The large space ship that brought you here along with many other adventurers is called a TradeStar. You play as the boss of a small gold mining company that has just arrived at a newly discovered Earth-like planet. In this gold rush of galactic proportions, fantastic adventure and brutal competition awaits. Interstellar trade and prosperity reigns, but there is always an ever-expanding frontier to explore and exploit for precious resources. Our analysis concludes that none of these algorithms fully meets these criteria.Cortex Command is set a few hundred years into the future, where the now cybernetic human race is able to travel between stars and has already met several alien civilizations.
These criteria are defined to be their ability to cope with noise variation over time, track association, high variability in track shape, closely separated tracks, multiple tracks, the birth/death of tracks, low signal-to-noise ratios, that they have no a priori assumption of track shape and that they are computationally cheap. This paper presents an extensive survey and an algorithm taxonomy, additionally each algorithm is reviewed according to a set of criteria relating to their success in application. There has not been a review paper which describes and critically analyses the application of these key algorithms. Most of the literature is concentrated in three areas: image processing, neural networks, and statistical models such as the Hidden Markov model. This problem has attracted researchers’ interest from a variety of backgrounds ranging between image processing, signal processing, simulated annealing and Bayesian filtering. Recent advances in technology and the abundance of data requires the development of more sensitive detection methods. The detection of tracks in spectrograms is an important step in remote sensing applications such as the analysis of marine mammal calls and remote sensing data in underwater environments.
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