- Open Access
Developments in target micro-Doppler signatures analysis: radar imaging, ultrasound and through-the-wall radar
© Clemente et al.; licensee Springer. 2013
- Received: 31 July 2012
- Accepted: 9 February 2013
- Published: 12 March 2013
Target motions, other than the main bulk translation of the target, induce Doppler modulations around the main Doppler shift that form what is commonly called a target micro-Doppler signature. Radar micro-Doppler signatures are generally both target and action specific and hence can be used to classify and recognise targets as well as to identify possible threats. In recent years, research into the use of micro-Doppler signatures for target classification to address many defence and security challenges has been of increasing interest. In this article, we present a review of the work published in the last 10 years on emerging applications of radar target analysis using micro-Doppler signatures. Specifically we review micro-Doppler target signatures in bistatic SAR and ISAR, through-the-wall radar and ultrasound radar. This article has been compiled to provide radar practitioners with a unique reference source covering the latest developments in micro-Doppler analysis, extraction and mitigation techniques. The article shows that this research area is highly active and fast moving and demonstrates that micro-Doppler techniques can provide important solutions to many radar target classification challenges.
- Synthetic Aperture Radar
- Synthetic Aperture Radar Image
- Inverse Synthetic Aperture Radar
- Singular Spectrum Analysis
Moving targets illuminated by a radar system contain frequency modulations caused by the time-varying delay occurring between the target and the sensor. The main bulk translation of the target towards or away from the sensor induces a frequency or Doppler shift of the echo as a result of the well-known Doppler effect. However, the target can contain parts that can have additional movements. These can contribute frequency modulations around the main Doppler shift that are commonly referred to as micro-Doppler (m-D) modulations. Chen [1, 2] modelled the radar m-D phenomenon and simulated m-D signatures for various targets, such as rotating cylinders, vibrating scatterers and personnel targets. The authors also showed an effective tool in extracting the m-D signature is the time-frequency analysis of the received signal, leading to additional information on the target that can be used for classification and recognition. Micro-Doppler can be regarded as a unique signature of the target that provides additional information about the target that is complementary to existing methods for target recognition. Specific applications include the recognition of space, air and ground targets. For example, the m-D effect can be used to identify specific types of vehicles and determine their movement and the speed of their engines. Vibrations generated by a vehicle engine can be detected by radar signals returned from the surface of the vehicle. For example, from m-D modulations in the engine vibration signal, one can distinguish whether it is a gas turbine engine of a tank or the diesel engine of a bus. Another application is the use of m-D signature for human identification making possible the identification of humans on different weather or light conditions. In particular, specific components of m-D gait signature can be related to parts of the body for identification purposes . Recently effective signature extraction techniques have been developed and tested on real data [4–11] providing features leading to classification results with a high level of confidence. These results could probably be improved if a multistatic m-D signature is used  where the self occlusion problem of the target is avoided.
In this article, we review recent advances in radar based m-D analysis over the last decade from radar imaging systems and emerging radar techniques. Our attention will focus on four fields that provide interesting results exploiting the m-D features: synthetic aperture radar, inverse synthetic aperture radar, ultrasound and through wall radar.
The remainder of the article is organised as follows. In Section 2, the basic concepts of micro-Doppler from a radar system are introduced. Section 3 is the review Section. Subsection 3.1 introduces the m-D effect studies for radar imaging platform, monostatic SAR, bistatic SAR and Inverse SAR. The exploitations of m-D signatures proposed for ultrasound radar systems are described in Subsection 3.2 while Subsection 3.3 describes the ongoing research made in the field of the through the wall radar opening to the opportunity to extract m-D also in the presence of obstacles.
From this introduction to radar micro-Doppler it is evident how the ability to extract such an information from the returned signal can play an important role in modern automatic target recognition systems. In the following sections, recent advances in the analysis of micro-Doppler signatures from specific radar platforms are reviewed.
