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Author: Christer Persson Publisher: ISBN: 9781423500759 Category : Languages : en Pages : 148
Book Description
The characteristic of low probability of intercept (LPI) radar makes it difficult to intercept with conventional signal intelligence methods so new interception methods need to be developed. This thesis initially describes a simulation of a polytime phase-coded LPI signal. The thesis then introduces a method for classification of LPI radar signals. The method utilizes a parallel tree structure with three separate 'branches' to exploit the image representation formed by three separate detection methods. Each detection method output is pre-processed and features are extracted using image processing. After processing the images, they are each fed into three separate neural networks to be classified. The classification output of each neural network is then combined and fed into a fourth neural network performing the final classification. The outcome of testing shows only 53%, which might be the result of the image representation of the detection methods not being distinct enough, the pre - processing/feature extraction not being able to extract relevant information or the neural networks not being properly trained. The thesis concludes with a brief discussion about a suitable method for image processing to extract significant parameters from a LPI signal.
Author: Christer Persson Publisher: ISBN: 9781423500759 Category : Languages : en Pages : 148
Book Description
The characteristic of low probability of intercept (LPI) radar makes it difficult to intercept with conventional signal intelligence methods so new interception methods need to be developed. This thesis initially describes a simulation of a polytime phase-coded LPI signal. The thesis then introduces a method for classification of LPI radar signals. The method utilizes a parallel tree structure with three separate 'branches' to exploit the image representation formed by three separate detection methods. Each detection method output is pre-processed and features are extracted using image processing. After processing the images, they are each fed into three separate neural networks to be classified. The classification output of each neural network is then combined and fed into a fourth neural network performing the final classification. The outcome of testing shows only 53%, which might be the result of the image representation of the detection methods not being distinct enough, the pre - processing/feature extraction not being able to extract relevant information or the neural networks not being properly trained. The thesis concludes with a brief discussion about a suitable method for image processing to extract significant parameters from a LPI signal.
Author: Phillip E. Pace Publisher: Artech House ISBN: 159693235X Category : Technology & Engineering Languages : en Pages : 893
Book Description
"This comprehensive book presents LPI radar design essentials, including ambiguity analysis of LPI waveforms, FMCW radar, and phase-shift and frequency-shift keying techniques. Moreover, you find details on new OTHR modulation schemes, noise radar, and spatial multiple-input multiple-output (MIMO) systems. The book explores autonomous non-linear classification signal processing algorithms for identifying LPI modulations. It also demonstrates four intercept receiver signal processing techniques for LPI radar detection that helps you determine which time-frequency, bi-frequency technique best suits any LPI modulation of interest."--Publisher.
Author: Fernando L. Taboada Publisher: ISBN: 9781423507079 Category : Languages : en Pages : 297
Book Description
Low probability of intercept (LPI) is that property of an emitter that because of its low power, wide bandwidth, frequency variability, or other design attributes, makes it difficult to be detected or identified by means of passive intercept devices such as radar warning, electronic support and electronic intelligence receivers, In order to detect LPI radar waveforms new signal processing techniques are required This thesis first develops a MATLAB toolbox to generate important types of LPI waveforms based on frequency and phase modulation The power spectral density and the periodic ambiguity function are examined for each waveforms These signals are then used to test a novel signal processing technique that detects the waveforms parameters and classifies the intercepted signal in various degrees of noise, The technique is based on the use of parallel filter (sub-band) arrays and higher order statistics (third- order cumulant estimator) Each sub-band signal is treated individually and is followed by the third-order estimator in order to suppress any symmetrical noise that might be present, The significance of this technique is that it separates the LPI waveforms in small frequency bands, providing a detailed time-frequency description of the unknown signal, Finally, the resulting output matrix is processed by a feature extraction routine to detect the waveforms parameters Identification of the signal is based on the modulation parameters detected,
Author: Antonio F. Lime, Jr. Publisher: ISBN: 9781423507642 Category : Languages : en Pages : 186
Book Description
LPI radar is a class of radar systems that possess certain performance characteristics that make them nearly undetectable by today's digital intercept receivers, This presents a significant tactical problem in the battle space To detect these types of radar, new digital receivers that use sophisticated signal processing techniques are required This thesis investigates the use of cyclostationary processing to extract the modulation parameters from a variety of continuous-wave (CW) low-probability-of- intercept (LPI) radar waveforms, The cyclostationary detection techniques described exploit the fact that digital signals vary in time with single or multiple periodicities, because they have spectral correlation, namely, non-zero correlation between certain frequency components, at certain frequency shifts, The use of cyclostationary signal processing in a non-cooperative intercept receiver can help identify the particular emitter and can help develop electronic attacks, LPI CW waveforms examined include Frank codes, polyphase codes (Pt through P4), Frequency Modulated CW (FMCW), Costas frequencies as well as several frequency-shift- keying/phase-shift-keying (FSK/PSK) waveforms It is shown that for signal-to- noise ratios of OdB and -6 dB, the cyclostationary signal processing can extract the modulation parameters necessary in order to distinguish among the various types of LPI modulations.
