Infrared search and track system is an integral part of modern weaponry. The detection and tracking algorithm forms the heart of an IRST system and their effectiveness plays an important role in determining performance of the system. This report studies various detection and tracking algorithms for multiple point targets in noisy environment resulting in very low signal to noise ratio. Target detection is carried out using spatial-temporal techniques needing multiple frames, since targets are assumed to be irresolvable in a single image frame. The tracking algorithms are classified in the basis of different approaches for data selection and model selection. Data selection and model selection is used for tracking multiple targets in dense clutter environment. An overview of the Interacting Multiple Model Expectation Maximization algorithm, and brief description of Multiple Hypothesis Tracking and Joint Probabilistic Data Association Filter algorithm is also presented.
The ever-increasing effectiveness of electronic warfare and the advent of anti-radiation missiles create the need for covert, radar silent operations. Therefore systems are required which are virtually immune to jamming, undetectable and yet capable of detecting targets at reasonable ranges. The infrared search and track system (IRST) is the most suitable choice for such scenario. IRST is a system that surveys environment by analyzing the infrared radiation, emitted by the targets compared to background. The major work of the IRST is detection and tracking of target. In a practical scenario target are not in ideal conditions, they are generally surrounded by clutter. Hence so detection and tracking is much more complication in practical scenario.
The developments of efficient clutter rejection algorithms are important to detect the targets in the modern IRST systems. In low signal to noise (SNR) situation, the target could not possibly be localized on the single frame so the successive frames are used to detect the target. If the alignment is done properly the signals of the various images would add up and a signal with suffiently large SNR are achieved, while the noise will be cancel out. This approach of detection of dim target is usually referred as target before detection (TBD) and generally used for detection of dim targets.