3.1 Micro-Doppler in radar imaging systems
Radar imaging techniques such as synthetic aperture radar (SAR), inverse synthetic aperture radar (ISAR) and bistatic synthetic aperture radar (BSAR) are well established and useful techniques to acquire high-resolution images of an area of interest from both airborne or space borne sensors. The amount of information provided from these systems is extremely high and its exploitation is still a growing field of research. For example, automatic target recognition based on the reflectivity images is a field of research with great interest and presenting many signal processing and design challenges .
The extraction of m-D signatures from radar imaging systems for target classification purposes is an emerging technique. The research in this field provided interesting developments and results in the last years. This section will review the advances in the exploitation of the radar imaging platform for m-D analysis, showing that from this kind of sensors a significant advantage can be taken if the m-D information can be extracted.
3.1.1 Synthetic aperture radar
Synthetic aperture radar provides high resolution images of static ground scenes. The observed scenes may contain moving targets. As a result the processed image will change depending on the kind of target motion. Vibrating and rotating targets are two canonical cases that are often present within a scene. Rotating radar search antennas, wind turbines and vehicles that vibrate due to the engine are typical targets that exhibit micro-motions and related m-D characteristics. It is often possible to observe these micro-motions from a SAR. The analysis of the m-D features has been modelled and experimentally characterised for different type of targets, configurations and working frequencies over the last decade.
where r is the rotating radius, w r is the rotating frequency and Θ(T) is the instantaneous aspect angle .
Silverstein and Hawkins  analytically derived the m-D signature of rotating objects in Synthetic Aperture Radar and highlighted differences with the vibrating case. They pointed out that the order required in the approximation of the expansion of the phase history needed to be greater than first order in the case of a rotation, while for the vibrating case a first order approximation was sufficient. The main reason was that in a rotational target the motion was large compared to the wavelength so a numerical analysis was required.
These ghost targets can appear stronger than the original target thus leading to incorrect interpretations of the focussed image that can mask other targets of interest.
The most relevant recent work on monostatic SAR m-D was presented in . In this paper, it was pointed out that in most of the previous work the targets were assumed to be point scatterers and that this assumption may not always agree with reality. The effect of micro-motions using distributed scatterers and localized scatterer model was addressed in this paper. Also more accurate and complex micro-motion models are introduced for rotating, vibrating sinusoidal moving and rocking targets, providing a good set of tools for the analysis in SAR. Two novel models for the phase history of the received echoes described in  includes: (1) A sawtooth expansion for a more accurate model of the phase modulation introduced by micro-motions and (2) a generalized paired echoes principle (PEP) to model the effect on the focussed image the general cases like a rocking ship.
An analysis of the effect on focussed images was also reported in  with the distinction of different micro-motions and their amplitude and frequency characteristics. The effect on different imaging algorithms was reported showing that the focussing algorithm plays a key rule in the final effect on the image. The loss in azimuth resolution and the GMTI and MTI problems introduced by micro-motions was analysed. These aspects are not of direct interest in this review and will not be further considered.
All the previously presented papers focussed on the demonstration of the models of micro-Doppler signatures from SAR. For this reason all experiments were carried in cases of high signal to clutter ratio (SCR). This assumption often is not valid and strong clutter can bury the useful micro-Doppler signature.
To deal with this problem an extraction technique is required. In , the Singular Spectrum Analysis was applied to simulated SAR data. The applied procedure starts from the range compressed data, then selecting the range gate containing the scatterer with micro motions. Depending on the analysed configuration a range cell migration correction step can be performed before the extraction of the range gate. In order to align the signal within the time-frequency plane to obtain a correct visualization and positioning of the time-frequency distribution, the Doppler centroid and the azimuth frequency slope is removed from the signal before the SSA. After the selection of the singular components the resulting micro-Doppler signature can be visualized and used for target classification through the computation of a time frequency distribution.
