Cognitive Dynamic Systems: Perception-action Cycle, Radar and Radio
The principles of cognition are becoming increasingly important in the areas of signal processing, communications and control. In this groundbreaking book, Simon Haykin, a pioneer in the field and an award-winning researcher, educator and author, sets out the fundamental ideas of cognitive dynamic systems. Weaving together the various branches of study involved, he demonstrates the power of cognitive information processing and highlights a range of future research directions. The book begins with a discussion of core topics such as cognition and sensing, dealing, in particular, with the perception-action cycle. Bayesian filtering, machine learning and dynamic programming are then addressed. Building on these foundations, there is detailed coverage of two important practical applications, cognitive radar and cognitive radio. Blending theory and practice, this insightful book is aimed at all graduate students and researchers looking for a thorough grounding in this fascinating field.
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2 The perceptionaction cycle
3 Powerspectrum estimation for sensing the environment
4 Bayesian filtering for state estimation of the environment
5 Dynamic programming for action in the environment
6 Cognitive radar
action algorithm application approximate Bayesian filter behavior Chapter cognitive dynamic system cognitive radio network complex computational cost-to-go function covariance matrix cubature cubature rule cyclostationarity decision-making defined denoted described discussed distribution dynamic programming dynamic spectrum management dynamic-programming algorithm environmental scene error executive memory feedback information femtocells Figure follows formulation Fourier frequency Gaussian GQ(l Haykin Hebbian learning hidden layer iteration IWFC Kalman filter linear multitaper Nash equilibrium neural network neurons observation OFDM optimal output parameter vector perception perception–action cycle perceptor perceptual memory performed posterior power spectrum predictive problem Q-factor Q-learning radio environment receiver referred RMLP scene analyzer secondary users self-organized Slepian space sparse coding spectral estimator spectrum holes spectrum sensing state-estimation state-space model statistical stochastic subbands subcarriers supervised learning synaptic weights system noise target TD learning theory traditional active radar transmit-waveform transmitter update waveform weight vector