Multiple radar environment emission deinterleaving and PRI prediction

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dc.contributor.advisorMishra, Amit Ken_ZA
dc.contributor.authorManickchand, Kaveeren_ZA
dc.date.accessioned2017-09-23T06:19:07Z
dc.date.available2017-09-23T06:19:07Z
dc.date.issued2017en_ZA
dc.description.abstractThe aim of this study was to research TOA based tracking and deinterleaving algorithms suited to radar emitters in an EW environment for application on the CSIR 5th generation DRFM platform. The research problem statement stipulated that the only defining characteristic of the different emitters be the time of arrival (TOA) of their pulses. The pulse repetition interval (PRI) schemes considered in the study was constant, jittered, staggered and dwell and switch. The different TOA based deinterleaving algorithms investigated were sequence search (SS), TOA difference histogram, CDIF, SDIF, CDIF with SS (CDIF SS), SDIF with SS (SDIF SS) and interleaved pulse train spectrum estimation. The interleaved pulse train spectrum estimation algorithm results could not be replicated and were not included in simulations. The TOA based tracking algorithms that were also investigated were Delta-t histogram, Kalman filter, alpha-beta filter and alpha-beta-gamma filter. The alpha-beta-gamma filter became unstable during simulations and hence their results have also been excluded. The algorithms were simulated in MATLAB against EW environments with varied TOA measurement noise, number of emitters, PRI schemes and interference pulses (missing and spurious). General conclusions drawn from the deinterleaving simulations were the success of the algorithms decrease with the increase of emitters in the EW environment, interference pulses increased the success of some algorithms and the success of algorithms increased with TMNR (time measurement to noise ratio). General conclusions drawn from the tracking simulations were track loss of the algorithms decrease with increase in TMNR, tracking error decreases with increase in TMNR and interference pulses affected the initial estimates used to initialise the filters. The performance of the deinterleaving (CDIF & CDIF SS) and tracking ( Delta-t histogram & alpha-beta filter) algorithms were compared on the DRFM platform. On the DRFM platform, the CDIF algorithm deinterleaved in fewer pulses but had more false detections as compared to the CDIF SS algorithm. The alpha-beta filter performed better with lower TMNR than the Delta-t histogram, on the DRFM platform. The CDIF SS algorithm and alpha-beta filter were chosen, based on their performance on the DRFM, to be implemented on a DRFM based system that would deinterleave and then track emitters in an EW environment. The system was successfully implemented and met all requirements that were placed on it. Possible improvements to the system and the future improvements to the research are also discussed.en_ZA
dc.identifier.apacitationManickchand, K. (2017). <i>Multiple radar environment emission deinterleaving and PRI prediction</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/25327en_ZA
dc.identifier.chicagocitationManickchand, Kaveer. <i>"Multiple radar environment emission deinterleaving and PRI prediction."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2017. http://hdl.handle.net/11427/25327en_ZA
dc.identifier.citationManickchand, K. 2017. Multiple radar environment emission deinterleaving and PRI prediction. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Manickchand, Kaveer AB - The aim of this study was to research TOA based tracking and deinterleaving algorithms suited to radar emitters in an EW environment for application on the CSIR 5th generation DRFM platform. The research problem statement stipulated that the only defining characteristic of the different emitters be the time of arrival (TOA) of their pulses. The pulse repetition interval (PRI) schemes considered in the study was constant, jittered, staggered and dwell and switch. The different TOA based deinterleaving algorithms investigated were sequence search (SS), TOA difference histogram, CDIF, SDIF, CDIF with SS (CDIF SS), SDIF with SS (SDIF SS) and interleaved pulse train spectrum estimation. The interleaved pulse train spectrum estimation algorithm results could not be replicated and were not included in simulations. The TOA based tracking algorithms that were also investigated were Delta-t histogram, Kalman filter, alpha-beta filter and alpha-beta-gamma filter. The alpha-beta-gamma filter became unstable during simulations and hence their results have also been excluded. The algorithms were simulated in MATLAB against EW environments with varied TOA measurement noise, number of emitters, PRI schemes and interference pulses (missing and spurious). General conclusions drawn from the deinterleaving simulations were the success of the algorithms decrease with the increase of emitters in the EW environment, interference pulses increased the success of some algorithms and the success of algorithms increased with TMNR (time measurement to noise ratio). General conclusions drawn from the tracking simulations were track loss of the algorithms decrease with increase in TMNR, tracking error decreases with increase in TMNR and interference pulses affected the initial estimates used to initialise the filters. The performance of the deinterleaving (CDIF & CDIF SS) and tracking ( Delta-t histogram & alpha-beta filter) algorithms were compared on the DRFM platform. On the DRFM platform, the CDIF algorithm deinterleaved in fewer pulses but had more false detections as compared to the CDIF SS algorithm. The alpha-beta filter performed better with lower TMNR than the Delta-t histogram, on the DRFM platform. The CDIF SS algorithm and alpha-beta filter were chosen, based on their performance on the DRFM, to be implemented on a DRFM based system that would deinterleave and then track emitters in an EW environment. The system was successfully implemented and met all requirements that were placed on it. Possible improvements to the system and the future improvements to the research are also discussed. DA - 2017 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2017 T1 - Multiple radar environment emission deinterleaving and PRI prediction TI - Multiple radar environment emission deinterleaving and PRI prediction UR - http://hdl.handle.net/11427/25327 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/25327
dc.identifier.vancouvercitationManickchand K. Multiple radar environment emission deinterleaving and PRI prediction. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2017 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/25327en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical Engineeringen_ZA
dc.subject.otherRadar and Electronic Defenceen_ZA
dc.titleMultiple radar environment emission deinterleaving and PRI predictionen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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