The micro-Doppler effect appears as Doppler frequency modulations in coherent laser or microwave radar systems induced by mechanical vibrations or rotations of a target or any part on the target. These Doppler modulations become a distinctive signature of a target that incorporates vibrating or rotating structures, and provides evidence of the identity of the target with movement. This book concentrates on the processing and application of radar micro-Doppler signatures in real world situations, providing readers with a working knowledge on various applications of radar micro-Doppler signatures such as detection, tracking and discrimination of vehicles and dismounts, identifying human movement based on radar micro-Doppler signatures, detection and tracking small boats in sea, detection and discrimination complex motion of missile warheads, discrimination of quadrupedal animals, and detection and tracking of flying birds. Topics covered include bistatic/multistatic micro-Doppler signatures, decomposition of micro-Doppler signatures, through-wall radar micro-Doppler signatures and ultrasound micro-Doppler signature studies. Radar Micro-Doppler Signatures: Processing and applications will be of interest to R&D researchers and engineers in government research centers, industries, and universities around the world who work on radar imaging and signal analysis, target feature extraction, and non-cooperative target recognition. Supplementary files are available for this title. To request a copy of these, please email books@theiet.org
Other keywords: helicopter rotor blades; radar microDoppler signatures; sea clutter; wind turbines; bistatic-multistatic microDoppler signature processing; super-high resolution radar; human movement analysis; sonar microDoppler signatures; microrange signature analysis; through-wall radar
The micro-Doppler signature is a distinctive characteristic of the observed micro-Doppler effect in an object. The “signature” is commonly used to refer to the characteristic expression of an object or a process. When examining the micro-Doppler effect, the distinctive micro-Doppler characteristic, i.e., the micro-Doppler signature of an object, allows the recognition of an object's identity through its movement. To extract micro-Doppler signature of an object by radar, a simple continuous wave (CW) radar is good enough. However, wide-band coherent Doppler radars, which use wide frequency bandwidth to gain high range resolution and measure both the range and Doppler information, are more useful and desirable in the radar micro-Doppler signature research. In this chapter, we review current progress in the extraction, processing, analysis, and applications of radar micro-Doppler signatures, and discuss the challenges and perspectives in radar micro-Doppler signature research.
In this chapter, we introduce mathematics of the micro-Doppler effect in radar, derive basic formulas of micro-Doppler shifts due to vibration, rotation, tumbling and conning, and extract micro-Doppler signatures using time-frequency analysis.
In this chapter, the micro-range and micro-Doppler phenomenon has been discussed. The simultaneous utilization of both the microrange and microDoppler domains provides an extra dimension in which to resolve and isolate individual scatterer paths corresponding to different body elements. Extracting information regarding specific body parts may be exploited for improved target detection, classification, and discrimination. An algorithm for automated microrange-microDoppler target signature decomposition has been discussed. These concepts and algorithms were illustrated on measured human signatures.
In radar imaging it is well known that relative motion or deformations of parts of illuminated objects induce additional features in the Doppler frequency spectra. These features are called micro-Doppler effect and appear as sidebands around the central Doppler frequency. They can provide valuable information about the structure of the moving parts and may be used for identification purposes.
Radar technology has been utilized for through-the-wall operation in recent years. Through-the-wall radars have become an area of interest due to their usefulness in surveillance, search and rescue operations, and others. We address the specific challenges that through-the-wall radars endure, and present simulations and experimental data for through-the-wall micro-Doppler signatures. The design considerations, including wall attenuation, frequency selection, and dispersion are discussed. Some commonly used time-frequency transforms are briefly discussed with a focus on how they can be used to analyze micro-Doppler signatures. Some of the effects that a barrier such as a wall has on the return microDoppler signals and discusses how these differ from that of micro-Doppler signals that arise without any barrier are demonstrated. Some experimental data of micro-Doppler signatures for targets with and without a translational motion are presented. Some models of common human activities and motions are also discussed and compared to the experimentally measured through-the-wall micro-Doppler signals.
