Performance Analysis of Classification Algorithms for Activity Recognition using Micro-Doppler Feature

Classification of different human activities using micro-Doppler data and features is considered in this study, focusing on the distinction between walking and running. 240 recordings from 2 different human subjects were collected in a series of simulations performed in the real motion data from the Carnegie Mellon University Motion Capture Database. The maximum the micro-Doppler frequency shift and the period duration are utilized as two classification criterions. Numerical results are compared against several classification techniques including the Linear Discriminant Analysis (LDA), Naïve Bayes (NB), K-nearest neighbors (KNN), Support Vector Machine(SVM) algorithms. The performance of different classifiers is discussed aiming at identifying the most appropriate features for the walking and running classification

A new energy detector of micro-emboli using a time-varying threshold

International audienceDetection of small emboli, precursors of Cerebrovascular Accidents, is a worldwide concern since CVAs represent the second cause of mortality in the world. Computerized analysis of Transcranial Doppler signals can aid early detection of circulating emboli and micro-emboli. Commercially used systems of automatic emboli detection rely on standard short time Fourier transform techniques in which detection is based on constant thresholds. These standard algorithms do not offer robust detections and are incapable of detecting the smallest micro-emboli. To enhance this detection, we propose in this study optimized techniques based on novel methods of threshold application. By implementing our new time-varying threshold of detection, we were able to decrease the probability of non-detection and the probability of false alarm by around half the values obtained by standard techniques. Moreover, our new techniques were clearly efficient in exploiting the transient-like embolic signals and hence make detection of micro-emboli easier and more evident. This was proved by enhancing important parameters of which are the embolus to blood ratio and the peak to threshold ratio. Applied on our set of recorded signals, the new detectors allowed obtaining embolus to blood ratios twice greater than the embolus to blood ratios achieved by standard techniques and a sufficient increase in peak to threshold ratios

Reliable detection and characterisation of dim target via track-before-detect

Detection of manoeuvring and small objects is a challenging task in radar surveillance applications. Small objects in high noise background induce low signal to noise ratio (SNR) reflections. Conventional methods detect such objects by integrating multiple reflections in the same range-bearing and doppler bins in sampled versions of received signals. When the objects manoeuvre, however, these methods are likely to fail to detect them because the integration is performed without taking into account the possibility of the object movements across resolution bins. Furthermore, slowly manoeuvring objects create detection difficulties in discriminating them from radar clutter. Reflections of such objects contain micro-Doppler shifts generated by their propulsion devices. These shifts can characterise specific types of objects. In this case, estimation of these shifts is a challenging task because the front-end signals at the receiver are low SNR reflections and are the superposition of all reflections from the entire object and the noise background. Conventional estimators for this purpose only use reflections collected in a coherent processing interval (CPI) and produce poor estimate outputs. In order to achieve the desired accuracy, one requires more reflections than those collected in a CPI. This thesis mainly considers the aforementioned two difficulties and aims to develop efficient algorithms, which can detect low SNR and manoeuvring objects by incorporating long-time pulse integration and micro-doppler estimation. Main contributions in this thesis are based on the following two algorithms. The first work considers the detection of manoeuvring and small objects with radars. The radar systems are considered both co-located and separated transmitter/receiver pairs, i.e., monostatic and bistatic configurations, respectively, as well as multistatic settings involving both types. The proposed detection algorithm is capable of coherently integrating reflected signals within a CPI in all these configurations and continuing integration for an arbitrarily long time across consecutive CPIs. This approach estimates the complex value of the reflection coefficients for the integration while simultaneously estimating the object trajectory. Compounded with this simultaneous tracking and reflection coefficient estimation is the estimation of the unknown time reference shift of the separated transmitters necessary for coherent processing. The detection is made by using the resulting integration value in a Neyman-Pearson test against a constant false alarm rate threshold. The second work focuses on micro-Doppler signature estimation of manoeuvring and small rotor based unmanned aerial vehicle (UAV) systems with a monostatic radar. The micro-Doppler signature is considered rotation frequencies generated by rotating rotor blades of the UAVs. This estimation uses a maximum likelihood (ML) approach that finds rotation frequencies to maximise a likelihood function conditioned on an object trajectory, complex reflection coefficients, and rotation frequencies. In particular, the proposed algorithm uses an expectation-maximisation (EM) approach such that the expectation of the likelihood mentioned above is approximated by using the state distributions generated from Bayesian recursive filtering for the trajectory estimation. The reflection coefficients and the rotation frequencies are estimated by maximising this approximated expectation. As a result, this algorithm is capable of simultaneously tracking the trajectory and estimating the reflection coefficients and the rotation frequencies of the UAVs before the decision on the object presence is made

Adaptive Signal Processing Techniques and Realistic Propagation Modeling for Multiantenna Vital Sign Estimation

Tämän työn keskeisimpänä tavoitteena on ihmisen elintoimintojen tarkkailu ja estimointi käyttäen radiotaajuisia mittauksia ja adaptiivisia signaalinkäsittelymenetelmiä monen vastaanottimen kantoaaltotutkalla. Työssä esitellään erilaisia adaptiivisia menetelmiä, joiden avulla hengityksen ja sydämen värähtelyn aiheuttamaa micro-Doppler vaihemodulaatiota sisältävät eri vastaanottimien signaalit voidaan yhdistää. Työssä johdetaan lisäksi realistinen malli radiosignaalien etenemiselle ja heijastushäviöille, jota käytettiin moniantennitutkan simuloinnissa esiteltyjen menetelmien vertailemiseksi. Saatujen tulosten perusteella voidaan osoittaa, että adaptiiviset menetelmät parantavat langattoman elintoimintojen estimoinnin luotettavuutta, ja mahdollistavat monitoroinnin myös pienillä signaali-kohinasuhteen arvoilla.This thesis addresses the problem of vital sign estimation through the use of adaptive signal enhancement techniques with multiantenna continuous wave radar. The use of different adaptive processing techniques is proposed in a novel approach to combine signals from multiple receivers carrying the information of the cardiopulmonary micro-Doppler effect caused by breathing and heartbeat. The results are based on extensive simulations using a realistic signal propagation model derived in the thesis. It is shown that these techniques provide a significant increase in vital sign rate estimation accuracy, and enable monitoring at lower SNR conditions

