The Experts below are selected from a list of 17400 Experts worldwide ranked by ideXlab platform
Yuanhao Huang - One of the best experts on this subject based on the ideXlab platform.
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a uwb Radar Signal Processing platform for real time human respiratory feature extraction based on four segment linear waveform model
IEEE Transactions on Biomedical Circuits and Systems, 2016Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao HuangAbstract:This paper presents an ultra-wideband (UWB) impulse-radio Radar Signal Processing platform used to analyze human respiratory features. Conventional Radar systems used in human detection only analyze human respiration rates or the response of a target. However, additional respiratory Signal information is available that has not been explored using Radar detection. The authors previously proposed a modified raised cosine waveform (MRCW) respiration model and an iterative correlation search algorithm that could acquire additional respiratory features such as the inspiration and expiration speeds, respiration intensity, and respiration holding ratio. To realize real-time respiratory feature extraction by using the proposed UWB Signal Processing platform, this paper proposes a new four-segment linear waveform (FSLW) respiration model. This model offers a superior fit to the measured respiration Signal compared with the MRCW model and decreases the computational complexity of feature extraction. In addition, an early-terminated iterative correlation search algorithm is presented, substantially decreasing the computational complexity and yielding negligible performance degradation. These extracted features can be considered the compressed Signals used to decrease the amount of data storage required for use in long-term medical monitoring systems and can also be used in clinical diagnosis. The proposed respiratory feature extraction algorithm was designed and implemented using the proposed UWB Radar Signal Processing platform including a Radar front-end chip and an FPGA chip. The proposed Radar system can detect human respiration rates at 0.1 to 1 Hz and facilitates the real-time analysis of the respiratory features of each respiration period.
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human respiratory feature extraction on an uwb Radar Signal Processing platform
International Symposium on Circuits and Systems, 2013Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao HuangAbstract:This paper presents a human respiratory feature extraction algorithm and its implementation on an ultra-wideband (UWB) impulse-radio Radar Signal Processing platform. The conventional human detection algorithms only extract the respiration rate by the Radar system. However, there is more information that is never explored in the Radar-detected respiratory Signals. Thus, this study proposes a modified raised cosine waveform as the respiration model and an iterative feature extraction algorithm to acquire more respiratory features, such as inspiration and expiration speeds, respiration intensity, and respiration holding ratio. These extracted features can be regarded as the compressed Signals for the long-term remote medical monitoring system. The proposed respiratory feature extraction algorithm is designed and implemented on a Radar Signal Processing platform with an Radar front-end chip, an ARM processor, and an FPGA chip. The proposed circuit can detect human respiratory Signals from 0.1 to 1 Hz rate and analyze the respiratory features for each period of the respiratory Signal.
Petre Stoica - One of the best experts on this subject based on the ideXlab platform.
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model order selection rules for covariance structure classification in Radar
IEEE Transactions on Signal Processing, 2017Co-Authors: Vincenzo Carotenuto, Danilo Orlando, Petre StoicaAbstract:The adaptive classification of the interference covariance matrix structure for Radar Signal Processing applications is addressed in this paper. This represents a key issue because many detection architectures are synthesized assuming a specific covariance structure which may not necessarily coincide with the actual one due to the joint action of the system and environment uncertainties. The considered classification problem is cast in terms of a multiple hypotheses test with some nested alternatives and the theory of model order selection (MOS) is exploited to devise suitable decision rules. Several MOS techniques, such as the Akaike, Takeuchi, and Bayesian information criteria, are adopted and the corresponding merits and drawbacks are discussed. At the analysis stage, illustrating examples for the probability of correct model selection are presented showing the effectiveness of the proposed rules.
