Target-Centered Models and Information-Theoretic Segmentation for Automatic Target Recognition
Multidimensional Systems and Signal Processing
Waveform selection in radar target classification
IEEE Transactions on Information Theory
Information theory and radar waveform design
IEEE Transactions on Information Theory
Quantitative statistical assessment of conditional models for synthetic aperture radar
IEEE Transactions on Image Processing
Optimizing zero-slice feature of ambiguity function for radar emitter identification
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Situation assessment via multi-target identification and classification in radar sensor networks
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
A new automatic target recognition system based on wavelet extreme learning machine
Expert Systems with Applications: An International Journal
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In this paper, we perform a number of theoretical studies on constant frequency (CF) pulse waveform design and diversity in radar sensor networks (RSN): (1) the conditions for waveform co-existence, (2) interferences among waveforms in RSN, (3) waveform diversity combining in RSN. As an application example, we apply the waveform design and diversity to automatic target recognition (ATR) in RSN and propose maximum-likehood (ML)-ATR algorithms for non-fluctuating target as well as fluctuating target. Simulation results show that our waveform diversity-based ML-ATR algorithm performs much better than single-waveform ML-ATR algorithm for non-fluctuating targets or fluctuating targets. Conclusions are drawn based on our analysis and simulations.