The effectiveness of the technique was demonstrated in low SCR with K-distributed clutter that model correctly the case of sea clutter in SAR and bistatic SAR clutter.
3.1.2 Bistatic SAR
However, as the use of a bistatic configuration reduces the m-D amplitude it is possible to keep the signature inside the azimuth Doppler bandwidth. This means that it is possible to use a smaller minimum PRF to avoid aliasing in the analysis compared to the monostatic configuration.
As in the monostatic case the vibration introduces paired echoes in the resulting bistatic SAR image, the signal model can still be expanded into a series of Bessel functions of the first kind but with a different value of the argument of the Bessel functions depending on the bistatic factor. This introduces an acquisition geometry dependence of the effect on the focussed image.
In order to address the issues of micro-Doppler hiding in clutter and the absence of the micro-Doppler signature if the target is moving along the radial direction of the common monostatic SAR a displaced phase center array (DPCA) based technique was proposed in . The authors proposed a configuration with a fixed receiver and a dual channel receiver separated by phase centres. The two channel information is exploited to cancel the stationary clutter and keep the incoherent signal due to the micro-motion of the target. In addition the authors exploited a Fractional Fourier Transform based estimation of the signal Doppler to correct the motion of the transmitting platform. However, two main limitations are present in this proposed technique: synchronization and modulation of the backscattered energy from the target with micro-motions. The authors assumed that all the synchronization issues for the proposed configuration are accurately corrected, however for the problem under analysis even a small error on the synchronization of the two channel signals with the receiver would make the extraction of the micro-Doppler ineffective. The second issue is the presence of a sinusoidal modulation of the amplitude of the energy of the micro-Doppler signature due to the DPCA processing. This would produce a weaker micro-Doppler signal that could be difficult to detect and exploited for classification purposes in a noisy environment.
3.1.3 Inverse SAR
In inverse synthetic aperture radar imaging the radar platform is stationary and the target is moving with the assumption of a quasi-constant linear motion. Other target motions will produce errors in the final focussed image. The Doppler resolution is inversely related to the total time of coherently processed pulses. If the duration of the coherent processing interval (CPI) is too long, then uncompensated motion could affect the image producing smearing in the Doppler dimension, affecting the Doppler resolution too.
Specific analysis and experiments about the analysis of wheels and pedestrian in ISAR was presented in . The analysis first focussed on the model of the m-D effect from wheels of civil vehicles, demonstrating (with both simulated and real data) the possibility of extracting all the information required to correctly identify size, position, aspect angle and rotating frequency of a rotating wheel. The analysis then considered the case of the pedestrian, where an analysis of the features characterizing walking/running was reported. The paper demonstrated also the possibility to obtain information of the gait of the pedestrian using of time-frequency analysis on both simulated and real data.
In the last decade, however, the interest of the ISAR community moved from the analysis of the m-D signatures to the most challenging problem of the compensation of the micro-motions in order to obtain good focussed ISAR images. In , a good review of the m-D analysis in ISAR was presented. Methods for analysing, visualizing, exploiting m-D signatures are reviewed in conjunction with examples of models for rigid and non-rigid body motions. Finally,  introduces perspective in ISAR m-D exploitation.
The next part of this review section will focus on the imaging techniques used to mitigate the effect of micro-motions. In , a novel imaging method of birds was proposed, wherein a technique to remove the flapping spectrogram from the data firstly by the variety of moving average values of the cross-correlation coefficients of the high-resolution range profiles, and then removing the flapping spectrogram. In addition genetic algorithms and minimum waveform entropy were used for phase compensation. The effectiveness of the technique was demonstrated by simulations.
A real time imaging technique is proposed in . The proposed technique used a time-frequency s-method based on the relationship between the short time Fourier transform and the Wigner Distribution. The s-method was tested on simulated data of a Boing-717, a MIG-25, a Delta Wing and a Canadian Coast Guard Vessel providing good results in terms of motion compensation and computational efficiency.
