This paper provides a comparison of the two main techniques currently in use to solve the problem of radar pulse train
deinterleaving. Pulse train deinterleaving separates radar pulse trains into the tracks or bins associated with the detected
emitters. The two techniques are simple time of arrival (TOA) histogramming and multi-parametric analysis. TOA
analysis uses only the time of arrival (TOA) parameter of each pulse to deinterleave radar pulse trains. Such algorithms
include Cumulative difference (CDIF) histogramming and Sequential difference (SDIF) histogramming. Multiparametric
analysis utilizes any combination of the following parameters: TOA, radio frequency (RF), pulse width (PW),
and angle of arrival (AOA). These techniques use a variety of algorithms, such as Fuzzy Adaptive Resonance Theory
(Fuzzy-ART), Fuzzy Min-Max Clustering (FMMC), Integrated Adaptive Fuzzy Clustering (IAFC) and Fuzzy Adaptive
Resonance Theory Map (Fuzzy-ARTMAP) to compare the pulses to determine if they are from the same emitter. Good
deinterleaving is critical since inaccurate deinterleaving can lead to misidentification of emitters.
The deinterleaving techniques evaluated in this paper are a sizeable and representative sample of both US and
international efforts developed in the UK, Canada, Australia and Yugoslavia. Mardia  and Milojevic and Popovich
 shows some of the early work in TOA-based deinterleaving. Ray  demonstrates some of the more recent
work in this area. Multi-parametric techniques are exemplified by Granger, et al  and Thompson and Sciortino
. This paper will provide an analysis of the algorithms and discuss the results obtained from the referenced
articles. The algorithms will be evaluated for usefulness in deinterleaving pulse trains from agile radars.
This paper will compare the various methods of analyzing the results of radar pulse train deinterleavers. This paper is divided into three sections. The first section of this paper will describe the basic methods, such as the confusion matrix, and some measures that can be obtained from the matrix. The measures will include correct correlation, miscorrelation, ambiguity and track purity. Correct correlation is calculated by dividing the total number of correctly clustered pulses by the total number of pulses in the collect. Miscorrelation measures the fraction of received pulses that incorrectly deinterleaved. Ambiguity measures the fraction of received pulses that are rejected by the deinterleaver as having uncertain association with a ground truth track. Track purity measures the ability of the deinterleaver to create a constructed track comprised of pulses from a single ground truth track. These metrics will show the quality of the deinterleaving operation.
The second section of this paper will describe some of the advanced similarity measures of effectiveness. This section will also describe how distance measures will be used to analyze deinterleaver results. The two main similarity measures to be discussed in this paper will be the Rand Adjust and Jaccard coefficient. These similarity measures are also known as criterion indices and are used for evaluating the capacity to recover true cluster structure. The reason for the selection of the Jaccard and Rand Adjust as measures is that they both allow a value to be obtained that is between 0 and 1 that will show how good the clusterer in question has performed. The Rand Adjust also allows for more variability in the range between 0 and 1 and appears to provide a more accurate evaluation of the cluster. The distance measures that will be described include Euclidean, Mahalanobis and Minkowski distances. These distance measures have different methods to evaluate each cluster for purity. These measures will provide an indication of the quality of the deinterleaver operation.
The third section of this paper will provide the results from these measures on a deinterleavered dataset and will discuss the comparison of these metrics.
This paper will evaluate one promising method used to solve one of the main problems in electronic warfare. This problem is the identification of radar signals in a tactical environment. The identification process requires two steps: clustering of collected radar pulse descriptor words and the classification of clustered results. The method described here, Fuzzy Adaptive Resonance Theory Map (Fuzzy ARTMAP) is a self-organizing neural network algorithm. The
benefits of this algorithm are that the training process is very stable and fast and that it needs a small number of required initial parameters and it performs very well at novelty detection, which is the classification of unknown radar emitters. This paper will discuss the theory behind the Fuzzy ARTMAP, as well as results of the processing of two `i real^i radar pulse data sets. The first evaluated data set consists of 5242 radar pulse descriptor words from 32 different emitters. The second data set consists of 107850 pulse descriptor words from 112 different emitters. The radar pulse descriptors words that were used by the algorithm for both sets of data were radio frequency (RF) and pulse width (PW). The results of the processing of both of these datasets were better than 90% correct correlation with actual ID, which exceeds the results of processing these datasets with other algorithms such as K-Means and other self-organizing neural networks.