Passive radar array processing with non-uniform linear arrays for ground target's detection and localization

The problem of ground target detection with passive radars is considered. The design of an antenna array based on commercial elements is presented, based on a non-uniform linear array optimized according to sidelobe level requirements. Array processing techniques are applied in the cross-ambiguity f...

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Detalhes bibliográficos
Autores: Rey Maestre, Nerea del|||0000-0002-2933-8940, Mata Moya, David Anastasio de la|||0000-0002-3612-9418, Jarabo Amores, María del Pilar|||0000-0001-5841-6613, Gómez del Hoyo, Pedro José, Bárcena Humanes, José Luis, Rosado Sanz, Javier|||0000-0002-9850-2943
Formato: artículo
Fecha de publicación:2017
País:España
Recursos:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/59750
Acesso em linha:http://hdl.handle.net/10017/59750
https://dx.doi.org/10.3390/rs9070756
Access Level:acceso abierto
Palavra-chave:Passive radar
Array
Beamforming
Non-ULA
DOA
Detection
Telecomunicaciones
Telecommunication
Descrição
Resumo:The problem of ground target detection with passive radars is considered. The design of an antenna array based on commercial elements is presented, based on a non-uniform linear array optimized according to sidelobe level requirements. Array processing techniques are applied in the cross-ambiguity function domain to exploit integration gain, system resolution and the sparsity of targets in this domain. A modified two-stage detection scheme is described, which is based on a previously-published one by other authors. All of these contributions are validated in a real semiurban scenario, proving the capabilities of detection, the direction of arrival estimation and the tracking of ground targets in the presence of big buildings that generate strong clutter returns. Detection performance is validated through the probability of false alarm and the probability of detection estimation with specified estimation errors.