The case of fast rotating targets was addressed in the technique proposed in . In this paper a segmental Pseudo keystone transform (SPKT) was introduced to correct the migration through the range cells (MTRC) of fast rotating targets. The proposed algorithm applies the keystone transform on segments of the data before performing the image formation. The technique was shown to be effective on simulated data achieving good computational efficiency with an increase in the number of segments used to divide the data. In addition the algorithm tested on simulated data provided high resolution and robustness with respect to the noise.
In , two techniques for separation of the rigid part from rotating parts were proposed. The first used the order statistic technique applied on the time frequency distributions, the second used the Radon transform. Both techniques were shown to provide good results on simulated data. However, the authors stated that the Radon transform based technique is limited and works on very emphatic micro-Doppler cases. The use of the order statistic technique requires an increased computational load that was not quantified in the paper. In addition both techniques were tested on simulated data with high signal to clutter ratio, while a more common case is of micro-Doppler signatures embedded in strong clutter.
The presented papers demonstrate how the information coming from the micro-motions of a target can affect the return to a SAR/BSAR/ISAR platform. This family of radar systems provide the advantage to analyse the target reflectivity image of the target jointly with its micro-motion features. The radar imaging community could then take advantage of this complementary source of information for example to remove uncertainty in automatic target recognition systems.
3.2 Ultrasound micro-Doppler
In the last decade there has been a growing interest in applying m-D techniques to air acoustic sensors operating at ultrasound frequencies. In particular the literature shows a number of investigations where ultrasound m-D target signatures have been used to detect and classify humans and animals with operating frequencies ranging from 20 kHz up to around 80 kHz. There are a number of reasons for this; firstly the electromagnetic spectrum is becoming over-congested and hence the use of acoustics can become a valid substitute in situation where long range detection is not a tight requirement. Second the hardware, including the transducers and the electronics, is on the average much cheaper than its radar counter part although, recently, a range of low-power and cost effective super-etherodyne radars have become available off-the-shelf. In particular, for a much lower price, it is possible to operate at frequencies that corresponds to small wavelengths and hence high Doppler shifts; for example the Doppler shift due to a target that moves with a speed of 10 m/s at 40 kHz in the ultrasound regime is the same as that achieved by a radar operating at 34 GHz. On the other hand the use of acoustic sensors has a significant drawback compared to RF systems. The attenuation of acoustic pressure waves in air increases dramatically with frequency and the maximum operating range is often limited to a few tens of metres.
This experiment was repeated in  to gather m-D signatures of a) a 9.5 lb Miniature Pinscher in a 30 feet long indoor corridor, b) an American Pitbull and a horse in a farm and c) two personnel targets walking together in a parking lot. The experimental set up was the same of  but an accelerometer was placed on the humans’ ankle to verify the m-D signatures. The authors showed that the signatures of these four-legged animals are significantly different to those of humans.
A low cost (about 20 USD) acoustic Doppler sensor (ADS) made of off-the-shelf components was developed in  to collect m-D signatures of personnel targets at 40 kHz. m-D signatures of humans walking towards and away from the sensor were collected with the ADS positioned at about waist height. A small loudspeaker, with diameter as small as 1 wavelength (8.6 mm), was used to achieve a sonar beamwidth of about 60 degrees. The Doppler signatures were then used to extract Cepstral features to feed a Bayesian classifier modelled with Gaussian mixtures. Experiments were repeated for different locations and set ups with about 30 targets (males and females). Classification performance was assessed between, targets walking away vs target walking towards the sensor, male vs female and identification performance was also assessed. It is interesting to observe that in this paper the information contained in the main Doppler shift of main bulk of the targets is used to perform classification.
Following this work, Zhang et al.  significantly reduced their 1 MHz sampling rate of  by deploying a simple and cost-effective bandpass sampling approach. They repeated their experiments with the modified system and gathered m-D signatures of eight humans, five males and three females, with the new system and the old system simultaneously. The signatures were used to extract Cepstral features from the data and to perform classification with a Bayesian classifier based on Gaussian mixture models. Classification results are presented and performance is compared to that obtained in .
