Performance analysis of adaptive algorithms for space-time adaptive processor (STAP) in phased array radar
The most challenging task in phased array radar system is the mitigation or suppression of noise and interferences to enhance the useful signal in radar received signals. Multiple solutions have been proposed by researchers for suppression of unwanted signals or interferences which have highly degra...
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doaj-660791aca3eb4708a2a0faebf12baa7f2021-04-02T11:35:43ZengWileyThe Journal of Engineering2051-33052019-07-0110.1049/joe.2019.0234JOE.2019.0234Performance analysis of adaptive algorithms for space-time adaptive processor (STAP) in phased array radarRaafia Irfan0Haroon ur Rasheed1Waqas A Toor2Muhammad Ashraf3Pakistan Institute of Engineering & Applied Sciences (PIEAS)Pakistan Institute of Engineering & Applied Sciences (PIEAS)Capital University of Science & Technology (CUST)Capital University of Science & Technology (CUST)The most challenging task in phased array radar system is the mitigation or suppression of noise and interferences to enhance the useful signal in radar received signals. Multiple solutions have been proposed by researchers for suppression of unwanted signals or interferences which have highly degraded the overall system performance. Nowadays, space-time adaptive processor (STAP) which is a two-dimensional filtering technique is commonly used in phased array antenna with multiple spatial channels. A detailed study is performed on the well-known adaptive algorithms least mean square (LMS), normalized least mean square (NLMS) and recursive least square (RLS), to suppress the high state sea clutter by updating the adaptive STAP weight vector. However, RLS is considered as the suitable adaptive algorithm in STAP because of its good convergence rate, but has high computational complexity. To overcome this issue, two variants of RLS (QRD-RLS and Inverse QRD-RLS) are also discussed which reduce the computational complexity of the algorithm. MATLAB simulations are done to verify the performance of LMS and RLS algorithms in terms of accuracy and convergence rate. Finally, hardware implementations of STAP-QRD-RLS and STAP-IQRD-RLS adaptive beamformers are done and evaluated in terms of latency, throughput and efficiency. The selected platform is the Virtex-5 field-programmable gate array.https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0234radar signal processingfiltering theoryleast squares approximationsphased array radarradar clutterantenna phased arraysarray signal processingspace-time adaptive processingcomputational complexityradar detectionsignal denoisinglinear antenna arraysradar antennasvectorsVirtex-5 field-programmable gate arrayperformance analysisspace-time adaptive processorphased array radar systemradar received signalsinterference mitigationtwo-dimensional filtering techniqueadaptive array processing algorithmsLMSadaptive STAP weight vectornoise mitigationnoise suppressioninterference suppressionunwanted signal suppressionuniform linear arrayULA-based one-dimensional filtering techniquesphased array antennasmultiple spatial channelstarget detectionleast mean squarerecursive least squarecomputational complexityQRD-RLSMatlab simulationsSTAP-IQRD-RLS adaptive beamformersSTAP-QRD_RLS adaptive beamformersoptimised single precision floating point complex multiplier2D beamformersconvergence rate |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Raafia Irfan Haroon ur Rasheed Waqas A Toor Muhammad Ashraf |
spellingShingle |
Raafia Irfan Haroon ur Rasheed Waqas A Toor Muhammad Ashraf Performance analysis of adaptive algorithms for space-time adaptive processor (STAP) in phased array radar The Journal of Engineering radar signal processing filtering theory least squares approximations phased array radar radar clutter antenna phased arrays array signal processing space-time adaptive processing computational complexity radar detection signal denoising linear antenna arrays radar antennas vectors Virtex-5 field-programmable gate array performance analysis space-time adaptive processor phased array radar system radar received signals interference mitigation two-dimensional filtering technique adaptive array processing algorithms LMS adaptive STAP weight vector noise mitigation noise suppression interference suppression unwanted signal suppression uniform linear array ULA-based one-dimensional filtering techniques phased array antennas multiple spatial channels target detection least mean square recursive least square computational complexity QRD-RLS Matlab simulations STAP-IQRD-RLS adaptive beamformers STAP-QRD_RLS adaptive beamformers optimised single precision floating point complex multiplier 2D beamformers convergence rate |
