Automatic modulation recognition using time domain parameters
Signal Processing
Automatic identification of digital modulation types
Signal Processing
A comparison of self-organizing neural networks for fast clustering of radar pulses
Signal Processing - Special issue on neural networks
CFAR Adaptive threshold for ESM receiver with logarithmic amplification
Signal Processing
Automatic digital modulation recognition using artificial neural network and genetic algorithm
Signal Processing - Special issue on independent components analysis and beyond
An interval-amplitude algorithm for deinterleaving stochastic pulsetrain sources
IEEE Transactions on Signal Processing
On the estimation of interleaved pulse train phases
IEEE Transactions on Signal Processing
Spectrum estimation of interleaved pulse trains
IEEE Transactions on Signal Processing
The limits of extended Kalman filtering for pulse traindeinterleaving
IEEE Transactions on Signal Processing
Deinterleaving pulse trains using discrete-time stochasticdynamic-linear models
IEEE Transactions on Signal Processing
On periodic pulse interval analysis with outliers and missingobservations
IEEE Transactions on Signal Processing
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The main function of Electronic Support Measure (ESM) is to receive, measure and deinterleave pulses, and then identify alternative threat emitters. Among these processes, pulse deinterleaving is vitally important because dense electromagnetic environments could cause an ESM system to receive a seemingly random pulse stream consisting of interleaved pulse trains with high noise levels. Only when we segregate different radar pulse trains from the pulse stream can we proceed with further processing. Traditional deinterleaving algorithms have demonstrated instability in unconventional circumstances (such as agility of pulse repetition interval (PRI), large noise and jitter, missing of intercepted pulses). Based on the dynamic process of different emitters, a new Statistical Association Pulse Deinterleaving (SAPD) approach is proposed based on the Multiple Hypothesis Tracking (MHT) algorithm in Multiple Target Tracking system. Simulation results have shown that the proposed algorithm can successfully identify pulse trains with constant, jittered and staggered PRI, and provide much greater accuracy in PRI estimation and pulse classification than traditional algorithms, with the presence of large noise, frequency jitter, and many missing pulses.