Another 40 kHz ultrasound sensor for collection of m-D signatures of personnel targets is presented in . As in previous work, this is a low cost, compact system characterised by a low processing power. This system was deployed in a variety of experiments aiming at demonstrate that m-D signatures can be successfully used to classify between different human actions. The experiments were carried out for different geometries and with targets moving on and off a treadmill. The authors showed that classification performance of a k-Nearest-Neighbour classifier reached values over 95 % average correct classification when 50 % of the signatures were randomly selected to form the training set. The same sensor was used in  to investigate classification performance of a k-NN classifier as a function of what the authors named “clusters”, for recognition of gender and for biometric. An experimental trial aiming at collecting multimodal acoustic data including m-D signatures is described in .
The papers presented here show 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 classification by ultrasound m-D signatures is achievable and they might potentially open up a new research avenue looking at the exploitation of these techniques in future systems.
3.3 The use of micro-Doppler in through-the-wall applications
Through-the-wall applications represent another area where the use of m-D signatures for target classification may provide a significant breakthrough. Indeed, increasing research efforts have been dedicated to assessing the impact of through-the-wall propagation on m-D signatures in the last years and this is because, in the last decade, the ability to detect, track and classify moving targets behind walls has been become imperative to address many security and safety issues. Some example are applications such as non-invasive indoor surveillance, search-and-rescue missions, anti terrorism, urban warfare and law enforcement.
In , Chen et al. present a through-the-wall noise radar that combines target detection, target imaging and m-D capabilities. Here the authors present the results of an experimental trial that was carried out to collect m-D signatures of moving humans behind walls performing a number of activities which included arm waving and breathing. Data was gathered for targets moving a) behind a wall made of 10 cm-thick concrete blocks and 2) behind a plastic shed. Results demonstrate promising capabilities for classification of human activities by through-the-wall m-D signatures.
Micro Doppler signatures have also been studied to quantify the undesired errors induced by movement on the estimation of the position of human targets behind walls . Also m-D signatures have been used to detect and classify humans around corners . These techniques, however, rely on multipath propagation and hence on wall reflections rather than through- the-wall propagation.
The presented papers shows the potential of the through wall m-D signatures for target detection and classification. Much more work is expected to be done in order to quantify the performances. For example studies to be carried on should deal with different system bandwidths, carrier frequencies, type of wall and scenarios behind the wall. This is still an early stage for this research but the impact and benefits that can be achieved are significant.
We have presented a review of the work published in the last 10 years on radar m-D target classification using SAR and ISAR systems as well as the use of this method in key emerging radar applications such as bistatic SAR, through-the-wall radars and ultrasound radars.
The research on imaging radars demonstrated that the m-D features presented in the return can be used for target classification with the potential to be integrated into existing automatic target recognition systems and improve target classification capabilities.
The research on ultrasound radars presented here demonstrated that target detection and classification by ultrasound micro-Doppler signatures is achievable and showed that ultrasound sensors could represent a cost effective alternative to radar systems for applications that do not require long range detection. Indeed, they might potentially open up a new research avenue looking at the exploitation of these techniques in future systems.
The review of through-the-wall research activities indicated that m-D signatures may not be significantly affected by through-the wall propagation. This represents a first attempt to tackle the problem of detection and classification of targets behind walls with m-D signatures and much more work is expected to quantify performance. This research showed that potentially there are significant advantages and benefits of using m-D techniques for this application.
Overall research into m-D is highly active and fast moving and demonstrated that m-D techniques can provide important solutions to many radar target classification challenges.
This study was supported by the Engineering and Physical Sciences Research Council of the United Kingdom (grants N. EP/H012877/1 and EP/H011625/1), the UK MoD University Defence Research Centre in Signal Processing and Selex-Galileo Edinburgh.
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