Article

Hierarchical classification of dynamically varying radar pulse repetition interval modulation patterns.

Tampere University of Technology, Department of Signal Processing, Tampere, Finland.
Neural networks: the official journal of the International Neural Network Society (Impact Factor: 2.08). 12/2010; 23(10):1226-37. DOI: 10.1016/j.neunet.2010.06.008
Source: PubMed

ABSTRACT The central purpose of passive signal intercept receivers is to perform automatic categorization of unknown radar signals. Currently, there is an urgent need to develop intelligent classification algorithms for these devices due to emerging complexity of radar waveforms. Especially multifunction radars (MFRs) capable of performing several simultaneous tasks by utilizing complex, dynamically varying scheduled waveforms are a major challenge for automatic pattern classification systems. To assist recognition of complex radar emissions in modern intercept receivers, we have developed a novel method to recognize dynamically varying pulse repetition interval (PRI) modulation patterns emitted by MFRs. We use robust feature extraction and classifier design techniques to assist recognition in unpredictable real-world signal environments. We classify received pulse trains hierarchically which allows unambiguous detection of the subpatterns using a sliding window. Accuracy, robustness and reliability of the technique are demonstrated with extensive simulations using both static and dynamically varying PRI modulation patterns.

Full-text

Available from: Jukka-Pekka Kauppi, Dec 20, 2013
2 Followers
 · 
417 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: TDOA (Time Difference of Arrival) is an important and widely used wireless localization technique. Among the enormous approaches of TDOA, high resolution TDOA algorithms have drawn much attention for its ability to resolve closely spaced signal delays in multipath environment. However, the state-of-art high resolution TDOA algorithms still have performance weakness on resolving time delays in a wireless channel with dense multipath effect, as well as difficulties of implementation for their high computation complexity. In this paper, we propose a novel TDOA algorithm with super resolution based on certain kind of multi-dimensional cross-correlation function: the volume cross-correlation function (VCC). The proposed TDOA algorithm has excellent time resolution performance in multipath environment, and it also has a much lower computational complexity. Our algorithm does not require priori knowledge about the waveform or power spectrum of transmitted signals, therefore has great potential of usage in various passive wireless localization systems. Numerical simulations is also provided to demonstrate the validity of our conclusion.