{"title":"Signal Detection","authors":"M. S. Fadali","doi":"10.1017/9781107185920.007","DOIUrl":null,"url":null,"abstract":"In Chapter 13 we considered hypothesis testing in the context of random variables. The detector resulting in the minimum probability of error corresponds to the MAP test as developed in section 13.2.1 or equivalently the likelihood ratio test in section 13.2.3. In this chapter we extend those results to a class of detection problems that are central in radar, sonar and communications, involving measurements of signals over time. The generic signal detection problem that we consider corresponds to receiv ing a signal r(t) over a noisy channel. r(t) either contains a known deterministic pulse s(t) or it does not contain the pulse. Thus our two hypotheses are H 1 : r(t) = s(t) + w(t) H 0 : r(t) = w(t), (14.1) where w(t) is a wide-sense stationary random process. One example of a scenario in which this problem arises is in binary communication using pulse amplitude modulation. In that context the presence or absence of the pulse s(t) represents the transmission of a \" one \" or a \" zero \". As another example, radar and sonar systems are based on transmitting a pulse and detecting the presence or absence of an echo. In our treatment in this chapter we first consider the case in which the noise is white and carry out the formulation and analysis in discrete-time which avoids some of the subtler issues associated with continuous-time white noise. We also initially treat the case in which the noise is Gaussian. In Section 14.3.4 we extend the discussion to discrete-time Gaussian colored noise. In Section 14.3.2 we discuss the implications when the noise is not Gaussian and in Section 14.3.3 we discuss how the results generalize to the continuous-time case. In the signal detection task outlined above, our hypothesis test is no longer based on the measurement of a single (scalar) random variable R, but instead involves a collection of L","PeriodicalId":257527,"journal":{"name":"Encyclopedia of Wireless Networks","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Encyclopedia of Wireless Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/9781107185920.007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 24
Abstract
In Chapter 13 we considered hypothesis testing in the context of random variables. The detector resulting in the minimum probability of error corresponds to the MAP test as developed in section 13.2.1 or equivalently the likelihood ratio test in section 13.2.3. In this chapter we extend those results to a class of detection problems that are central in radar, sonar and communications, involving measurements of signals over time. The generic signal detection problem that we consider corresponds to receiv ing a signal r(t) over a noisy channel. r(t) either contains a known deterministic pulse s(t) or it does not contain the pulse. Thus our two hypotheses are H 1 : r(t) = s(t) + w(t) H 0 : r(t) = w(t), (14.1) where w(t) is a wide-sense stationary random process. One example of a scenario in which this problem arises is in binary communication using pulse amplitude modulation. In that context the presence or absence of the pulse s(t) represents the transmission of a " one " or a " zero ". As another example, radar and sonar systems are based on transmitting a pulse and detecting the presence or absence of an echo. In our treatment in this chapter we first consider the case in which the noise is white and carry out the formulation and analysis in discrete-time which avoids some of the subtler issues associated with continuous-time white noise. We also initially treat the case in which the noise is Gaussian. In Section 14.3.4 we extend the discussion to discrete-time Gaussian colored noise. In Section 14.3.2 we discuss the implications when the noise is not Gaussian and in Section 14.3.3 we discuss how the results generalize to the continuous-time case. In the signal detection task outlined above, our hypothesis test is no longer based on the measurement of a single (scalar) random variable R, but instead involves a collection of L