Author: Publisher: ISBN: Category : Algorithms Languages : en Pages : 195
Book Description
In this thesis, an autonomous feature extraction algorithm for classification of Low Probability of Intercept (LPI) radar modulations is investigated. A software engineering architecture that allows a full investigation of various preprocessing algorithms and classification techniques is applied to a database of important LPI radar waveform modulations including Frequency Modulation Continuous Waveform (FMCW), Phase Shift Keying (PSK), Frequency Shift Keying (FSK) and combined PSK and FSK. The architecture uses time-frequency detection techniques to identify the parameters of the modulation. These include the Wigner-Ville distribution, the Choi-Williams distribution and quadrature mirror filtering. Autonomous time-frequency image cropping algorithm is followed by a feature extraction algorithm based on principal components analysis. Classification networks include the multilayer perceptron, the radial basis function and the probabilistic neural networks. Lastly, using image processing techniques on images obtained by the Wigner-Ville distribution and the Choi-Williams distribution, two autonomous extraction algorithms are investigated to derive the significant modulation parameters of polyphase coded LPI radar waveform modulations.
Author: Publisher: ISBN: Category : Languages : en Pages : 186
Book Description
LPI radar is a class of radar systems that possess certain performance characteristics that make them nearly undetectable by today's digital intercept receivers, This presents a significant tactical problem in the battle space To detect these types of radar, new digital receivers that use sophisticated signal processing techniques are required This thesis investigates the use of cyclostationary processing to extract the modulation parameters from a variety of continuous-wave (CW) low-probability-of- intercept (LPI) radar waveforms, The cyclostationary detection techniques described exploit the fact that digital signals vary in time with single or multiple periodicities, because they have spectral correlation, namely, non-zero correlation between certain frequency components, at certain frequency shifts, The use of cyclostationary signal processing in a non-cooperative intercept receiver can help identify the particular emitter and can help develop electronic attacks, LPI CW waveforms examined include Frank codes, polyphase codes (Pt through P4), Frequency Modulated CW (FMCW), Costas frequencies as well as several frequency-shift-keying/phase-shift-keying (FSK/PSK) waveforms It is shown that for signal-to-noise ratios of OdB and -6 dB, the cyclostationary signal processing can extract the modulation parameters necessary in order to distinguish among the various types of LPI modulations.
Author: Publisher: ISBN: Category : Radar Languages : en Pages : 162
Book Description
LPI radar is a class of radar systems possessing certain performance characteristics that make them nearly undetectable by today's digital intercept receivers. This presents a significant tactical problem in the battle space. To detect these types of radar, new digital receivers that use sophisticated signal processing techniques are required. This thesis investigates the use of cyclostationary processing to extract the modulation parameters from a variety of continuous-wave (CW) low-probability-of-intercept (LPI) radar waveforms. The cyclostationary detection techniques described exploit the fact that digital signals vary in time with single or multiple periodicity, owing to their spectral correlation, namely non-zero correlation between certain frequency components, at certain frequency shifts. The use of cyclostationary signal processing in a non-cooperative intercept receiver can help identify the particular emitter and aid in the development of electronic attack signals. LPI CW waveforms examined include Frank codes, P1 through P4, Frequency Modulated CW (FMCW), Costas frequencies as well as several frequency-shift-keying/phase-shift-keying (FSK/PSK) waveforms. This thesis show that for signal-to-noise ratios of 0 dB and -6 dB, the cyclostationary signal processing can extract the modulation parameters necessary in order to distinguish between the various types of LPI modulations.
