Radar: Theory and Insight

Radar principles and design considerations are reviewed, using both analytical and heuristic methods. Emphasis is placed on achieving physical insight while maintaining technical rigor. Waveform properties and signal detectability maximization through matched filtering is discussed in detail. The Moving Target Indicator radar system is presented as an illustration of digital signal processing technology applied to modern radar systems

Design of a Simulator for Neonatal Multichannel EEG: Application to Time-Frequency Approaches for Automatic Artifact Removal and Seizure Detection

The electroencephalogram (EEG) is used to noninvasively monitor brain activities; it is the most utilized tool to detect abnormalities such as seizures. In recent studies, detection of neonatal EEG seizures has been automated to assist neurophysiologists in diagnosing EEG as manual detection is time consuming and subjective; however it still lacks the necessary robustness that is required for clinical implementation. Moreover, as EEG is intended to record the cerebral activities, extra-cerebral activities external to the brain are also recorded; these are called “artifacts” and can seriously degrade the accuracy of seizure detection. Seizures are one of the most common neurologic problems managed by hospitals occurring in 0.1%-0.5% livebirths. Neonates with seizures are at higher risk for mortality and are reported to be 55-70 times more likely to have severe cerebral-palsy. Therefore, early and accurate detection of neonatal seizures is important to prevent long-term neurological damage. Several attempts in modelling the neonatal EEG and artifacts have been done, but most did not consider the multichannel case. Furthermore, these models were used to test artifact or seizure detection separately, but not together. This study aims to design synthetic models that generate clean or corrupted multichannel EEG to test the accuracy of available artifact and seizure detection algorithms in a controlled environment. In this thesis, synthetic neonatal EEG model is constructed by using; single-channel EEG simulators, head model, 21-electrodes, and propagation equations, to produce clean multichannel EEG. Furthermore, neonatal EEG artifact model is designed using synthetic signals to corrupt EEG waveforms. After that, an automated EEG artifact detection and removal system is designed in both time and time-frequency domains. Artifact detection is optimised and removal performance is evaluated. Finally, an automated seizure detection technique is developed, utilising fused and extended multichannel features along a cross-validated SVM classifier. Results show that the synthetic EEG model mimics real neonatal EEG with 0.62 average correlation, and corrupted-EEG can degrade seizure detection average accuracy from 100% to 70.9%. They also show that using artifact detection and removal enhances the average accuracy to 89.6%, and utilising the extended features enhances it to 97.4% and strengthened its robustness.لمراقبة ورصد أنشطة واشارات المخ، دون الحاجة لأي عملیات (EEG) یستخدم الرسم أو التخطیط الكھربائي للدماغ للدماغجراحیة، وھي تعد الأداة الأكثر استخداما في الكشف عن أي شذوذأو نوبات غیر طبیعیة مثل نوبات الصرع. وقد أظھرت دراسات حدیثة، أن الكشف الآلي لنوبات حدیثي الولادة، ساعد علماء الفسیولوجیا العصبیة في تشخیص الاشارات الدماغیة بشكل أكبر من الكشف الیدوي، حیث أن الكشف الیدوي یحتاج إلى وقت وجھد أكبر وھوذو فعالیة أقل بكثیر، إلا أنھ لا یزال یفتقر إلى المتانة الضروریة والمطلوبة للتطبیق السریري.علاوة على ذلك؛ فكما یقوم الرسم الكھربائي بتسجیل الأنشطة والإشارات الدماغیة الداخلیة، فھو یسجل أیضا أي نشاط أو اشارات خارجیة، مما یؤدي إلى -(artifacts) :حدوث خلل في مدى دقة وفعالیة الكشف عن النوبات الدماغیة الداخلیة، ویطلق على تلك الاشارات مسمى (نتاج صنعي) . 0.5٪ولادة حدیثة في -٪تعد نوبات الصرع من أكثر المشكلات العصبیة انتشارا،ً وھي تصیب ما یقارب 0.1المستشفیات. حیث أن حدیثي الولادة المصابین بنوبات الصرع ھم أكثر عرضة للوفاة، وكما تشیر التقاریر الى أنھم 70مرة أكثر. لذا یعد الكشف المبكر والدقیق للنوبات الدماغیة -معرضین للإصابة بالشلل الدماغي الشدید بما یقارب 55لحدیثي الولادة مھم جدا لمنع الضرر العصبي على المدى الطویل. لقد تم القیام بالعدید من المحاولات التي كانتتھدف الى تصمیم نموذج التخطیط الكھربائي والنتاج الصنعي لدماغ حدیثي الولادة, إلا أن معظمھا لم یعر أي اھتمام الى قضیة تعدد القنوات. إضافة الى ذلك, استخدمت ھذه النماذج , كل على حدة, أو نوبات الصرع. تھدف ھذه الدراسة الى تصمیم نماذج مصطنعة من شأنھا (artifact) لإختبار كاشفات النتاج الصنعيأن تولد اشارات دماغیة متعددة القنوات سلیمة أو معطلة وذلك لفحص مدى دقة فعالیة خوارزمیات الكشف عن نوبات ضمن بیئة یمكن السیطرة علیھا. (artifact) الصرع و النتاج الصنعي في ھذه الأطروحة, یتكون نموذج الرسم الكھربائي المصطنع لحدیثي الولادة من : قناة محاكاة واحده للرسم الكھربائي, نموذج رأس, 21قطب كھربائي و معادلات إنتشار. حیث تھدف جمیعھا لإنتاج إشاراة سلیمة متعدده القنوات للتخطیط عن طریق استخدام اشارات مصطنعة (artifact) الكھربائي للدماغ.علاوة على ذلك, لقد تم تصمیم نموذجالنتاج الصنعيفي نطاقالوقت و (artifact) لإتلاف الرسم الكھربائي للدماغ. بعد ذلك تم انشاء برنامج لكشف و إزالةالنتاج الصناعينطاقالوقت و التردد المشترك. تم تحسین برنامج الكشف النتاج الصناعيالى ابعد ما یمكن بینما تمت عملیة تقییم أداء الإزالة. وفي الختام تم التمكن من تطویر تقنیة الكشف الآلي عن نوبات الصرع, وذلك بتوظیف صفات مدمجة و صفات الذي تم التأكد من صحتھ. (SVM) جدیدة للقنوات المتعددة لإستخدامھا للمصنفلقد أظھرت النتائج أن نموذج الرسم الكھربائي المصطنع لحدیثي الولادة یحاكي الرسمالكھربائي الحقیقي لحدیثي الولادة بمتوسط ترابط 0.62, و أنالرسم الكھربائي المتضرر للدماغ قد یؤدي الى حدوث ھبوطفي مدى دقة متوسط الكشف عن نوبات الصرع من 100%الى 70.9%. وقد أشارت أیضا الى أن استخدام الكشف والإزالة عن النتاج الصنعي (artifact) یؤدي الى تحسن مستوى الدقة الى نسبة 89.6 %, وأن توظیف الصفات الجدیدة للقنوات المتعددة یزید من تحسنھا لتصل الى نسبة 94.4 % مما یعمل على دعم متانتھا

MULTISTATIC RADAR EMITTER IDENTIFICATION USING ENTROPY MAXIMIZATION BASED INDEPENDENT COMPONENT ANALYSIS

Radar emitter identification is state-of-the-art in modern electronic warfare. Presently multistatic architecture is adapted by almost all the radar systems for better tracking performance and accuracy in target detection. Hence, identification and classification of radar emitters operating in the surveillance region are the major problems. To deal with the difficulty of identification of radar emitters in a complex electromagnetic environment, in this work entropy maximization method of Independent Component Analysis (ICA) based on gradient ascent algorithm is proposed. This algorithm separates unknown source signals from the interleaved multi-component radar signals. The discrete source signals are extracted from the multi-component signal by optimizing the entropy where maximum entropy is achieved using a gradient ascent approach through unsupervised learning. As better detection capability and range resolution are achieved by Linear Frequency Modulated (LFM) signals for radar systems here, multicomponent LFM signals with low SNR are considered as the signal mixture from which, the independent sources separated. A mathematical model of the algorithm for entropy maximization is illustrated in this paper. Simulation result validates the effectiveness of the algorithm in terms of time domain separation of the signal, and time-frequency analysi

Underdetermined Blind Separation of Nondisjoint Sources in the Time-Frequency Domain

International audienceThis paper considers the blind separation of non-stationary sources in the underdetermined case, when there are more sources than sensors. A general framework for this problem is to work on sources that are sparse in some signal representation domain. Recently, two methods have been proposed with respect to the time-frequency (TF) domain. The first uses quadratic time-frequency distributions (TFDs) and a clustering approach, and the second uses a linear TFD. Both of these methods assume that the sources are disjoint in the TF domain; i.e. there is at most one source present at a point in the TF domain. In this paper, we relax this assumption by allowing the sources to be TF-nondisjoint to a certain extent. In particular, the number of sources present at a point is strictly less than the number of sensors. The separation can still be achieved thanks to subspace projection that allows us to identify the sources present and to estimate their corresponding TFD values. In particular, we propose two subspace-based algorithms for TF-nondisjoint sources, one uses quadratic TFDs and the other a linear TFD. Another contribution of this paper is a new estimation procedure for the mixing matrix. Finally, then numerical performance of the proposed methods are provided highlighting their performance gain compared to existing ones

Investigation of Non-coherent Discrete Target Range Estimation Techniques for High-precision Location

Ranging is an essential and crucial task for radar systems. How to solve the range-detection problem effectively and precisely is massively important. Meanwhile, unambiguity and high resolution are the points of interest as well. Coherent and non-coherent techniques can be applied to achieve range estimation, and both of them have advantages and disadvantages. Coherent estimates offer higher precision but are more vulnerable to noise and clutter and phase wrap errors, particularly in a complex or harsh environment, while the non-coherent approaches are simpler but provide lower precision. With the purpose of mitigating inaccuracy and perturbation in range estimation, miscellaneous techniques are employed to achieve optimally precise detection. Numerous elegant processing solutions stemming from non-coherent estimate are now introduced into the coherent realm, and vice versa. This thesis describes two non-coherent ranging estimate techniques with novel algorithms to mitigate the instinct deficit of non-coherent ranging approaches. One technique is based on peak detection and realised by Kth-order Polynomial Interpolation, while another is based on Z-transform and realised by Most-likelihood Chirp Z-transform. A two-stage approach for the fine ranging estimate is applied to the Discrete Fourier transform domain of both algorithms. An N-point Discrete Fourier transform is implemented to attain a coarse estimation; an accurate process around the point of interest determined in the first stage is conducted. For KPI technique, it interpolates around the peak of Discrete Fourier transform profiles of the chirp signal to achieve accurate interpolation and optimum precision. For Most-likelihood Chirp Z-transform technique, the Chirp Z-transform accurately implements the periodogram where only a narrow band spectrum is processed. Furthermore, the concept of most-likelihood estimator is introduced to combine with Chirp Z-transform to acquire better ranging performance. Cramer-Rao lower bound is presented to evaluate the performance of these two techniques from the perspective of statistical signal processing. Mathematical derivation, simulation modelling, theoretical analysis and experimental validation are conducted to assess technique performance. Further research will be pushed forward to algorithm optimisation and system development of a location system using non-coherent techniques and make a comparison to a coherent approach

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Detecting and locating electronic devices using their unintended electromagnetic emissions

Electronically-initiated explosives can have unintended electromagnetic emissions which propagate through walls and sealed containers. These emissions, if properly characterized, enable the prompt and accurate detection of explosive threats. The following dissertation develops and evaluates techniques for detecting and locating common electronic initiators. The unintended emissions of radio receivers and microcontrollers are analyzed. These emissions are low-power radio signals that result from the device\u27s normal operation. In the first section, it is demonstrated that arbitrary signals can be injected into a radio receiver\u27s unintended emissions using a relatively weak stimulation signal. This effect is called stimulated emissions. The performance of stimulated emissions is compared to passive detection techniques. The novel technique offers a 5 to 10 dB sensitivity improvement over passive methods for detecting radio receivers. The second section develops a radar-like technique for accurately locating radio receivers. The radar utilizes the stimulated emissions technique with wideband signals. A radar-like system is designed and implemented in hardware. Its accuracy tested in a noisy, multipath-rich, indoor environment. The proposed radar can locate superheterodyne radio receivers with a root mean square position error less than 5 meters when the SNR is 15 dB or above. In the third section, an analytic model is developed for the unintended emissions of microcontrollers. It is demonstrated that these emissions consist of a periodic train of impulses. Measurements of an 8051 microcontroller validate this model. The model is used to evaluate the noise performance of several existing algorithms. Results indicate that the pitch estimation techniques have a 4 dB sensitivity improvement over epoch folding algorithms --Abstract, page iii