![]() The signal beam is transmitted via the antenna towards the target and the idler beam is detected directly into the idler detector array that carries the quantum states changes of the radar signal beam simultaneously before the echo arrival. signal and idler entangled photonic beams generator. Here, a block diagram of the EPR system is presented, which includes high power. The predicted Entangled Photonic Radar (EPR) System employs the non-classic strong correlation of the quantum states of two entangled photonic beams over radar range. This paper develops a new radar system based on the concept of quantum entanglement property termed by Albert Einstein as a spooky action at a distance. The final chapter considers advanced concepts and trends in ISAR imaging. For all these algorithms, MATLAB codes and figures are included. Finally, various real applications of ISAR imagery, like imaging the antenna-platform scattering, are given in a separate chapter. ![]() To enhance the image quality in ISAR imaging, several imaging tricks and fine-tuning procedures such as zero-padding and windowing are also presented. After providing the fundamentals for ISAR imaging, the book gives the detailed imaging procedures for ISAR imaging with associated MATLAB functions and codes. ![]() Important concepts of SAR such as resolution, pulse compression and image formation are given together with associated MATLAB codes. The book offers a fair amount of signal processing techniques and radar basics before introducing the inverse problem of ISAR and the forward problem of Synthetic Aperture Radar (SAR). The book covers all possible aspects of ISAR imaging. This book provides a full representation of Inverse Synthetic Aperture Radar (ISAR) imagery, which is a popular and important radar signal processing tool. The MADAR system eventually will provide the capability to detect, classify, track and determine the location of enemy indirect fire, such as mortars, artillery and rockets in either 90-degree or 360-degree modes. The innovative ideas applied into the proposed MADAR will achieve improved performance parameters such as receiver dynamic range, detection range, resolution range, beam pattern, beam pointing accuracy, higher probability of small targets detection in heavy clutters, lower rate of false alarms, lower clutter to signal to noise ratio, lower side lobes level, and lighter weight aperture array. The proposed MADAR is an S-band phased array radar system utilizing the gallium nitride solid state cavity transceivers, microstrip radiators elements, Adaptive Beam Forming (ABF), and sophisticated digital receiver algorithms. The author proposed the MADAR system in order to achieve a high performance level over the current radar versions in the field such as EQ-36. This paper presents an innovative architecture for a feasible Mobile Adaptive Digital Array Radar (MADAR) system that could be utilized for detection and tracking different sizes of short range targets from fighter aircrafts to UAV, rockets, and cannons projectiles. Through real-world examples and the evaluation of algorithmic results, this detailed book provides an understanding of MSDF concepts and methods from a practical point of view. Fusing sensors' data can lead to numerous benefits in a system's performance. The final chapters discuss how DF is applied to mobile intelligent autonomous systems and intelligent monitoring systems. ![]() It also presents procedures for combing tracks obtained from imaging sensor and ground-based radar. The book then employs principal component analysis, spatial frequency, and wavelet-based image fusion algorithms for the fusion of image data from sensors. It develops a new MATLAB graphical user interface for evaluating fuzzy implication functions, before using fuzzy logic to estimate the unknown states of a dynamic system by processing sensor data. It next covers fuzzy logic, fuzzy sets and their properties, fuzzy logic operators, fuzzy propositions/rule-based systems, an inference engine, and defuzzification methods. After presenting several useful strategies and algorithms for DF and tracking performance, the book evaluates DF algorithms, software, and systems. The authors elucidate DF strategies, algorithms, and performance evaluation mainly for aerospace applications, although the methods can also be applied to systems in other areas, such as biomedicine, military defense, and environmental engineering. Using MATLABĀ® examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF fuzzy logic and decision fusion and pixel- and feature-level image fusion.
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