Author: Pedro Jarpa Publisher: ISBN: 9781423507475 Category : Languages : en Pages : 174
Book Description
Low Probability of Intercept (LPI) radars are a class of radar systems that possess certain performance% characteristics causing them to be nearly undetectable by most modern digital intercept receivers, Consequently, LPI radar systems can operate undetected until the intercept receiver is much closer than the radar's target detector, The enemy is thus faced with a significant problem To detect these types of radar, new direct digital receivers that use sophisticated signal processing are required, This thesis describes a novel signal processing architecture, and shows simulation results for a number of LPI waveforms. The LPI signal detection receiver is based on Quadrature Minor Filter Bank (QMFB) Tree processing and orthogonal wavelet techniques to decompose the input waveform into components representing the signal energy in rectangular "tiles" in the time-frequency plane, By analyzing the outputs at different layers of the tree it is possible to do feature extraction, identify and classify the LPI waveform parameters, and distinguish among the various LPI signal modulations Waveforms used as input signals to the detection algorithm include Frequency Modulated Continuous Wave, Polyphase Codes, Costas Codes and Frequency Shift Keying/Phase Shift Keying waveforms. The output matrices resulting from the most relevant layers of the QMFB tree processing are examined and the LPI modulation parameters are extracted under various signal-to-noise ratios,
Author: Fulvio Gini Publisher: John Wiley & Sons ISBN: 0470283149 Category : Science Languages : en Pages : 287
Book Description
Discover the technology for the next generation of radar systems Here is the first book that brings together the key concepts essential for the application of Knowledge Based Systems (KBS) to radar detection, tracking, classification, and scheduling. The book highlights the latest advances in both KBS and radar signal and data processing, presenting a range of perspectives and innovative results that have set the stage for the next generation of adaptive radar systems. The book begins with a chapter introducing the concept of Knowledge Based (KB) radar. The remaining nine chapters focus on current developments and recent applications of KB concepts to specific radar functions. Among the key topics explored are: Fundamentals of relevant KB techniques KB solutions as they apply to the general radar problem KBS applications for the constant false-alarm rate processor KB control for space-time adaptive processing KB techniques applied to existing radar systems Integrated end-to-end radar signals Data processing with overarching KB control All chapters are self-contained, enabling readers to focus on those topics of greatest interest. Each one begins with introductory remarks, moves on to detailed discussions and analysis, and ends with a list of references. Throughout the presentation, the authors offer examples of how KBS works and how it can dramatically improve radar performance and capability. Moreover, the authors forecast the impact of KB technology on future systems, including important civilian, military, and homeland defense applications. With chapters contributed by leading international researchers and pioneers in the field, this text is recommended for both students and professionals in radar and sonar detection, tracking, and classification and radar resource management.
Author: Publisher: ISBN: Category : Algorithms Languages : en Pages : 78
Book Description
Three autonomous cropping and feature extraction algorithms are examined that can be used for classification of low probability of intercept radar modulations using time-frequency (T-F) images. The first approach, Erosion Dilation Adaptive Binarization (EDAB), uses erosion and a new adaptive threshold binarization algorithm embedded within a recursive dilation process to determine the modulation energy centroid (radar's carrier frequency) and properly place a fixed-width cropping window. The second approach, Marginal Frequency Adaptive Binarization (MFAB), uses the marginal frequency distribution and the adaptive threshold binarization algorithm to determine the start and stop frequencies of the modulation energy to locate and adapt the size of the cropping window. The third approach, Fast Image Filtering, uses the fast Fourier transform and a Gaussian lowpass filter to isolate the modulation energy. The modulation is then cropped from the original T-F image and the adaptive binarization algorithm is used again to compute a binary feature vector for input into a classification network. The binary feature vector allows the image detail to be preserved without overwhelming the classification network that follows. A multi-layer perceptron and a radial basis function network are used for classification and the results are compared. Classification results for nine simulated radar modulations are shown to demonstrate the three feature-extraction approaches and quantify the performance of the algorithms. It is shown that the best results are obtained using the Choi-Williams distribution followed by the MFAB algorithm and a multi-layer perceptron. This setup produced an overall percent correct classification (Pcc) of 87.2% for testing with noise variation and 77.8% for testing with modulation variation. In an operational context, the ability to process and classify LPI signals autonomously allows the operator in the field to receive real-time results.
Author: Christer Persson
Author: Christer Persson
Author: Phillip E. Pace
Author: Fernando L. Taboada
Author: Antonio F. Lime, Jr.
Author: 
Author: 
Author: 
Author: Pedro Jarpa
Author: Fulvio Gini
Author: