International Conference on Radar Systems (Radar 2017) 2017
DOI: 10.1049/cp.2017.0386
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Cognitive Radar Experiments with CODIR

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Cited by 9 publications
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“…From an interceptor’s perspective, an actual intercepted CR work mode sample encloses a dedicated serial pulse to serve a certain radar function. This sample can also be represented by a combination of inter‐ and intra‐pulse modulations on pulse parameters like PRI, RF, PW and MOP [11, 13, 37–39] (see Figure 2). For a CR that optimises the corresponding modulation parameters on a dwell‐to‐dwell basis, it would present parametric agility in transmitted signals from pulse group to pulse group.…”
Section: Problem Formulationmentioning
confidence: 99%
“…From an interceptor’s perspective, an actual intercepted CR work mode sample encloses a dedicated serial pulse to serve a certain radar function. This sample can also be represented by a combination of inter‐ and intra‐pulse modulations on pulse parameters like PRI, RF, PW and MOP [11, 13, 37–39] (see Figure 2). For a CR that optimises the corresponding modulation parameters on a dwell‐to‐dwell basis, it would present parametric agility in transmitted signals from pulse group to pulse group.…”
Section: Problem Formulationmentioning
confidence: 99%
“…1. The dynamics of the cognitive radar -target interaction and the inverse learning problem A detailed account of the general mathematical frameworks for cognitive radars are available in [9], [10], [11], [6], [12], [13], [14]. Figure 1, a generalized model adopted from [14], [5], depicts the interaction between cognitive radar and target which include: (i) the scene, which comprises the radar target and the environment, typically modelled using the parameterized transition probability, p αt (•), of the state of the target, x k , (ii) the sensor, that consists of a transceiver, which illuminates the environment and senses the reflections -modelled using a time varying sensor parameter, β t , and an observation likelihood, p β t (•), (iii) the processor, which transforms the observed data to the perception of the scene -typically modelled using a Bayesian tracker, that estimates the posterior belief π k , of target stats given past observation of the sensor, (iv) the controller, which decides the actions to be taken by the sensor and processor modules, taking into account the perception of the scene, produced by the processor, and the controller function is modelled as a constrained optimization problem.…”
Section: Introductionmentioning
confidence: 99%