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Nolan White
Nolan White

Mti And Pulsed Doppler Radar With Matlab



Moving target indication (MTI) is a technique that enhances thedetection and display of moving radar targets that can be implementedwith pulsed Doppler radar in order to exploit the differential Dopplershifts between real targets and clutter. In this revised and updatedtext, Schleher (emeritus, US Naval Postgraduate School) discussestheoretical and practical considerations involved with the system designand performance of MTI and pulsed Doppler radar. He addresses radarclutter properties, how MTI and pulsed Doppler radar functions separatetargets from clutter, and the theoretical basis for optimum detection oftargets. Derivations are given for the theoretical relationships used inthese systems and are followed by practical examples to illustrate theiruse. Some 100 MATLAB programs are presented to aid in the design andanalysis of these systems using the matrix and vector approach found inthe text. The CD-ROM contains the MATLAB programs, color figures, and ashort tutorial on MATLAB.




Mti And Pulsed Doppler Radar With Matlab



Written for engineers, this book provides comprehensive coverage of radar design. Clear descriptions and characteristics of modern doppler radars are provided, along with over 350 illustrations and more than 730 equations. Topics covered include MTI radar, pulse doppler radar, doppler performance measures, and radar clutter. The revised second edition includes MATLAB for analysis and design.


This class introduces the student to the fundamentals of radar system engineering. The radar range equation in its many forms is developed and applied to different situations. Radar transmitters, antennas, and receivers are covered. The concepts of matched filtering, pulse compression, and the radar ambiguity function are introduced, and the fundamentals of radar target detection in a noise background are discussed. Target radar cross-section models are addressed, as well as the effects of the operating environment, including propagation and clutter. MTI and pulsed Doppler processing and performance are addressed. Range, angle, and Doppler resolution/accuracy, as well as fundamental tracking concepts, will also be discussed.


Pulsed Doppler is a radar system that is capable of not only detecting target location, but also measuring its velocity. Moving target indication (MTI) is a mode of operation of a radar to discriminate a target against clutter. Radar design and system engineers need to understand these essential topics, and this newly revised and updated edition of the classic Artech House book, "MTI and Pulsed Doppler Radar", offers professionals a complete and current presentation of the subject.Moreover, this unique resource provides clear descriptions and characteristics of modern Doppler radars that cannot be found in any other book. The second edition includes a new interactive CD-ROM with MATLAB software that saves practitioners time while working on challenging projects in the field. CD-ROM is included in this title! It contains time-saving MATLAB software that serves as a valuable tool for the analysis and design of "MTI and Pulsed Doppler Radar". The disc also includes several full-color images that support key topics discussed in the book.


The vast majority of literature on the topic of drone identification focuses on image processing and video analytics based on the training of classifiers, namely machine learning algorithms, fed by video sequences collected from different types of cameras. The goal is to extract some features for different types of classifications: drone category (fixed wing, single-rotor, multi-rotor); distinction between drones and birds, which are the most similar targets for size and radar cross section (RCS); evaluation of the presence of any payload that affects the RCS of the entire target [17,18]. The main limitation of this technology for primary detection is the size of such targets, which can be easily confused with the background or indistinguishable from birds. Moreover, cameras are severely affected by environmental conditions, for instance low ambient light, variable illumination and shadows, or even simple occlusion of the lens that turns the system completely inoperative; for night time and generally dark areas infrared cameras are also required, which have usually lower resolution and range, and higher cost. Due to all these considerations, video-based approaches are very useful in good weather conditions and at short distances, especially for the verification and classification phases after having already declared a detection. The reader is referred to [19] for a recent comprehensive review of the existing video-based techniques for drone detection. Less surveyed is instead the literature on radar techniques. In this respect, an original contribution of this paper is the focus on the review of radar-based detection and classification work.


RF signals are robust to weather and illumination conditions, and can provide medium to long range coverage. They are a suitable tool to handle the primary detection phase, which triggers the entire identification process. The most important device for active RF-based drone detection is the radar sensor, but passive technologies are also relevant. In particular, the use of spectrum sensing can be a prominent solution to detect a drone when uplink/downlink transmissions exist between the drone and its controller. This may also enable localization of the remote pilot, which is important from a liability point of view. However, there are two limitations in this approach, related to the portion of the spectrum on which communications between the controller and the drone take place, and to the possibility of autonomous (GPS-based) guidance mode. Indeed, drone communications typically use the industrial, scientific and medical (ISM) frequency spectrum where many other systems (including Wi-Fi and some fixed wireless access technologies) are found; this means the band of interest is crowded, increasing the risk of false alarms. To cope with this issue, several papers have addressed the subject of network traffic analysis, see, for example, [24,25,26,27,28,29]. Nonetheless, passive detection is completely ineffective in the case of drones flying in fully autonomous mode, which can be especially the case of security threats. Laser scanners (LIDAR) are also considered for active detection in environments where radars cannot be used. In this case, backscattering of laser light is exploited, which however is sensitive to bad visibility due to weather, smog, or direct sunlight. In normal weather conditions, LIDARs can be very effective for drone detection and basic classification, namely based on target size, which can occupy several cells as a consequence of the fine angular resolution of lasers (very narrow beam). However, targets with similar size, in particular drones and birds, are indistinguishable. LIDARs can be thus considered a complementary technology with respect to RF sensors.


An FMCW signal, also known as linear FMCW (LFMCW) or linear frequency modulated (LFM) signal, consists of a linearly modulated continuous wave radio energy transmitted in a desired direction. These kinds of signals are often referred to as chirps and differ from CW because, in the latter, the operating frequency is not varied during the transmission, as shown in Figure 3a,b. Radars using such waveforms became very popular especially in the automotive field, due to the low cost of hardware components, and thanks to their ability to provide both range and Doppler information. The FMCW transmitted and received signals are schematically depicted in Figure 4; from these signals, it is possible to extract delay τ and phase φ information, which are useful to obtain distance and velocity information of one or more targets simultaneously. The basic processing of the received signal is performed through I/Q demodulation (as reported in Figure 5), which provides in-phase and quadrature-phase components of the complex baseband signal, called beat signal or Intermediate Frequency (IF) signal. The down-conversion is introduced to considerably simplify the realization of the processing circuits, allowing the circuit to work at much lower frequency compared to the transmitted signal. The substantial difference between FMCW and CW processing chains, see Figure 5, is only in the control signal generator, which provides the reference signal (in the CW case it is a constant one). Combining this signal with a voltage controlled oscillator (VCO) produces the resulting RF signal that will be transmitted by the radar.


The following works are conversely more specifically focused on the detection phase. In [62] an FMCW radar in K-band is distributed in two separate platforms and connected with optical fiber, in order to reduce the leakage coupling between transmit and receive antennas and improve the sensitivity of the system. By means of 2D Fast Fourier Transform (FFT), range and speed of the target are obtained, and thanks to the particular structure of the radar system, detection distances up to 500 m can be achieved.


In [63] two solutions based on FMCW radar operating in W-band are proposed for detecting small UAVs. The first is an 8 Hz rotating surveillance radar system supported with a camera; in this way for every second of monitoring, eight scans are obtained, which are then analyzed with an algorithm based on the CFAR approach. The second system is a multi-channel surveillance radar with four receiving antennas. This type of setup improves the spatial resolution in azimuth and elevation compared to the previous one. Experimental evaluations with both systems have shown that it is possible to detect a UAV up to about 100 m from the radar. 350c69d7ab


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