Purity of deinterleaved pulse descriptor word (PDW) trains is critical to the performance of emitter classification software that analyzes PDW data. Contamination of the input PDW train can lead to artifacts in the analysis resulting in incorrect or ambiguous classification. This paper presents results of an investigation into the possibility of applying transition matrices to the detection and removal of contaminating pulses from a deinterleaver output. The utility of transition matrices as a fast and efficient pattern recognition tool in ESM emitter classification has been known for over a decade [Brown, R.G., "Pattern Recognition Using Transition Matrices", 7th RMC Symposium on Applications of Advanced Software in Engineering, pp 52-57, Royal Military College of Canada, Kingston Ontario, May 1995]. In the work presented herein, transition matrix patterns unique to contaminated pulse trains are sought in order to provide a warning that a particular PDW train is contaminated, and provide a clue as to which pulses should be removed to purify the PDW train.
High-accuracy, low-ambiguity emitter classification based on ESM signals is critical to the safety and effectiveness of military platforms. Many previous ESM classification techniques involved comparison of either the average observed value or the observed limits of ESM parameters with the expected limits contained in an emitter library. Signal parameters considered typically include radio frequency (RF), pulse repetition interval (PRI), and pulse width (PW). These simple library comparison techniques generally yield ambiguous results because of the high density of emitters in key regions of the parameter space (X-band). This problem is likely to be exacerbated as military platforms are more frequently called upon to conduct operations in littoral waters, where high densities of airborne, sea borne, and land based emitters greatly increase signal clutter. A key deficiency of the simple techniques is that by focusing only on parameter averages or limits, they fail to take advantage of much information contained in the observed signals. In this paper we describe a Dempster-Shafer technique that exploits a set of hierarchical parameter trees to provide a detailed description of signal behavior. This technique provides a significant reduction in ambiguity particularly for agile emitters whose signals provide much information for the algorithm to utilize.