Distributed binary hypothesis testing with feedback

The problem of binary hypothesis testing is revisited in the context of distributed detection with feedback. Two basic distributed structures with decision feedback are considered. The first structure is the fusion center network, with decision feedback connections from the fusion center element to each one of the subordinate decisionmakers. The second structure consists of a set of detectors that are fully interconnected via decision feedback. Both structures are optimized in the Neyman-Pearson sense by optimizing each decision-maker individually. Then, the time evolution of the power of the tests is derived. Definite conclusions regarding the gain induced by the feedback process and direct comparisons between the two structures and the optimal centralized scheme are obtained through asymptotic studies (that is, assuming the presence of asymptotically many local detectors). The behavior of these structures is also examined in the presence of variations in the statistical description of the hypotheses. Specific robust designs are proposed and the benefits from robust operations are established. Numerical results provide additional support to the theoretical arguments. >

[1]  Ramanarayanan Viswanathan,et al.  Optimal serial distributed decision fusion , 1988 .

[2]  Pramod K. Varshney,et al.  Distributed Bayesian hypothesis testing with distributed data fusion , 1988, IEEE Trans. Syst. Man Cybern..

[3]  P. Papantoni-Kazakos,et al.  Detection and Estimation , 1989 .

[4]  Ramanarayanan Viswanathan,et al.  Optimal Decision Fusion in Multiple Sensor Systems , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[5]  R. Gray,et al.  Robustness of Estimators on Stationary Observations , 1979 .

[6]  Ramanarayanan Viswanathan,et al.  Optimal serial distributed decision fusion , 1987, 26th IEEE Conference on Decision and Control.

[7]  D. Kazakos,et al.  On-Line Threshold Learning for Neyman-Pearson Distributed Detection , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[8]  Nils Sandell,et al.  Detection with Distributed Sensors , 1980, IEEE Transactions on Aerospace and Electronic Systems.

[9]  George V. Moustakides,et al.  Robust detection of signals: A large deviations approach , 1985, IEEE Trans. Inf. Theory.

[10]  H. Chernoff A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations , 1952 .

[11]  Krishna R. Pattipati,et al.  An algorithm for determining the decision thresholds in a distributed detection problem , 1989, Conference Proceedings., IEEE International Conference on Systems, Man and Cybernetics.

[12]  R. Srinivasan,et al.  Distributed detection with decision feedback , 1990 .

[13]  Chung Chieh Lee,et al.  Optimum local decision space partitioning for distributed detection , 1989 .

[14]  R. Srinivasan Distributed radar detection theory , 1986 .

[15]  Alfonso Farina,et al.  Multistatic radar detection: synthesis and comparison of optimum and suboptimum receivers , 1983 .

[16]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[17]  P.K. Varshney,et al.  Optimal Data Fusion in Multiple Sensor Detection Systems , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[18]  Pramod K. Varshney,et al.  Distributed Bayesian signal detection , 1989, IEEE Trans. Inf. Theory.

[19]  John N. Tsitsiklis,et al.  On the complexity of decentralized decision making and detection problems , 1985 .

[20]  Panayota Papantoni-Kazakos,et al.  New nonleast-squares neural network learning algorithms for hypothesis testing , 1995, IEEE Trans. Neural Networks.