Topology Optimization of Trusses—Random Cost Method Versus Evolutionary Algorithms
Computational Optimization and Applications
Global random optimization by simultaneous perturbation stochastic approximation
Proceedings of the 33nd conference on Winter simulation
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Optimal transmit-receiver design in the presence of signal-dependent interference and channel noise
IEEE Transactions on Information Theory
Proper complex random processes with applications to information theory
IEEE Transactions on Information Theory
Information theory and radar waveform design
IEEE Transactions on Information Theory
OFDM MIMO radar with mutual-information waveform design for low-grazing angle tracking
IEEE Transactions on Signal Processing
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A novel criterion for waveform diversity in radar systems is presented that is based on the information theoretic concept of Shannon mutual information (MI). In general, waveform diversity refers to adaptively changing a transmitted waveform based on the target and interference environment. MI is a measure of the information in a random variable or vector about another random variable or vector. It is shown herein that an MI framework provides the basis for a general and powerful criterion for evaluation of candidate waveforms, as well as design of waveforms. The criterion inherently includes radar system and scenario statistical information. In addition, the criterion reduces to a simple analytical function of a set of scalar parameters for the case of Gaussian-distributed proper random processes. It is shown that those scalar parameters are also the canonical correlations of the random variables in the MI expression that involves the received radar signal at two distinct time instants. This property suggests the possibility of new insights into waveform diversity using MI as a criterion. The concept is presented assuming a SAR system configuration, but can be extended to other radar modes and functions, as well as to other sensing applications.