Quantization for decentralized hypothesis testing under communication constraints

In a decentralized hypothesis testing network, several peripheral nodes observe an environment and communicate their observations to a central node for the final decision. The presence of capacity constraints introduces theoretical and practical problems. The following problem is addressed: given that the peripheral encoders that satisfy these constraints are scalar quantizers, how should they be designed in order that the central test to be performed on their output indices is most powerful? The scheme is called cooperative design-separate encoding since the quantizers process separate observations but have a common goal; they seek to maximize a system-wide performance measure. The Bhattacharyya distance of the joint index space as such a criterion is suggested, and a design algorithm to optimize arbitrarily many quantizers cyclically is proposed. A simplified version of the algorithm, namely an independent design-separate encoding scheme, where the correlation is either absent or neglected for the sake of simplicity, is outlined. Performances are compared through worked examples. >

[1]  D. Blackwell Comparison of Experiments , 1951 .

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

[3]  D. Blackwell Equivalent Comparisons of Experiments , 1953 .

[4]  R. N. Bradt On the Design and Comparison of Certain Dichotomous Experiments , 1954 .

[5]  K. Matusita,et al.  On testing statistical hypotheses , 1954 .

[6]  Solomon Kullback,et al.  Information Theory and Statistics , 1960 .

[7]  Thomas L. Grettenberg,et al.  Signal selection in communication and radar systems , 1963, IEEE Trans. Inf. Theory.

[8]  S. M. Ali,et al.  A General Class of Coefficients of Divergence of One Distribution from Another , 1966 .

[9]  T. Kailath The Divergence and Bhattacharyya Distance Measures in Signal Selection , 1967 .

[10]  H. V. Poor,et al.  Applications of Ali-Silvey Distance Measures in the Design of Generalized Quantizers for Binary Decision Systems , 1977, IEEE Trans. Commun..

[11]  Saleem A. Kassam,et al.  Optimum Quantization for Signal Detection , 1977, IEEE Trans. Commun..

[12]  Robert M. Gray,et al.  Speech coding based upon vector quantization , 1980, ICASSP.

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

[14]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[15]  H. Vincent Poor,et al.  On Optimum and Nearly Optimum Data Quantization for Signal Detection , 1984, IEEE Trans. Commun..

[16]  R. Gray,et al.  Vector quantization , 1984, IEEE ASSP Magazine.

[17]  R. Gray,et al.  Product code vector quantizers for waveform and voice coding , 1984 .

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

[19]  A. Farina,et al.  Overview of detection theory in multistatic radar , 1986 .

[20]  Rudolf Ahlswede,et al.  Hypothesis testing with communication constraints , 1986, IEEE Trans. Inf. Theory.

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

[22]  Te Han,et al.  Hypothesis testing with multiterminal data compression , 1987, IEEE Trans. Inf. Theory.

[23]  Robert M. Gray,et al.  Encoding of correlated observations , 1987, IEEE Trans. Inf. Theory.

[24]  H. Poor,et al.  Fine quantization in signal detection and estimation , 1988, IEEE Trans. Inf. Theory.