Nature-inspired waveform optimisation for range spread target detection in cognitive radar
The waveform optimisation problem in cognitive radar is non-convex and will have sub-optimal solutions when solved by the semi-definite relaxation (SDR) technique. Here, a novel nature-inspired waveform optimisation framework is proposed for range-spread target detection in cognitive radar. First, t...
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| Veröffentlicht in: | Journal of engineering (Stevenage, England) England), 2019-10, Vol.2019 (20), p.6767-6771 |
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| container_title | Journal of engineering (Stevenage, England) |
| container_volume | 2019 |
| creator | Wang, Qing Li, Meng Gao, Lirong Li, Kaiming Chen, Hua |
| description | The waveform optimisation problem in cognitive radar is non-convex and will have sub-optimal solutions when solved by the semi-definite relaxation (SDR) technique. Here, a novel nature-inspired waveform optimisation framework is proposed for range-spread target detection in cognitive radar. First, the waveform optimisation problem is formulated using maximum a posteriori probability and Kalman filtering to estimate the target scattering coefficients. To solve this problem more accurately and efficiently, three nature-inspired algorithms (modified particle swarm optimisation algorithm, Bat Algorithm, and Beetle Antennae Search algorithm), as a nature-inspired waveform optimisation (NIWO) approach is proposed. It is demonstrated through computer simulations that the proposed NIWO approach significantly outperforms the SDR approach, showing a promising tool for waveform optimisation in cognitive radar. |
| doi_str_mv | 10.1049/joe.2019.0527 |
| format | Article |
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It is demonstrated through computer simulations that the proposed NIWO approach significantly outperforms the SDR approach, showing a promising tool for waveform optimisation in cognitive radar.</description><subject>antenna arrays</subject><subject>Beetle Antennae Search algorithm</subject><subject>cognitive radar</subject><subject>IET International Radar Conference (IRC 2018)</subject><subject>Kalman filters</subject><subject>modified particle swarm optimisation algorithm</subject><subject>nature-inspired algorithms</subject><subject>nature-inspired waveform optimisation approach</subject><subject>novel nature-inspired waveform optimisation framework</subject><subject>object detection</subject><subject>optimisation</subject><subject>particle swarm optimisation</subject><subject>probability</subject><subject>radar detection</subject><subject>range spread target detection</subject><subject>search problems</subject><subject>semidefinite relaxation technique</subject><subject>target scattering coefficients</subject><subject>waveform optimisation problem</subject><issn>2051-3305</issn><issn>2051-3305</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>DOA</sourceid><recordid>eNp9kD1PAkEQhi9GE4lS2l9rcTj7cV-lElCM0QYbm83c7hxZArdkb8Hw713AGButZjJ55p3JkyQ3DEYMZH23dDTiwOoR5Lw8SwYccpYJAfn5r_4yGfb9EgCYkBwkGyQfrxi2njLb9RvryaSfuKPW-XXqNsGubY_Bui6Nk9Rjt6C033hCkwb0CwqpoUD6SNgu1W7R2WB3FFGD_jq5aHHV0_C7XiXv08l8_JS9vD3OxvcvmRa5EJnWsqgIODVVUwigEoVogLgooEJWNwJFbhjHSvOSg5amakHUpURTltLwVlwls1OucbhUG2_X6PfKoVXHgfMLhT5YvSIFFcTnoWEohSxQVtSaps2rnBc5UlnHrOyUpb3re0_tTx4DdfCsomd18KwOniNfnPhPu6L9_7CaP0_4wxS4lCIu3p4WLYWIbX0XFf1x5AvySY7O</recordid><startdate>201910</startdate><enddate>201910</enddate><creator>Wang, Qing</creator><creator>Li, Meng</creator><creator>Gao, Lirong</creator><creator>Li, Kaiming</creator><creator>Chen, Hua</creator><general>The Institution of Engineering and Technology</general><general>Wiley</general><scope>IDLOA</scope><scope>24P</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>DOA</scope></search><sort><creationdate>201910</creationdate><title>Nature-inspired waveform optimisation for range spread target detection in cognitive radar</title><author>Wang, Qing ; Li, Meng ; Gao, Lirong ; Li, Kaiming ; Chen, Hua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3533-cc468e02eb8b630e7a33b0e23608a19b3a35d12a8c2720c4d8f03974ad774d2f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>antenna arrays</topic><topic>Beetle Antennae Search algorithm</topic><topic>cognitive radar</topic><topic>IET International Radar Conference (IRC 2018)</topic><topic>Kalman filters</topic><topic>modified particle swarm optimisation algorithm</topic><topic>nature-inspired algorithms</topic><topic>nature-inspired waveform optimisation approach</topic><topic>novel nature-inspired waveform optimisation framework</topic><topic>object detection</topic><topic>optimisation</topic><topic>particle swarm optimisation</topic><topic>probability</topic><topic>radar detection</topic><topic>range spread target detection</topic><topic>search problems</topic><topic>semidefinite relaxation technique</topic><topic>target scattering coefficients</topic><topic>waveform optimisation problem</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Qing</creatorcontrib><creatorcontrib>Li, Meng</creatorcontrib><creatorcontrib>Gao, Lirong</creatorcontrib><creatorcontrib>Li, Kaiming</creatorcontrib><creatorcontrib>Chen, Hua</creatorcontrib><collection>IET Digital Library (Open Access)</collection><collection>Wiley Online Library Open Access</collection><collection>CrossRef</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Journal of engineering (Stevenage, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Qing</au><au>Li, Meng</au><au>Gao, Lirong</au><au>Li, Kaiming</au><au>Chen, Hua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nature-inspired waveform optimisation for range spread target detection in cognitive radar</atitle><jtitle>Journal of engineering (Stevenage, England)</jtitle><date>2019-10</date><risdate>2019</risdate><volume>2019</volume><issue>20</issue><spage>6767</spage><epage>6771</epage><pages>6767-6771</pages><issn>2051-3305</issn><eissn>2051-3305</eissn><abstract>The waveform optimisation problem in cognitive radar is non-convex and will have sub-optimal solutions when solved by the semi-definite relaxation (SDR) technique. Here, a novel nature-inspired waveform optimisation framework is proposed for range-spread target detection in cognitive radar. First, the waveform optimisation problem is formulated using maximum a posteriori probability and Kalman filtering to estimate the target scattering coefficients. To solve this problem more accurately and efficiently, three nature-inspired algorithms (modified particle swarm optimisation algorithm, Bat Algorithm, and Beetle Antennae Search algorithm), as a nature-inspired waveform optimisation (NIWO) approach is proposed. It is demonstrated through computer simulations that the proposed NIWO approach significantly outperforms the SDR approach, showing a promising tool for waveform optimisation in cognitive radar.</abstract><pub>The Institution of Engineering and Technology</pub><doi>10.1049/joe.2019.0527</doi><tpages>5</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | antenna arrays Beetle Antennae Search algorithm cognitive radar IET International Radar Conference (IRC 2018) Kalman filters modified particle swarm optimisation algorithm nature-inspired algorithms nature-inspired waveform optimisation approach novel nature-inspired waveform optimisation framework object detection optimisation particle swarm optimisation probability radar detection range spread target detection search problems semidefinite relaxation technique target scattering coefficients waveform optimisation problem |
| title | Nature-inspired waveform optimisation for range spread target detection in cognitive radar |
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