Non-self-embedding context-free grammars for multifunction radar modeling ~ electronic warfare application

Visnevski, Nikita; Dilkes, Fred A.; Haykin, S.; Krishnamurthy, Vikram

doi:10.1109/radar.2005.1435911

Cited by 14 publications

(9 citation statements)

References 7 publications

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“…They supposed that most CFGs used to describe radar models are non-self-embedding (NSE). Visnevski et al 12 considered the signals from an MFR to be strings from some formal languages that can be modeled by a compact syntactic representation, which is called the NSE CFG, and made use of the theory of finite-state automata (FSA) to the electronic warfare (EW) signal processing of MFRs. Considering the aspect of radar words extracting, in Refs.…”

Section: Introductionmentioning

confidence: 99%

Multi-function radar emitter identification based on stochastic syntax-directed translation schema

Liu

Sun

et al. 2014

Chinese Journal of Aeronautics

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To cope with the problem of emitter identification caused by the radar words' uncertainty of measured multi-function radar emitters, this paper proposes a new identification method based on stochastic syntax-directed translation schema (SSDTS). This method, which is deduced from the syntactic modeling of multi-function radars, considers the probabilities of radar phrases appearance in different radar modes as well as the probabilities of radar word errors occurrence in different radar phrases. It concludes that the proposed method can not only correct the defective radar words by using the stochastic translation schema, but also identify the real radar phrases and working modes of measured emitters concurrently. Furthermore, a number of simulations are presented to demonstrate the identification capability and adaptability of the SSDTS algorithm. The results show that even under the condition of the defective radar words distorted by noise, the proposed algorithm can infer the phrases, work modes and types of measured emitters correctly.

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Section: Introductionmentioning

confidence: 99%

Multi-function radar emitter identification based on stochastic syntax-directed translation schema

Liu

Sun

et al. 2014

Chinese Journal of Aeronautics

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“…It is advantageous to model complex patterns in such a way to benefit from the finite state automaton (FSA) theory background [25]. Owing to this evidence, FSA as ND 0 are used obtaining an adaptive finite automaton (AFA), a Turing-powerful device [22] indicated by Expression 1.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Modeling of Digital Straightness Applied to Geometric Representation Enhancement

Neto¹,

Hirakawa²,

Massola³

2010

IJCA

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For representing of digitized straight line segments (DSLS), each of the available research techniques has its advantages and appropriate applications considering the complexities of real world scenarios. Based on adaptive finite automaton (AFA), we propose an alternative paradigm that is convenient for problems modeled by a set of rules. The main objective is to investigate the representation of DSLS through adaptivity, aiming to exploit the ability to represent tolerances, scalability, errors and deviations in angle or in length of the mentioned segments through a device called adaptive DSLS, for short ADSLS. Consequently, ADSLS is shown to be effective to represent segments; furthermore, it is able to adapt, reacting to circumstance stimuli in a single pass.

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“…In (15), SCFG is introduced as a framework to model MFRs' signal and it is shown that MFRs' dynamic behavior can be explicitly described using a finite set of rules corresponding to the production rules of the SCFG. SCFG has several potential advantages: i) SCFG is a compact formal representation that forms a homogeneous basis for modeling and storing complex system domain knowledge, and in which it is simpler and more natural for the model designer to express the control rules of MFR (16). ii) SCFG is more efficient in modeling hidden branching processes when compared to a stochastic regular grammars or hidden Markov models with the same number of parameters.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Interpretation of Multifunction Radars with Stochastic Grammar

Wang

Krishnamurthy

2008

2008 IEEE Aerospace Conference

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Multifunction Radars (MFRs) are sophisticated sensors with complex dynamical modes that are widely used in surveillance and tracking. Because of their agility, a new solution to the interpretation of radar signal is critical to aircraft survivability and successful mission completion. In this paper, we introduce a knowledge-based statistical signal processing technique that allows syntactic representation of domain expert knowledge. In particular, we model MFRs as systems that speak" a language that can be characterized by a Markov modulated stochastic context free grammar(SCFG). We demonstrate that SCFG, modulated by a Markov chain, serves as an adequate knowledge representation of MFRs' dynamics. We then deal with the statistical signal interpretation, the threat evaluation, of the MFR signal. Two statistical estimation algorithms for MFR signal are derived -a maximum likelihood sequence estimator to estimate the system state, and a maximum likelihood parameter estimator to infer the system parameter values. Based on the interpreted radar signal, the interaction dynamics between the MFR and the target is studied and the control of the aircraft's maneuvering models is implemented.

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Non-self-embedding context-free grammars for multifunction radar modeling ~ electronic warfare application

Cited by 14 publications

References 7 publications

Multi-function radar emitter identification based on stochastic syntax-directed translation schema

Multi-function radar emitter identification based on stochastic syntax-directed translation schema

Adaptive Modeling of Digital Straightness Applied to Geometric Representation Enhancement

Modeling and Interpretation of Multifunction Radars with Stochastic Grammar

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