Vehicle Tracking in Extreme Noisy Channel through Kalman Filter
Rahul Sharma
Rahul Sharma, Department of Electronics & Communication Engineering Bharati Vidyapeeth’s College of Engineering, (New Delhi), India.
Manuscript received on November 01, 2014. | Revised Manuscript received on November 07, 2014. | Manuscript published on November 15, 2014. | PP: 3-6 | Volume-2 Issue-12, November 2014. | Retrieval Number: L07421121214/2014©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Kalman Filter is one of the most important discoveries for a signal processing engineer. It uses a system’s dynamics model (i.e., physical laws of motion), known control inputs to that system, and measurements (such as from sensors) to form an estimate of the system’s varying quantities (its state) that is better than the estimate obtained by using any one measurement alone. This paper tries to estimate the correct position of a vehicle in an extreme noisy channel and compares it to the conventional filtering methods like running average etc. The paper presents threshold based technique along with Gaussian filtering to differentiate object from the background and estimates the two dimensional position of the vehicle.
Keywords: Kalman Filter, Object Tracking, Gaussian, Particle Filter, Adaptive filter.