73 research outputs found
A Normalized Fractionally Lower-Order Moment Algorithm for Space-Time Adaptive Processing
A new space-time adaptive processing algorithm is proposed for clutter suppression in phased array radar systems. In contrast to the commonly used normalized least mean square (NLMS) algorithm which uses the second order moments of the data for adaptation, the proposed method uses the lower order moments of the data to adapt the weight coefficients. The normalization is also performed based on the data sample dispersion rather than the variance. Processing results using simulated and measured data show that the proposed algorithm converges faster than the NLMS algorithms in Gaussian and non-Gaussian clutter environments. It also provides better clutter suppression than the NLMS algorithm under heavy-tailed, impulsive, non-Gaussian environments. It in turn improves the target detection performance
Dynamic Multi-Arm Bandit Game Based Multi-Agents Spectrum Sharing Strategy Design
For a wireless avionics communication system, a Multi-arm bandit game is
mathematically formulated, which includes channel states, strategies, and
rewards. The simple case includes only two agents sharing the spectrum which is
fully studied in terms of maximizing the cumulative reward over a finite time
horizon. An Upper Confidence Bound (UCB) algorithm is used to achieve the
optimal solutions for the stochastic Multi-Arm Bandit (MAB) problem. Also, the
MAB problem can also be solved from the Markov game framework perspective.
Meanwhile, Thompson Sampling (TS) is also used as benchmark to evaluate the
proposed approach performance. Numerical results are also provided regarding
minimizing the expectation of the regret and choosing the best parameter for
the upper confidence bound
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