Automatic target recognition using passive bistatic radar signals.
type: Dataset
description: We present the design, development, and test of three novel, distinct automatic target recognition (ATR) systems for the recognition of airplanes and, more specifically, non-cooperative airplanes, i.e. airplanes that do not provide information when interrogated, in the framework of passive bistatic radar systems. Passive bistatic radar systems use one or more illuminators of opportunity (already present in the field), with frequencies up to 1 GHz for the transmitter part of the systems considered here, and one or more receivers, deployed by the persons managing the system, and not co-located with the transmitters. The sole source of information are the signal scattered on the airplane and the direct-path signal that are collected by the receiver, some basic knowledge about the transmitter, and the geometrical bistatic radar configuration. The three distinct ATR systems that we built respectively use the radar images, the bistatic complex radar cross-section (BS-RCS), and the bistatic radar cross-section (BS-RCS) of the targets. We use data acquired either on scale models of airplanes placed in an anechoic, electromagnetic chamber or on real-size airplanes using a bistatic testbed consisting of a VOR transmitter and a software-defined radio (SDR) receiver, located near Orly airport, France. We describe the radar phenomenology pertinent for the problem at hand, as well as the mathematical underpinnings of the derivation of the bistatic RCS values and of the construction of the radar images.For the classification of the observed targets into pre-defined classes, we use either extremely randomized trees or subspace methods. A key feature of our approach is that we break the recognition problem into a set of sub-problems by decomposing the parameter space, which consists of the frequency, the polarization, the aspect angle, and the bistatic angle, into regions. We build one recognizer for each region. We first validate the extra-trees method on the radar images of the MS…
identifier: NNT: 2013SUPL0009
issued: 2026-05-05T22:09:33.879843
language: en
modified: 2026-05-05T22:09:33.879847
contact point: Organization
  fn: CCSD
keyword: arbres-extremement-aleatoires
keyword: automatic-target-recognition-atr
keyword: classification
keyword: complex-radar-cross-section
keyword: extremely-randomized-treesextra-trees
keyword: infoeu-reposemanticsdoctoralthesis
keyword: methodes-des-sous-espaces
keyword: non-cooperative-target-recognition
keyword: passive-bistatic-radar
keyword: radar-passif-bistatique
keyword: radio-logicielle
keyword: reconnaissance-automatique-des-cibles
keyword: reconnaissance-de-cibles-non-cooperatives
keyword: software-defined-radio-sdr
keyword: spiotherengineering-sciences-physicsother
keyword: subspace-methods
keyword: surface-equivalente-radar-complexe
keyword: theses
landing page: https://theses.hal.science/tel-00963601

Automatic target recognition using passive bistatic radar signals.
type: Distribution
format: HTML
issued: 2026-05-05T22:09:33.926829
modified: 2026-05-05T22:09:33.860664
access URL: https://theses.hal.science/tel-00963601

test_moissonnage_selune
type: Agent