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mimo Radar Signal Processing
2008Co-Authors: Jian Li, Petre StoicaAbstract:PREFACE. CONTRIBUTORS. 1 MIMO Radar - Diversity Means Superiority (Jian Li and Petre Stoica). 1.1 Introduction. 1.2 Problem Formulation. 1.3 Parameter Identifiability. 1.4 Nonparametric Adaptive Techniques for Parameter Estimation. 1.5 Parametric Techniques for Parameter Estimation. 1.6 Transmit Beampattern Designs. 1.7 Conclusions. Appendix IA Generalized Likelihood Ratio Test. Appendix 1B Lemma and Proof. Acknowledgments. References. 2 MIMO Radar: Concepts, Performance Enhancements, and Applications (Keith W. Forsythe and Daniel W. Bliss). 2.1 Introduction. 2.2 Notation. 2.3 MIMO Radar Virtual Aperture. 2.4 MIMO Radar in Clutter-Free Environments. 2.5 Optimality of MIMO Radar for Detection. 2.6 MIMO Radar with Moving Targets in Clutter: GMTI Radars. 2.7 Summary. Appendix 2A A Localization Principle. Appendix 2B Bounds on R(N). Appendix 2C An Operator Norm Inequality. Appendix 2D Negligible Terms. Appendix 2E Bound on Eigenvalues. Appendix 2F Some Inner Products. Appendix 2G An Invariant Inner Product. Appendix 2H Kronecker and Tensor Products. Acknowledgments. References. 3 Generalized MIMO Radar Ambiguity Functions (Geoffrey San Antonio, Daniel R. Fuhrmann, and Frank C. Robey). 3.1 Introduction. 3.2 Background. 3.3 MIMO Signal Model. 3.4 MIMO Parametric Channel Model. 3.5 MIMO Ambiguity Function. 3.6 Results and Examples. 3.7 Conclusion. References. 4 Performance Bounds and Techniques for Target Localization Using MIMO Radars (Joseph Tabrikian). 4.1 Introduction. 4.2 Problem Formulation. 4.3 Properties. 4.4 Target Localization. 4.5 Performance Lower Bound for Target Localization. 4.6 Simulation Results. 4.7 Discussion and Conclusions. Appendix 4A Log-Likelihood Derivation. Appendix 4B Transmit-Receive Pattern Derivation. Appendix 4C Fisher Information Matrix Derivation. References. 5 Adaptive Signal Design For MIMO Radars (Benjamin Friedlander). 5.1 Introduction. 5.2 Problem Formulation. 5.3 Estimation. 5.4 Detection. 5.5 MIMO Radar and Phased Arrays. Appendix 5A Theoretical SINR Calculation. References. 6 MIMO Radar Spacetime Adaptive Processing and Signal Design (Chun-Yang Chen and P. P. Vaidyanathan). 6.1 Introduction. 6.2 The Virtual Array Concept. 6.3 Spacetime Adaptive Processing in MIMO Radar. 6.4 Clutter Subspace in MIMO Radar. 6.5 New STAP Method for MIMO Radar. 6.6 Numerical Examples. 6.7 Signal Design of the STAP Radar System. 6.8 Conclusions. Acknowledgments. References. 7 Slow-Time MIMO SpaceTime Adaptive Processing (Vito F. Mecca, Dinesh Ramakrishnan, Frank C. Robey, and Jeffrey L. Krolik). 7.1 Introduction. 7.2 SIMO Radar Modeling and Processing. 7.3 Slow-Time MIMO Radar Modeling. 7.4 Slow-Time MIMO Radar Processing. 7.5 OTHr Propagation and Clutter Model. 7.6 Simulations Examples. 7.7 Conclusion. Acknowledgment. References. 8 MIMO as a Distributed Radar System (H. D. Griffiths, C. J. Baker, P. F. Sammartino, and M. Rangaswamy). 8.1 Introduction. 8.2 Systems. 8.3 Performance. 8.4 Conclusions. Acknowledgment. References. 9 Concepts and Applications of A MIMO Radar System with Widely Separated Antennas (Hana Godrich, Alexander M. Haimovich, and Rick S. Blum). 9.1 Background. 9.2 MIMO Radar Concept. 9.3 NonCoherent MIMO Radar Applications. 9.4 Coherent MIMO Radar Applications. 9.5 Chapter Summary. Appendix 9A Deriving the FIM. Appendix 9B Deriving the CRLB on the Location Estimate Error. Appendix 9C MLE of Time Delays - Error Statistics. Appendix 9D Deriving the Lowest GDOP for Special Cases. Acknowledgments. References. 10 SpaceTime Coding for MIMO Radar (Antonio De Maio and Marco Lops). 10.1 Introduction. 10.2 System Model. 10.3 Detection In MIMO Radars. 10.4 Spacetime Code Design. 10.5 The Interplay Between STC and Detection Performance. 10.6 Numerical Results. 10.7 Adaptive Implementation. 10.8 Conclusions. Acknowledgment. References. INDEX.
