• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.9
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• pp.1019-1026
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• 1988

A parametric detection scheme for determenistic signals is obtained in a generalized observation model which contains non-additive noise. The model employed in this paper includes several special cases such as those describing purely-additive noise, multiplicative noise, and signal dependent noise and allows the consideration of deterministic and random signals. Locally optimum detectors for known deterministic signals in the model are derived and analyzed for performance. It is shown that the locally optimum detectors are interesting generalizations of those for the purely-additive noise model. Performance of the locally optimum detectors designed for the generalized observation model is compared to that of other common detectors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.5
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• pp.949-955
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• 1997

Detection of the existence of a desired user is considered in this paper. We assume that the signal to noise ratio is high enough for us to ignore the effects of noise compared with those of the interference by other users. The inter-user interference and user signals are modeled by the Nakagami model. An observation model for this situation is proposed, the locally optimum test statistic is derived under the model, and the asymptotic performance of the locally optimum detector is compared with of the envelope detector. We show that the locally optimum detector performs better than the conventional envelope detector.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.4
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• pp.319-325
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• 1991

A nonparametric detection scheme which uses rank statistics for detection of known signals is considered in a special case of a generalized observation model. Specifically locally optimum rank detectors for detection of known deterministic singals in a singnal-dependent noise model are derived, and compared to those derived for the purely-additive noise model. Examples of the score functions are given, which constitutes the test statistics of the locally optimum rank detectors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.3
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• pp.284-290
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• 1991

A detection scheme wiich has based in rank statistic is obtained for detection of random signals in additive noise. It is shown that the detector has similarities to the locally optimum detector for random signals and that it is a generalization of the locally optimum rank detector for known signals. Performance of the detector is also considered together with that of other detectors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.2C
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• pp.150-159
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• 2007

In Part 1 of this paper, the test statistics are obtained for detecting weak known signals in observations corrupted by multiplicative and first-order Markov additive noise. In this second part, the asymptotic and finite sample-size performance of several detectors are obtained and compared based on the test statistics. From the performance comparisons, it is confirmed that the dependence among interference components need to be taken into account to maintain the detection performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.532-540
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• 2005

In this paper, a sequential detection scheme is proposed as a combination of a novel weak-signal and a locally optimum(LO) detection schemes. In Part 1, we propose a novel sequential detection scheme for weak signals and show some interesting threshold properties and examples. In Part 2, the performance of the proposed sequential detection scheme is compared with that of the fixed sample size(FSS) test, sequential probability ratio test (SPRT), and truncated sequential probability ratio test(TSPRT).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.3
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• pp.270-276
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• 1988

The problem of detecting weak random signals is addressed in a generalized observation model incorporating multiplicative noised which has recently been introduced. It is shown that the locally optimum random-signal detectors in the multiplicative-noise model are interseting generalizations of those which would be obtained in the purely-additive noise model. Examples of explicits results for the locally optimum detector test statistics are given for two typical cases of well-known pdfs.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.1
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• pp.69-75
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• 2000

In this paper, we consider the problem of discrete-time random signal detection problem under the presence of additive noise exhibiting weak dependence The test statistic of the locally optimum detector for correlated random signals under a weakly dependent noise model is derived The performance characteristic of the locally optimum detector is analyzed and compared with that of the square-law detector in terms of the asymptotic relative efficiency.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.3
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• pp.31-40
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• 2000

A robust scheme of locating and counting the number of vehicles m urban traffic scenes, a core component of vision-based traffic monitoring systems, is presented The method is based on the evidential reasoning, where vehicle evidences m the background subtraction Image are obtained by a new locally optimum thresholding, and the evidences are merged by three heuristic rules using the geometric constraints The locally optimum thresholding guarantees the separation of bright and dark evidences of vehicles even when the vehicles are overlapped or when the vehicles have similar color to the background Experimental results on diverse traffic scenes show that the detection performance is very robust to the operating conditions such as the camera location and the weather The method may be applied even when vehicle movement is not observed since a static Image IS processed without the use of frame difference.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.159-162
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• 2000

Selecting locally optimum thresholds, based on optimizing a criterion composed of the area variation rate and the compactness of the segmented shape, is presented. The method is shown to have the shape-resolving property in the subtraction image, so that overlapped objects may be resolved into bright and dark evidences characterizing each object. As an application a vehicle detection algorithm robust to the operating conditions could be realized by applying simple merging rules to the geometrically correlated bright and dark evidences obtained by this local thresholding.