h. How do you know the exact speed? This is a tracking problem, and it's also solved using a Kalman Filter.


A Kalman FilterKalman Filter is the solution that is better than averaging (that causes too many errors), and it's also considering the strengths and weaknesses of each sensor.

In Robotics, Kalman Filters are a Swiss Knife you want to use every time you need to estimate the state of something (a speed, position, ...)


As an example, In sensor fusion, Kalman Filters are used because you have data coming from different sensors with different errors and you need to make one unique estimation

Particle FIlters can be used in order to solve non-gaussian noises problems, but are generally more computationally expensive than Kalman Filters. -- use localization 


Filtering is the process of removing unwanted features of data to extract information which can be used
Kalman Filtering: Can Kalman filter be used for Machine Learning or prediction problems?
Ans:- Yes it definitely can be used for regression problems. I think the most common applications are e.g., stock market analysis (or other types of forecasting) where you are dealing with a lot of noise and are interested in on-line regression over different time steps

It's really awesome drawing to explain Kalman Filters:- Link of blog Link2

short note process explain:






python library also Available you can see---Link

Uses of Kalman filter:-

  • Control systems: Kalman filters are used in control systems to estimate the state of a system and predict its future behavior.

  • Robotics: Kalman filters are used in navigation systems for robots to estimate the position and orientation of the robot in real-time.

  • Signal processing: Kalman filters are used in signal processing to estimate the parameters of a signal, such as its frequency and amplitude.

  • Economics: Kalman filters are used in econometrics to estimate the state of an economy, such as the level of inflation and GDP.

  • Aerospace: Kalman filters are used in aerospace applications, such as navigation and control of aircraft and satellites.

  • Finance: Kalman filters are used in finance for portfolio optimization, risk management and algorithmic trading.

  • Self-driving cars : Kalman filters are used in self-driving cars to fuse the data from multiple sensors like lidar, radar, cameras, etc to estimate the position and velocity of the car.

  • Medical: Kalman filters are used in medical imaging to estimate the motion of internal organs and improve the quality of images.
Kalman filters have some limitations and disadvantages that include:

  • Linearity assumption: Kalman filters assume that the system being modeled is linear and Gaussian, which may not always be the case in real-world applications.

  • Modeling complexity: Kalman filters require a good understanding of the system being modeled, as well as accurate models of the system's dynamics and noise.

  • Computational complexity: Kalman filters can be computationally expensive, especially for large and complex systems.

  • Initial conditions: The filter's performance is highly dependent on the initial conditions, if the initial state is not accurate, the filter may converge to a poor solution.

  • Limited in non-linear systems: Kalman filters are not well-suited for nonlinear systems, and extended Kalman filters(EKF) are used in these cases but still have limitations.

  • Limited in non-gaussian systems: Kalman filters assume that the noise is Gaussian, which is not always the case in real-world systems.

  • Limited in systems with multiple-model: Kalman filters are not well-suited for systems that have multiple models with different behaviors and uncertain switching points.

LAST WORDS:-
One thing to keep in the MIND Ai and self-driving Car technologies are very vast...! Don't compare yourself to others, You can keep learning..........

Competition And Innovation Are Always happening...!
so you should get really Comfortable with change...

So keep slowly Learning step by step and implement, be motivated and persistent



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