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mimo Radar Signal Processing
2008Co-Authors: Jian Li, Petre StoicaAbstract:PREFACE. CONTRIBUTORS. 1 MIMO Radar - Diversity Means Superiority (Jian Li and Petre Stoica). 1.1 Introduction. 1.2 Problem Formulation. 1.3 Parameter Identifiability. 1.4 Nonparametric Adaptive Techniques for Parameter Estimation. 1.5 Parametric Techniques for Parameter Estimation. 1.6 Transmit Beampattern Designs. 1.7 Conclusions. Appendix IA Generalized Likelihood Ratio Test. Appendix 1B Lemma and Proof. Acknowledgments. References. 2 MIMO Radar: Concepts, Performance Enhancements, and Applications (Keith W. Forsythe and Daniel W. Bliss). 2.1 Introduction. 2.2 Notation. 2.3 MIMO Radar Virtual Aperture. 2.4 MIMO Radar in Clutter-Free Environments. 2.5 Optimality of MIMO Radar for Detection. 2.6 MIMO Radar with Moving Targets in Clutter: GMTI Radars. 2.7 Summary. Appendix 2A A Localization Principle. Appendix 2B Bounds on R(N). Appendix 2C An Operator Norm Inequality. Appendix 2D Negligible Terms. Appendix 2E Bound on Eigenvalues. Appendix 2F Some Inner Products. Appendix 2G An Invariant Inner Product. Appendix 2H Kronecker and Tensor Products. Acknowledgments. References. 3 Generalized MIMO Radar Ambiguity Functions (Geoffrey San Antonio, Daniel R. Fuhrmann, and Frank C. Robey). 3.1 Introduction. 3.2 Background. 3.3 MIMO Signal Model. 3.4 MIMO Parametric Channel Model. 3.5 MIMO Ambiguity Function. 3.6 Results and Examples. 3.7 Conclusion. References. 4 Performance Bounds and Techniques for Target Localization Using MIMO Radars (Joseph Tabrikian). 4.1 Introduction. 4.2 Problem Formulation. 4.3 Properties. 4.4 Target Localization. 4.5 Performance Lower Bound for Target Localization. 4.6 Simulation Results. 4.7 Discussion and Conclusions. Appendix 4A Log-Likelihood Derivation. Appendix 4B Transmit-Receive Pattern Derivation. Appendix 4C Fisher Information Matrix Derivation. References. 5 Adaptive Signal Design For MIMO Radars (Benjamin Friedlander). 5.1 Introduction. 5.2 Problem Formulation. 5.3 Estimation. 5.4 Detection. 5.5 MIMO Radar and Phased Arrays. Appendix 5A Theoretical SINR Calculation. References. 6 MIMO Radar Spacetime Adaptive Processing and Signal Design (Chun-Yang Chen and P. P. Vaidyanathan). 6.1 Introduction. 6.2 The Virtual Array Concept. 6.3 Spacetime Adaptive Processing in MIMO Radar. 6.4 Clutter Subspace in MIMO Radar. 6.5 New STAP Method for MIMO Radar. 6.6 Numerical Examples. 6.7 Signal Design of the STAP Radar System. 6.8 Conclusions. Acknowledgments. References. 7 Slow-Time MIMO SpaceTime Adaptive Processing (Vito F. Mecca, Dinesh Ramakrishnan, Frank C. Robey, and Jeffrey L. Krolik). 7.1 Introduction. 7.2 SIMO Radar Modeling and Processing. 7.3 Slow-Time MIMO Radar Modeling. 7.4 Slow-Time MIMO Radar Processing. 7.5 OTHr Propagation and Clutter Model. 7.6 Simulations Examples. 7.7 Conclusion. Acknowledgment. References. 8 MIMO as a Distributed Radar System (H. D. Griffiths, C. J. Baker, P. F. Sammartino, and M. Rangaswamy). 8.1 Introduction. 8.2 Systems. 8.3 Performance. 8.4 Conclusions. Acknowledgment. References. 9 Concepts and Applications of A MIMO Radar System with Widely Separated Antennas (Hana Godrich, Alexander M. Haimovich, and Rick S. Blum). 9.1 Background. 9.2 MIMO Radar Concept. 9.3 NonCoherent MIMO Radar Applications. 9.4 Coherent MIMO Radar Applications. 9.5 Chapter Summary. Appendix 9A Deriving the FIM. Appendix 9B Deriving the CRLB on the Location Estimate Error. Appendix 9C MLE of Time Delays - Error Statistics. Appendix 9D Deriving the Lowest GDOP for Special Cases. Acknowledgments. References. 10 SpaceTime Coding for MIMO Radar (Antonio De Maio and Marco Lops). 10.1 Introduction. 10.2 System Model. 10.3 Detection In MIMO Radars. 10.4 Spacetime Code Design. 10.5 The Interplay Between STC and Detection Performance. 10.6 Numerical Results. 10.7 Adaptive Implementation. 10.8 Conclusions. Acknowledgment. References. INDEX.