In this section we present a feature-based approach to estimate human motion parameters from radar spectrograms. The walking model of Boulic is used with personification information of the torso and leg. We have proposed a sinusoidal model for the torso and leg. The torso sinusoidal is related to the centre velocity and the leg sinusoidal is related to the lower and upper velocity bounds. Three methods are described which extract these velocities. Sinusoidal fits of velocity-slices give the cycle frequency that originates from periodic spectrogram components. Kalman filters smooth the features. The animated human generated with the features provide a realistic look-alike of the real motion of human irrespective of the walking model used to generate it. The methods are applied to real radar measurements with different scenarios. The three methods give approximately equivalent results. The percentile method has best match with the velocity bounds and centre velocity. The computation time of the correlation method is about six times the other methods. We advise the percentile method with leg frequency and cycle frequency fusion. If enough computation time is available, the cycle frequency is not needed in real-time applications.
The work presented in this chapter has shown how to take advantage of the microDoppler features of the rotary parts of a helicopter. Traditionally, analysis of the amplitude variations of the target echo returns have been used in order to separate fixed wing and rotary wing aircraft. Modern radar systems are exploiting more signal processing, thus affording the extra costs of the Doppler processing. By applying the so-called short-time Fourier transform (STFT) to a data set, we can extract both amplitude variations (as a function of time) and Doppler contents from the signal, and thus more information will be available for target classification. The work has been focusing on the properties of helicopter micro-Doppler results from real-life systems, touching the dependency on monostatic and bistatic systems, frequency, geometry, and waveform. RCS modeling results from generic helicopter blades have shown the effects of frequency choice as well as geometrical dependence of the flashes from both main and tail rotor. Common models ranging from a box-like rotor to a high-fidelity CAD model of a generic helicopter blade demonstrated the required model accuracy as a function of frequency.
In this chapter, the authors first use the small boats and sea clutter database collected by the Council for Scientific and Industrial Research (CSIR) of South Africa to demonstrate radar microDoppler signatures of small boats in sea clutter. Then, microDoppler images of small boats were compared in the littoral environment.
In this chapter the generation and exploitation of multistatic micro-Doppler radar target signatures are examined with the ultimate aim of improving the classification of targets.
The micro-Doppler effects detection, estimation, and removal are analyzed in this chapter. Since the most common form of the micro-Doppler signal is a sinusoidal modulated one, a simple technique for its analysis, based on the inverse Radon transform is presented. A method for period estimation is presented as well. The presented method is important for the micro-Doppler analysis itself, as well as for the technique based on the inverse Radon transform. In addition, it is shown that a general micro-Doppler form can be separated from the rigid body, by using TF analysis and the L-statistics. Finally, the micro-Doppler effect in high noise environments is analyzed by using the Viterbi algorithm.
In this chapter we introduce the theory behind sonar micro-Doppler signatures and highlight advantages and disadvantages with respect to their radar counterpart. We present the results of two experimental trials carried out at (1) the U.S. Army Research Laboratory and (2) University College London in which sonar micro-Doppler signatures of personnel targets were collected at 40 and 80 kHz, respectively. This chapter shows that ultrasound sensors could represent a cost-effective alternative to radar systems for applications that do not require long-range detection. They certainly demonstrate that target detection and possibly classification by ultrasound micro-Doppler signatures are achievable and they might potentially open up a new research avenue looking at the exploitation of these techniques in future systems.
The radar micro-Doppler signatures of wind turbines have been discussed in this chapter. Due to the extremely large dimension and blade rotation, an increasingly large number of wind turbines have become potential interference to radar operations. The interactions between wind turbines and radar especially weather radars are analyzed, and some examples have been given to showcase the clutter characteristics. As a result of the blade rotation, wind turbines have unique micro-Doppler radar signatures that are best perceived in the time-frequency analysis. The Doppler flashes extend through the entire spectrum, making it difficult to mitigate in the Doppler domain. The scaled measurements of the wind turbine model have been implemented to better understand the complex micro-Doppler radar signatures and its dependency on wind turbine motions in a controlled laboratory environment. Both frequency and time domain measurement approaches have been used. Some interesting EM phenomenon and statistical variables are studied from the scaled measurement results. However, the scaling factor limits our ability to interpret the spectrum by compromising the relative Doppler spectrum resolution. Hence, a mobile radar has been deployed to better perceive the details of wind turbine micro-Doppler signatures. The wind turbine is a new form of radar interference that features extremely complicated micro-Doppler signatures. The radar return variations of wind turbines with respect to their position, shape, material, and radar parameters, etc. are still being studied and there are many aspects that are not covered in this chapter. More extensive studies from physical models, radar signature analysis to mitigation approaches are needed.
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