Knowledge‐Based Radar Signal and Data Processing: A Tutorial Overview

Capraro, Gerard T.; Farina, A.; Griffiths, Hugh; Wicks, Michael C.

doi:10.1002/9780470283158.ch3

Cited by 8 publications

(14 citation statements)

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“…R ww is estimated from available sensor data, or from receiver output signals with the receiver input blocked, or other means. R αα is estimated adaptively using previously-recorded data [20], a priori information [21], structured covariance techniques [22], analytical modeling techniques [23,24], or combinations of these methods. In fact, the methodology proposed here benefits directly from the large number of covariance estimation techniques developed in the adaptive array and space-time adaptive processing (STAP) areas.…”

Section: Waveform Selection Using Mutual Informationmentioning

confidence: 99%

Waveform diversity via mutual information

Roman

Davis

Garnham

et al. 2009

Digital Signal Processing

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Section: Waveform Selection Using Mutual Informationmentioning

confidence: 99%

Waveform diversity via mutual information

Roman

Davis

Garnham

et al. 2009

Digital Signal Processing

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“…Recently, knowledge-aided space-time adaptive processing (KA-STAP) has received much attention due to its potential of utilizing some available prior knowledge [11]- [14]. One focus of the KA-STAP is how the prior knowledge can be incorporated to formulate the detection problem [12].…”

Section: Introductionmentioning

confidence: 99%

Knowledge-aided parametric GLRT for space-time adaptive processing

Wang

Himed³

2010

2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers

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In this paper, we consider knowledge-aided spacetime adaptive processing (KA-STAP) with a parametric approach, where disturbances in both test and training signals are modeled as a multichannel auto-regressive (AR) model. The a priori knowledge is incorporated into the detection problem through a stochastic signal model, where the spatial covariance matrix of the disturbance is assumed random. According to this model, a Bayesian version of the parametric generalized likelihood ratio test (PGLRT) is developed in a two-step approach, which is referred to as the KA-PGLRT. Interestingly, the KA-PGLRT employs a colored loading approach for estimation of the spatial covariance matrix of the test signal. Simulation results show that the KA-PGLRT can obtain better detection performance over other parametric detectors.

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“…The agent first checks whether its local observations agree with the proposed assessment, say P x (line 2). If the agent agrees with the assessment P x , then it will just forward the message randomly to another agent, increasing ♯agree (lines [3][4][5][6]. If the agent disagrees, status is changed to "CHALLENGE".…”

Section: Distributed Assessment Protocolmentioning

confidence: 99%

“…Capraro et al use symbolic reasoning to refine perceptions at data level [5]: instead of allowing for higher level conclusions, a priori knowledge related to the specific sensor attitude is used to customize and tune the feature extraction process, thus reducing the effects of the uncertainty of information sources. With respect to these approaches, our research will pursue the goal of giving high level events a unique symbolic representation, and obtaining new conclusions with logical reasoning, distributing the process over a team of agents.…”

Section: Centralized Situation Assessmentmentioning

confidence: 99%

“…level 0 and 1 of the JDL model [27]), there is a strong need for techniques that can process data at a higher level thus performing Situation Assessment. Many Situation Assessment approaches have been proposed in the literature [22,26,5] but they usually propose a centralized architecture that can not be easily adapted to decentralized settings such as the ones we are interested here.…”

Section: Introductionmentioning

confidence: 99%

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Cooperative situation assessment in a maritime scenario

Farinelli

Nardi

Pigliacampo³

et al. 2012

Int. J. Intell. Syst.

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In large-scale, complex domains such as space defense and security systems, situation assessment and decision making are evolving from centralized models to high-level, net-centric models. In this context, collaboration among the many actors involved in the situation assessment process is critical in order to achieve a prompt reaction as needed in the operational scenario.In this paper, we propose a multi-agent based approach to situation assessment, where agents cooperate by sharing local information to reach a common and coherent assessment of situations. Specifically, we characterize situation assessment as a classification process based on OWL ontology reasoning, and we provide a protocol for cooperative multi-agent situation assessment, which allows the agents to achieve coherent high level conclusions. We validate our approach in a real maritime surveillance scenario, where our prototype system effectively supports the user in detecting and classifying potential threats; moreover, our distributed solution performs comparably to a centralized method, while preserving independence of decision makers and dramatically reducing the amount of communication required.

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Knowledge‐Based Radar Signal and Data Processing: A Tutorial Overview

Cited by 8 publications

References 0 publications

Waveform diversity via mutual information

Waveform diversity via mutual information

Knowledge-aided parametric GLRT for space-time adaptive processing

Cooperative situation assessment in a maritime scenario

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