Chihsuan Hsieh - One of the best experts on this subject based on the ideXlab platform.
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a uwb Radar Signal Processing platform for real time human respiratory feature extraction based on four segment linear waveform model
IEEE Transactions on Biomedical Circuits and Systems, 2016Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao HuangAbstract:This paper presents an ultra-wideband (UWB) impulse-radio Radar Signal Processing platform used to analyze human respiratory features. Conventional Radar systems used in human detection only analyze human respiration rates or the response of a target. However, additional respiratory Signal information is available that has not been explored using Radar detection. The authors previously proposed a modified raised cosine waveform (MRCW) respiration model and an iterative correlation search algorithm that could acquire additional respiratory features such as the inspiration and expiration speeds, respiration intensity, and respiration holding ratio. To realize real-time respiratory feature extraction by using the proposed UWB Signal Processing platform, this paper proposes a new four-segment linear waveform (FSLW) respiration model. This model offers a superior fit to the measured respiration Signal compared with the MRCW model and decreases the computational complexity of feature extraction. In addition, an early-terminated iterative correlation search algorithm is presented, substantially decreasing the computational complexity and yielding negligible performance degradation. These extracted features can be considered the compressed Signals used to decrease the amount of data storage required for use in long-term medical monitoring systems and can also be used in clinical diagnosis. The proposed respiratory feature extraction algorithm was designed and implemented using the proposed UWB Radar Signal Processing platform including a Radar front-end chip and an FPGA chip. The proposed Radar system can detect human respiration rates at 0.1 to 1 Hz and facilitates the real-time analysis of the respiratory features of each respiration period.
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human respiratory feature extraction on an uwb Radar Signal Processing platform
International Symposium on Circuits and Systems, 2013Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao HuangAbstract:This paper presents a human respiratory feature extraction algorithm and its implementation on an ultra-wideband (UWB) impulse-radio Radar Signal Processing platform. The conventional human detection algorithms only extract the respiration rate by the Radar system. However, there is more information that is never explored in the Radar-detected respiratory Signals. Thus, this study proposes a modified raised cosine waveform as the respiration model and an iterative feature extraction algorithm to acquire more respiratory features, such as inspiration and expiration speeds, respiration intensity, and respiration holding ratio. These extracted features can be regarded as the compressed Signals for the long-term remote medical monitoring system. The proposed respiratory feature extraction algorithm is designed and implemented on a Radar Signal Processing platform with an Radar front-end chip, an ARM processor, and an FPGA chip. The proposed circuit can detect human respiratory Signals from 0.1 to 1 Hz rate and analyze the respiratory features for each period of the respiratory Signal.
Krzysztof Kulpa - One of the best experts on this subject based on the ideXlab platform.
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the clean type algorithms for Radar Signal Processing
Microwaves Radar and Remote Sensing Symposium, 2008Co-Authors: Krzysztof KulpaAbstract:A new class of Signal Processing algorithm based on CLEAN methods primary introduced in radio-astronomy is presented in the paper. The classical Radar Signal Processing is based on match filtering concept, which is optimal in mean square sense in case, when single target echo is detected against white or colour Gaussian noise. Such approach was effective when pulse Radar have been widely used. The introduction of pulse-compression technique changes significantly the Signal model, but still the match filter have been widely. The introduction of continuous wave Radars, and especially noise and passive Radars changed dramatically the situation. In such Radars all echoes are superimposed and interfere with each other and the simple model no longer fits to that case. The straightforward solution - use of inverse problem mathematical solutions such as solving the set of nonlinear equations to find all echoes in received Signal - is usually computationally ineffective and often numerically not stable, so suboptimal methods are being developed to improve detections of weak Signals in CW Radars. One of possible solution is to use concept of CLEAN technique an remove all strong echoes from received Signal. When only weak Signals and white noise remains in is possible to use matched filter concept without significant loses of Radar sensitivity. In the paper several techniques for Radar Signal Processing utilizing CLEAN concept are shown.
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continuous wave Radars monostatic multistatic and network
2006Co-Authors: Krzysztof KulpaAbstract:Radar technology was designed to increase public safety on sea and in the air. Today Radars are used in many fields of application, such as airdefense, air-traffic-control, zone protection (in military bases, airports, industry), people search and others. Classic pulse Radars are often being replaced by continuous wave Radars. Unique features of continuous wave Radars, such as the lack of ambiguity, very low transmitted power and good electromagnetic compatibility with other radio-devices, enhance this trend. This chapter presents the theoretical background of continuous wave Radar Signal Processing (for FMCW and noise Radars), highlights the most important features of this type of Radar and shows their abilities in the field of security.
