Vehicle tracking using kalman filter and

Open source software and crowd sourced data for operational performance analysis Author: To date the access to operational data in support of research claims or observed performance benefits is limited. Though the majority of related data sets are established and curated by government authorities, open data access is still in its infancy. ICAO promotes a performance based approach.

Vehicle tracking using kalman filter and

Bucy of the University of Southern California contributed to the theory, leading to it sometimes being called the Kalman—Bucy filter. Schmidt is generally credited with developing the first implementation of a Kalman filter. He realized that the filter could be divided into two distinct parts, with one part for time periods between sensor outputs and another part for incorporating measurements.

This Kalman filter was first described and partially developed in technical papers by SwerlingKalman and Kalman and Bucy The Apollo computer used 2k of magnetic core RAM and 36k wire rope [ Clock speed was under kHz [ The fact that the MIT engineers were able to pack such good software one of the very first applications of the Kalman filter into such a tiny computer is truly remarkable.

Navy nuclear ballistic missile submarinesand in the guidance and navigation systems of cruise missiles such as the U. Navy's Tomahawk missile and the U. They are also used in the guidance and navigation systems of reusable launch vehicles and the attitude control and navigation systems of spacecraft which dock at the International Space Station.

As such, it is a common sensor fusion and data fusion algorithm. Noisy sensor data, approximations in the equations that describe the system evolution, and external factors that are not accounted for all place limits on how well it is possible to determine the system's state.

The Kalman filter deals effectively with the uncertainty due to noisy sensor data and to some extent also with random external factors. The Kalman filter produces an estimate of the state of the system as an average of the system's predicted state and of the new measurement using a weighted average.

The purpose of the weights is that values with better i. The weights are calculated from the covariancea measure of the estimated uncertainty of the prediction of the system's state. The result of the weighted average is a new state estimate that lies between the predicted and measured state, and has a better estimated uncertainty than either alone.

This process is repeated at every time step, with the new estimate and its covariance informing the prediction used in the following iteration. This means that the Kalman filter works recursively and requires only the last "best guess", rather than the entire history, of a system's state to calculate a new state.

The relative certainty of the measurements and current state estimate is an important consideration, and it is common to discuss the response of the filter in terms of the Kalman filter's gain.

The Kalman gain is the relative weight given to the measurements and current state estimate, and can be "tuned" to achieve particular performance. With a high gain, the filter places more weight on the most recent measurements, and thus follows them more responsively.

With a low gain, the filter follows the model predictions more closely. At the extremes, a high gain close to one will result in a more jumpy estimated trajectory, while low gain close to zero will smooth out noise but decrease the responsiveness. When performing the actual calculations for the filter as discussed belowthe state estimate and covariances are coded into matrices to handle the multiple dimensions involved in a single set of calculations.

This allows for a representation of linear relationships between different state variables such as position, velocity, and acceleration in any of the transition models or covariances.

Example application[ edit ] As an example application, consider the problem of determining the precise location of a truck. The truck can be equipped with a GPS unit that provides an estimate of the position within a few meters.

The GPS estimate is likely to be noisy; readings 'jump around' rapidly, though remaining within a few meters of the real position.

In addition, since the truck is expected to follow the laws of physics, its position can also be estimated by integrating its velocity over time, determined by keeping track of wheel revolutions and the angle of the steering wheel.

This is a technique known as dead reckoning.Nanodegree Program Become a Self Driving Car Engineer. Self-driving cars are set to revolutionize the way we live. This is transformational technology, on the cutting-edge of robotics, machine learning, software engineering, and mechanical engineering.

1 Vehicle Tracking based on Kalman Filter Algorithm Tuan Le, Meagan Combs, and Dr. Qing Yang (Computer Science Department at Montana State University). IntroductionDiminishing herbicide options, fear of ground water contamination, and customer pressure to minimise herbicide use, are all pushing the field vegetable and .

ASME Biennial Stability and Damped Critical Speeds of a Flexible Rotor in Fluid-Film Bearings J. W. Lund 1 ASME Biennial Experimental Verification of Torquewhirl-the Destabilizing Influence of Tangential Torque J. M. Vance and K.

Vehicle tracking using kalman filter and

B. Yim Color Based Vehicle Detection and Tracking using Kalman Filter tracks, as and when new detections and predictions of existing tracks are made.


Basic motion detection and tracking with Python and OpenCV - PyImageSearch