## Abstract

The need to use quantized data in signal detection can arise in many situations. If remotely acquired data is to be transmitted to a central processing facility, for example, data rate reduction may be highly desirable or necessary. Similarly, storage of data for off-line processing may also require quantization into a relatively small number of levels, especially if one is dealing with a large volume of data. The use of quantized data often results in more robust This work was supported by the Air Force Office of Scientific Research under Grant AFOSR 82-0022. systems which are not very sensitive in their performance to deviations of the actual noise density functions from those assumed in their design. Finally, the use of quantized data can allow simple adaptive schemes to be implemented to allow the system to retain good detection performance even when the noise characteristics can vary considerably over time.

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### References

- [1]J. Max, Quantizing for minimum distortion,
*IRE Trans. Inform. Theory*, Vol. IT–6, pp. 7–12, 1960.Google Scholar - [2]E.L. Lehmann, Testing Statistical Hypotheses, New York, NY: Wiley, pp. 83–88, 1959.Google Scholar
- [3]J. Hajek and Z. Sidak,
*Theory of Rank Tests*, New York, NY: Academic Press, Chap. 6, 1967.Google Scholar - [4]S.A. Kassam, Optimum quantization for signal detection,
*IEEE Trans. Comm.*, Vol. COM-25, pp. 479–484, 1977.Google Scholar - [5]S.A. Kassam and J.B. Thomas, Generalizations of the sign detector based on conditional tests,
*IEEE Trans. Comm.*, Vol. COM–24, pp. 481–487, 1976.Google Scholar - [6]S.A. Kassam, The performance characteristics of two extensions of the sign detector,
*IEEE Trans. Comm.*, Vol. COM–29, pp. 1038–1044, 1981.Google Scholar - [7]W.J. Bushnell and L. Kurz, The optimization and performance of detectors based on partition tests,
*Proc. 12th Allerton Conf. Circ. and Sys.*, pp. 1016–1023, 1974.Google Scholar - [8]R.A. Groeneveld, Asymptotically optimum group rank tests for location,
*J. Amer. Stat. Assoc.*, Vol. 67, pp. 847–849, 1972.MathSciNetMATHCrossRefGoogle Scholar - [9]Y.-C. Ching and L. Kurz, Nonparametric detectors based on m-interval partitioning,
*IEEE Trans. Inform. Theory*, Vol. IT–18, pp. 251–257, 1972.Google Scholar - [10]L. Kurz, Nonparametric detectors based on partition tests, in
*Non- parametric Methods in Communications*, P. Papantoni-Kazakos and D. Kazakos, Eds., New York, NY: Marcel Dekker, Chap. 3, 1977.Google Scholar - [11]H.V. Poor and J.B. Thomas, Applications of Ali-Silvey distance measures in the design of generalized quantizers for binary decision systems,
*IEEE Trans. Comm.*, Vol. COM–25, pp. 893–900, 1977.Google Scholar - [12]H.V. Poor and J.B. Thomas, Optimum quantization for local decisions based on independent samples,
*J. Franklin Inst.*, Vol. 303, pp. 549–561, 1977.MATHCrossRefGoogle Scholar - [13]H.V. Poor and D. Alexandrou, A general relationship between two quan-tizer design criteria,
*IEEE Trans. Inform. Theory*, Vol. IT–26, pp. 210–212, 1980.Google Scholar - [14]P.K. Varshney, Combined quantization-detection of uncertain signals,
*IEEE Trans. Inform. Theory*, Vol. IT–27, pp. 262–265, 1981.Google Scholar - [15]C.C.Lee and J.B. Thomas, Detectors for multinomial input,
*IEEE Trans. Aero. Elec. Sys.*, Vol. AES–19, pp. 288–296, 1983.Google Scholar - [16]V.G. Hansen, Weak-signal optimization of multilevel quantization and corresponding detection performance,
*NTZ Commun. J.*, Vol. 22, pp. 120–123, 1969.Google Scholar - [17]V.M. Baronkin, Asymptotically optimum grouping of observations,
*Radio Eng. Electron. Phys.*, Vol. 17, pp. 1572–1576, 1972.Google Scholar - [18]B.R. Levin and V.M. Baronkin, Asymptotically optimum algorithms of detection of signals from quantized observations,
*Radio Eng. Electron. Phys.*, Vol. 18, pp. 682–689, 1973.Google Scholar - [19]A.H. Nutall, Detection performance characteristics for a system with quantizers, OR-ing, and accumulator,
*J. Acoust. Soc. Amer.*, Vol. 73, pp. 1631–1642, 1983.CrossRefGoogle Scholar - [20]H.V. Poor and J.B. Thomas, Asymptotically robust quantization for detection,
*IEEE Trans. Inform. Theory*, Vol. IT–24, pp. 222–229, 1978.Google Scholar - [21]S. Tantaratana and J.B. Thomas, Quantization for sequential signal detection,
*IEEE Trans. Comm.*, Vol. COM–25, pp. 696–703, 1977.Google Scholar - [22]H.V. Poor and Y. Rivani, Input amplitude compression in digital signal detection systems,
*IEEE Trans. Comm.*, Vol. COM–29, pp. 707–710, 1981.Google Scholar - [23]J.W. Modestino and A.Y. Ningo, Detection of weak signals in narrowband non-Gaussian noise,
*IEEE Trans. Inform. Theory*, Vol. IT–25, pp. 592–600, 1979.Google Scholar - [24]L.J. Cimini and S.A. Kassam, Data quantization for narrowband signal detection,
*IEEE Trans. Aero. Elec. Sys.*, Vol. AES–19, pp. 848–858, 1983.Google Scholar - [25]V.G. Hansen, Optimization and performance of multilevel quantization in automatic detectors,
*IEEE Trans. Aero. Elec. Sys.*, Vol. AES–10, pp. 274–280, 1974.Google Scholar - [26]D. Alexandrou and H.V. Poor, The analysis and design of data quantization schemes for stochastic signal detection systems,
*IEEE Trans. Comm.*, Vol. COM–28, pp. 983 - 991, 1980.Google Scholar - [27]J.G. Shin and S.A. Kassam, Multilevel coincidence correlators for random signal detection,
*IEEE Trans. Inform. Theory*, Vol. IT–25, pp. 47–53, 1979.Google Scholar - [28]S.A. Kassam and T.L. Lim, Coefficient and data quantization in matched filters for detection,
*IEEE Trans. Comm.*, Vol. COM–26, pp. 124–127, 1978.Google Scholar - [29]C.-T. Chen and S.A. Kassam, Optimum quantization of FIR Wiener and matched filters,
*Proc IEEE Intl. Conf. on Comm.*, pp. F6.1.1–F6. 1. 4, 1983.Google Scholar - [30]H.V. Poor and J.B. Thomas, Memoryless quantizer-detectors for constant signals in m-dependent noise,
*IEEE Trans. Inform. Theory*, Vol. IT–26, pp. 423–432, 1980.Google Scholar