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scalable hardware and software architecture for Radar Signal Processing system
Radar 97 (Conf. Publ. No. 449), 1997Co-Authors: Marek Nalecz, Krzysztof Kulpa, A Piatek, G WojdolowiczAbstract:The conventional approach to digital Signal Processing in Radar systems involves hardware realization with the use of specialized integrated circuits. Such an approach is lacking in versatility and scalability. Advances in digital Signal processor (DSP) technology make it possible to realize nearly all algorithms in software, using general-purpose DSP chips (cf. Edwards and Wilkinson 1996). Such an approach has many advantages as it can be easily adapted to changing (growing) users demand. In the current paper the latter method is considered in detail and a scalable architecture of hardware and software adequate for Radar Signal Processing is derived. Typical Radar systems require a total workload of the order of 1-10 Gflops. Comparing this figure with the 50 Mflops average performance of a modern floating-point DSP evidently one has to use about 100 processors in a single system. Therefore, both the topology of their connections and methods of paralleling Processing algorithms are very important.
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CONTINUOUS WAVE RadarS–MONOSTATIC, MULTISTATIC AND NETWORK
NATO Security Through Science Series, 2024Co-Authors: Krzysztof KulpaAbstract:Radar technology was designed to increase public safety on sea and in the air. Today Radars are used in many fields of application, such as airdefense, air-traffic-control, zone protection (in military bases, airports, industry), people search and others. Classic pulse Radars are often being replaced by continuous wave Radars. Unique features of continuous wave Radars, such as the lack of ambiguity, very low transmitted power and good electromagnetic compatibility with other radio-devices, enhance this trend. This chapter presents the theoretical background of continuous wave Radar Signal Processing (for FMCW and noise Radars), highlights the most important features of this type of Radar and shows their abilities in the field of security.
Yihsiang Shen - One of the best experts on this subject based on the ideXlab platform.
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a uwb Radar Signal Processing platform for real time human respiratory feature extraction based on four segment linear waveform model
IEEE Transactions on Biomedical Circuits and Systems, 2016Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao HuangAbstract:This paper presents an ultra-wideband (UWB) impulse-radio Radar Signal Processing platform used to analyze human respiratory features. Conventional Radar systems used in human detection only analyze human respiration rates or the response of a target. However, additional respiratory Signal information is available that has not been explored using Radar detection. The authors previously proposed a modified raised cosine waveform (MRCW) respiration model and an iterative correlation search algorithm that could acquire additional respiratory features such as the inspiration and expiration speeds, respiration intensity, and respiration holding ratio. To realize real-time respiratory feature extraction by using the proposed UWB Signal Processing platform, this paper proposes a new four-segment linear waveform (FSLW) respiration model. This model offers a superior fit to the measured respiration Signal compared with the MRCW model and decreases the computational complexity of feature extraction. In addition, an early-terminated iterative correlation search algorithm is presented, substantially decreasing the computational complexity and yielding negligible performance degradation. These extracted features can be considered the compressed Signals used to decrease the amount of data storage required for use in long-term medical monitoring systems and can also be used in clinical diagnosis. The proposed respiratory feature extraction algorithm was designed and implemented using the proposed UWB Radar Signal Processing platform including a Radar front-end chip and an FPGA chip. The proposed Radar system can detect human respiration rates at 0.1 to 1 Hz and facilitates the real-time analysis of the respiratory features of each respiration period.
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human respiratory feature extraction on an uwb Radar Signal Processing platform
International Symposium on Circuits and Systems, 2013Co-Authors: Chihsuan Hsieh, Yihsiang Shen, Yufang Chiu, Yuanhao HuangAbstract:This paper presents a human respiratory feature extraction algorithm and its implementation on an ultra-wideband (UWB) impulse-radio Radar Signal Processing platform. The conventional human detection algorithms only extract the respiration rate by the Radar system. However, there is more information that is never explored in the Radar-detected respiratory Signals. Thus, this study proposes a modified raised cosine waveform as the respiration model and an iterative feature extraction algorithm to acquire more respiratory features, such as inspiration and expiration speeds, respiration intensity, and respiration holding ratio. These extracted features can be regarded as the compressed Signals for the long-term remote medical monitoring system. The proposed respiratory feature extraction algorithm is designed and implemented on a Radar Signal Processing platform with an Radar front-end chip, an ARM processor, and an FPGA chip. The proposed circuit can detect human respiratory Signals from 0.1 to 1 Hz rate and analyze the respiratory features for each period of the respiratory Signal.
