Signal Interpretation of Multifunction Radars: Modeling and Statistical Signal Processing With Stochastic Context Free Grammar

Wang, A.; Krishnamurthy, Vikram

doi:10.1109/tsp.2007.908949

Cited by 51 publications

(18 citation statements)

References 29 publications

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“…In general, Kalman Filter-based models are sophisticated and require significant computation. The fixed length Markov model is another commonly used technique for time series analysis [12]. Examples of fixed order Markov models include the Markov Chain, Hidden Markov Model, etc.…”

Section: Related Workmentioning

confidence: 99%

“…Symbolic modeling and processing have several advantages over continuous measurements and models, including: 1) sensor data is often discrete (i.e., certain radar systems [12]); 2) environments that are modeled with discrete states that have clear physical interpretations are natural and easy for humans to interpret (e.g., eruption or no eruption vs. vibration measurements); and 3) data compression techniques, which we use to reduce the size of the observations, typically require discrete state representations.…”

Section: Introductionmentioning

confidence: 99%

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Detecting time-related changes in Wireless Sensor Networks using symbol compression and Probabilistic Suffix Trees

Thomason

Parker

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Our research focuses on anomaly detection problems in unknown environments using Wireless Sensor Networks (WSN). We are interested in detecting two types of abnormal events: sensory level anomalies (e.g., noise in an office without lights on) and time-related anomalies (e.g., freezing temperature in a mid-summer day). We present a novel, distributed, machine learning based anomaly detector that is able to detect timerelated changes. It consists of three components. First, a Fuzzy Adaptive Resonance Theory (ART) neural network classifier is used to label multi-dimensional sensor data into discrete classes and detect sensory level anomalies. Over time, the labeled classes form a sequence of classes. Next, a symbol compressor is used to extract the semantic meaning of the temporal sequence. Finally, a Variable Memory Markov (VMM) model in the form of a Probabilistic Suffix Tree (PST) is used to model and detect time-related anomalies in the environment. To our knowledge, this is the first work that analyzes/models the temporal sensor data using a symbol compressor and PST in a WSN. Our proposed detection algorithm is distributed, "light-weight", and practical for resource constrained sensor nodes. We verify the proposed approach using a volcano monitoring dataset. Our results show that this approach yields the same performance as the traditional Markov models with much less cost.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting time-related changes in Wireless Sensor Networks using symbol compression and Probabilistic Suffix Trees

Thomason

Parker

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…However, constructing the redundant dictionary requires a level of prior information, and the computational time required for seeking linear combinations in the dictionary must be improved. In addition, an emitter signal model was constructed using knowledgebased statistical signal processing with a syntactic domain knowledge representation to recognize radar emitters [8,2 Mathematical Problems in Engineering 9]. However, the method requires sufficient and correct prior information to accurately identify a radar emitter, which is relatively difficult to obtain under modern warfare conditions.…”

Section: Introductionmentioning

confidence: 99%

Radar Signal Emitter Recognition Based on Combined Ensemble Empirical Mode Decomposition and the Generalized S‐Transform

Liu

Feng

Yin

et al. 2019

Mathematical Problems in Engineering

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Present radar signal emitter recognition approaches suffer from a dependency on prior information. Moreover, modern emitter recognition must meet the challenges associated with low probability of intercept technology and other obscuration methodologies based on complex signal modulation and must simultaneously provide a relatively strong ability for extracting weak signals under low SNR values. Therefore, the present article proposes an emitter recognition approach that combines ensemble empirical mode decomposition (EEMD) with the generalized S-transform (GST) for promoting enhanced recognition ability for radar signals with complex modulation under low signal-to-noise ratios in the absence of prior information. The results of Monte Carlo simulations conducted using various mixed signals with additive Gaussian white noise are reported. The results verify that EEMD suppresses the occurrence of mode mixing commonly observed using standard empirical mode decomposition. In addition, EEMD is shown to extract meaningful signal features even under low SNR values, which demonstrates its ability to suppress noise. Finally, EEMD-GST is demonstrated to provide an obviously better time-frequency focusing property than that of either the standard S-transform or the short-time Fourier transform.

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“…Radar emitter classification is an important subject in electromagnetism surveillance [1][2][3], which is widely used in the airborne electronic support (ES) system [4]. It is a clustering problem which is used to process the sampled pulses radiated from unknown radars.…”

Section: Introductionmentioning

confidence: 99%

“…Electronic warfare (EW) is an important part of NCW, which can be broadly defined as any military action with the objective of controlling the electromagnetic spectrum [29]. A pivotal aspect of EW is the airborne ES system, whose goal is to protect itself or the important equipment from radar-equipped threat [4]. The ES system intercepts and analyzes the radar emitter pulses, and then it sends the processed result to the control center to help the command staff to make decision.…”

Section: Introductionmentioning

confidence: 99%

An Online Multisensor Data Fusion Framework for Radar Emitter Classification

Zhou

Wang

Cheng

et al. 2016

International Journal of Aerospace Engineering

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Radar emitter classification is a special application of data clustering for classifying unknown radar emitters in airborne electronic support system. In this paper, a novel online multisensor data fusion framework is proposed for radar emitter classification under the background of network centric warfare. The framework is composed of local processing and multisensor fusion processing, from which the rough and precise classification results are obtained, respectively. What is more, the proposed algorithm does not need prior knowledge and training process; it can dynamically update the number of the clusters and the cluster centers when new pulses arrive. At last, the experimental results show that the proposed framework is an efficacious way to solve radar emitter classification problem in networked warfare.

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Signal Interpretation of Multifunction Radars: Modeling and Statistical Signal Processing With Stochastic Context Free Grammar

Cited by 51 publications

References 29 publications

Detecting time-related changes in Wireless Sensor Networks using symbol compression and Probabilistic Suffix Trees

Detecting time-related changes in Wireless Sensor Networks using symbol compression and Probabilistic Suffix Trees

Radar Signal Emitter Recognition Based on Combined Ensemble Empirical Mode Decomposition and the Generalized S‐Transform

An Online Multisensor Data Fusion Framework for Radar Emitter Classification

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