author_facet |
Raafia Irfan Haroon ur Rasheed Waqas A Toor Muhammad Ashraf |
author_sort |
Raafia Irfan |
title |
Performance analysis of adaptive algorithms for space-time adaptive processor (STAP) in phased array radar |
title_short |
Performance analysis of adaptive algorithms for space-time adaptive processor (STAP) in phased array radar |
title_full |
Performance analysis of adaptive algorithms for space-time adaptive processor (STAP) in phased array radar |
title_fullStr |
Performance analysis of adaptive algorithms for space-time adaptive processor (STAP) in phased array radar |
title_full_unstemmed |
Performance analysis of adaptive algorithms for space-time adaptive processor (STAP) in phased array radar |
title_sort |
performance analysis of adaptive algorithms for space-time adaptive processor (stap) in phased array radar |
publisher |
Wiley |
series |
The Journal of Engineering |
issn |
2051-3305 |
publishDate |
2019-07-01 |
description |
The most challenging task in phased array radar system is the mitigation or suppression of noise and interferences to enhance the useful signal in radar received signals. Multiple solutions have been proposed by researchers for suppression of unwanted signals or interferences which have highly degraded the overall system performance. Nowadays, space-time adaptive processor (STAP) which is a two-dimensional filtering technique is commonly used in phased array antenna with multiple spatial channels. A detailed study is performed on the well-known adaptive algorithms least mean square (LMS), normalized least mean square (NLMS) and recursive least square (RLS), to suppress the high state sea clutter by updating the adaptive STAP weight vector. However, RLS is considered as the suitable adaptive algorithm in STAP because of its good convergence rate, but has high computational complexity. To overcome this issue, two variants of RLS (QRD-RLS and Inverse QRD-RLS) are also discussed which reduce the computational complexity of the algorithm. MATLAB simulations are done to verify the performance of LMS and RLS algorithms in terms of accuracy and convergence rate. Finally, hardware implementations of STAP-QRD-RLS and STAP-IQRD-RLS adaptive beamformers are done and evaluated in terms of latency, throughput and efficiency. The selected platform is the Virtex-5 field-programmable gate array. |
topic |
radar signal processing filtering theory least squares approximations phased array radar radar clutter antenna phased arrays array signal processing space-time adaptive processing computational complexity radar detection signal denoising linear antenna arrays radar antennas vectors Virtex-5 field-programmable gate array performance analysis space-time adaptive processor phased array radar system radar received signals interference mitigation two-dimensional filtering technique adaptive array processing algorithms LMS adaptive STAP weight vector noise mitigation noise suppression interference suppression unwanted signal suppression uniform linear array ULA-based one-dimensional filtering techniques phased array antennas multiple spatial channels target detection least mean square recursive least square computational complexity QRD-RLS Matlab simulations STAP-IQRD-RLS adaptive beamformers STAP-QRD_RLS adaptive beamformers optimised single precision floating point complex multiplier 2D beamformers convergence rate |
url |
https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0234 |
work_keys_str_mv |
AT raafiairfan performanceanalysisofadaptivealgorithmsforspacetimeadaptiveprocessorstapinphasedarrayradar AT haroonurrasheed performanceanalysisofadaptivealgorithmsforspacetimeadaptiveprocessorstapinphasedarrayradar AT waqasatoor performanceanalysisofadaptivealgorithmsforspacetimeadaptiveprocessorstapinphasedarrayradar AT muhammadashraf performanceanalysisofadaptivealgorithmsforspacetimeadaptiveprocessorstapinphasedarrayradar |
_version_ |
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