简介：因为他们的广泛的应用，无人的表面车辆(USV)成为了研究的一个焦点。保证安全和可靠性并且独立地执行复杂任务，USV被要求拥有环境和有效碰撞回避能力的精确感觉。完成这些，进实时海洋的雷达目标察觉的调查和自治碰撞回避技术被要求，瞄准解决噪音堵塞，在海洋的雷达中的不平的亮度，目标损失，和盲目区域的问题想象。这些技术应该也满足要求即时并且与USV的高航行速度有关的可靠性。因此，这研究基于海洋的雷达开发了一个嵌入的碰撞回避系统，调查了包含适应变光滑的算法和柔韧的分割算法的一个高度即时的目标察觉方法，开发了一个马厩和可靠动态本地环境模型保证USV的安全航行，并且基于调整USV标题和速度在的速度障碍(V障碍)构造了一个碰撞回避算法即时。在多障碍回避的海试用结果第一表明建议回避系统的有效性和效率，然后当时，验证它的大适应性和相对稳定性航行在真实、复杂的海洋的环境的USV。获得的结果将改进USV的聪明的水平并且保证USV独立人士航行的安全。

简介：Fortheissueofdeteriorationindetectionperformancecausedbydynamicallychangingenvironmentinultra-wideband(UWB)multipleinputmultipleoutput(MIMO)radar,thispaperproposesanoveladaptivewaveformdesignwhichisaimedtoimprovetheabilityofdiscriminatingtargetandclutterfromtheradarscene.Firstly,asequenceofMorletwaveletpulseswithfrequencyhoppingandpulsepositionmodulationbyWelch-Costasarrayisdesigned.Thenawaveformoptimizationsolutionisproposedwhichisachievedbyapplyingtheminimizationmutual-information(MI)strategy.Afterthat,withsubsequentiterationsofthealgorithm,simulationresultsdemonstratethattheoptimalwaveformdesignmethodbringsanimprovementinthetargetdetectionabilityinthepresenceofnoiseandclutter.

简介：Inthisstudy,cloudbaseheight(CBH)andcloudtopheight(CTH)observedbytheKa-band(33.44GHz)cloudradarattheBoseongNationalCenterforIntensiveObservationofSevereWeatherduringfall2013(September-November)wereverifiedandcorrected.Forcomparativeverification,CBHandCTHwereobtainedusingaceilometer(CL51)andtheCommunication,OceanandMeteorologicalSatellite(COMS).Duringrainfall,theCBHandCTHobservedbythecloudradarwerelowerthanobservedbytheceilometerandCOMSbecauseofsignalattenuationduetoraindrops,andthisdifferenceincreasedwithrainfallintensity.Duringdryperiods,however,theCBHandCTHobservedbythecloudradar,ceilometer,andCOMSweresimilar.Thinandlow-densitycloudswereobservedmoreeffectivelybythecloudradarcomparedwiththeceilometerandCOMS.Incasesofrainfallormissingcloudradardata,theceilometerandCOMSdatawereproveneffectiveincorrectingorcompensatingthecloudradardata.Thesecorrectedclouddatawereusedtoclassifycloudtypes,whichrevealedthatlowcloudsoccurredmostfrequently.