Deghosting Methods for Track-Before-Detect Multitarget Multisensor Algorithms

Abstract

Track-Before-Detect (TBD) algorithms are very powerful for tracking applications. In comparison to classical (Detect-Before-Track) algorithms they are computationally demanding but allow achieving incredible SNR (Signal-to-Noise Ratio) performance. For classical systems SNR should be greater then one. If this condition is fulfilled classical tracking algorithms does not need a lot of computations and they process acquired data by filtering, detection and estimation algorithms. Typical detection algorithms based on fixed or adaptive threshold fails for SNR<1 because if signal is below noise floor a lot of false measurements occurs or target can not be detected correctly. Improving performance for low SNR systems is very important from applications point of view and it is research very active area using alternative approaches and improved algorithms. Track-Before-Detect algorithms are excellent alternative for low SNR signals because signal (target) detection is processed after intensive testing set of hypotheses related to possible signal states (e.g. object trajectories). Even if there are no any signal from target complete search is used for best performance. Huge discrete state-space needs a lot of computations mostly not related to real state of target. Today available computing devices like fast processors, specialized VLSI circuits and distributed computing methods allows gives a possibility of using real-time TBD algorithms for dim target tracking. It is worth to be noted that computation cost for TBD algorithms is serious disadvantage because it significantly influent on financial cost of system but it can be meaningful for military applications (air, naval or space surveillance) where plane, ship or political costs are much more significant. There are two groups of TBD algorithms. The first one group contains deterministic TBD algorithms statistical computations oriented for results calculation. All hypotheses are tested and computation cost is usually constant. The second one group contains nondeterministic TBD algorithms. Such algorithms do not test all hypotheses only use statistical methods for finding most probable results but optimality of results is not guarantied. For example particle filters are statistical search based and they gives results sometimes faster in comparison to first group of algorithms (Gordon et al., 1993; Doucet et al., 2001; Arulampalam et al., 2002; Ristic et al., 2004), but deterministic group is much more reliable for many application and is only considered in this chapter. For real-time applications first group has advantages of results quality and constant processing time very important for