A Signal Processing Method for Artefact Rejection in Transcranial Doppler Signals used for Micro-embolus detection

International audienceArtefacts are High Intensity Transient Signals that appear in the Doppler signal due to probe tapping, probe displacement, patient movement and other external factors during TCD recording. In Holter TCD, the number of artefacts are further increased due to the servo-controlled probe positioning and long recording time. Current artefact rejection methods must be adapted to the new holter devices. Therefore, in this paper we present a complete procedure for eliminating two types of artefacts that occur during Holter TCD. The latter two types are bidirectional areftact, occurring in the energy of both positive and negative frequencies, and unidirectional artefacts occuring only in the energy of the negative frequencies. From a dataset of 25 signals, 218 artefact signatures were identified; 95% of which are bidirectional and 5% unidirectional. As a final result, 98% of the artefacts where successfully removed

Radar target classification by micro-Doppler contributions

This thesis studies non-cooperative automatic radar target classification. Recent developments in silicon-germanium and monolithic microwave integrated circuit technologies allows to build cheap and powerful continuous wave radars. Availability of radars opens new applications in different areas. One of these applications is security. Radars could be used for surveillance of huge areas and detect unwanted moving objects. Determination of the type of the target is essential for such systems. Microwave radars use high frequencies that reflect from objects of millimetre size. The micro-Doppler signature of a target is a time-varying frequency modulated contribution that arose in radar backscattering and caused by the relative movement of separate parts of the target. The micro-Doppler phenomenon allows to classify non-rigid moving objects by analysing their signatures. This thesis is focused on designing of automatic target classification systems based on analysis of micro-Doppler signatures. Analysis of micro-Doppler radar signatures is usually performed by second-order statistics, i.e. common energy-based power spectra and spectrogram. However, the information about phase coupling content in backscattering is totally lost in these energy-based statistics. This useful phase coupling content can be extracted by higher-order spectral techniques. We show that this content is useful for radar target classification in terms of improved robustness to various corruption factors. A problem of unmanned aerial vehicle (UAV) classification using continuous wave radar is covered in the thesis. All steps of processing required to make a decision out of the raw radar data are considered. A novel feature extraction method is introduced. It is based on eigenpairs extracted from the correlation matrix of the signature. Different classes of UAVs are successfully separated in feature space by support vector machine. Within experiments or real radar data, achieved high classification accuracy proves the efficiency of the proposed solutions. Thesis also covers several applications of the automotive radar due to very high growth in technologies for intelligent vehicle radar systems. Such radars are already build-in in the vehicle and ready for new applications. We consider two novel applications. First application is a multi-sensor fusion of video camera and radar for more efficient vehicle-to-vehicle video transmission. Second application is a frequency band invariant pedestrian classification by an automotive radar. This system allows us to use the same signal processing hardware/software for different countries where regulations vary and radars with different operating frequency are required. We consider different radar applications: ground moving target classification, aerial target classification, unmanned aerial vehicles classification, pedestrian classification. The highest priority is given to verification of proposed methods on real radar data collected with frequencies equal to 9.5, 10, 16.8, 24 and 33 GHz

Terahertz Micro-Doppler Radar for Detection and Characterization of Multicopters

abstract: The micromotions (e.g. vibration, rotation, etc.,) of a target induce time-varying frequency modulations on the reflected signal, called the micro-Doppler modulations. Micro-Doppler modulations are target specific and may contain information needed to detect and characterize the target. Thus, unlike conventional Doppler radars, Fourier transform cannot be used for the analysis of these time dependent frequency modulations. While Doppler radars can detect the presence of a target and deduce if it is approaching or receding from the radar location, they cannot identify the target. Meaning, for a Doppler radar, a small commercial aircraft and a fighter plane when gliding at the same velocity exhibit similar radar signature. However, using a micro-Doppler radar, the time dependent frequency variations caused by the vibrational and rotational micromotions of the two aircrafts can be captured and analyzed to discern between them. Similarly, micro-Doppler signature can be used to distinguish a multicopter from a bird, a quadcopter from a hexacopter or a octacopter, a bus from a car or a truck and even one person from another. In all these scenarios, joint time-frequency transforms must be employed for the analysis of micro-Doppler variations, in order to extract the targets’ features. Due to ample bandwidth, THz radiation provides richer radar signals than the microwave systems. Thus, a Terahertz (THz) micro-Doppler radar is developed in this work for the detection and characterization of the micro-Doppler signatures of quadcopters. The radar is implemented as a continuous-wave (CW) radar in monostatic configuration and operates at a low-THz frequency of 270 GHz. A linear time-frequency transform, the short-time Fourier transform (STFT) is used for the analysis the micro-Doppler signature. The designed radar has been built and measurements are carried out using a quadcopter to detect the micro-Doppler modulations caused by the rotation of its propellers. The spectrograms are obtained for a quadcopter hovering in front of the radar and analysis methods are developed for characterizing the frequency variations caused by the rotational and vibrational micromotions of the quadcopter. The proposed method can be effective for distinguishing the quadcopters from other flying targets like birds which lack the rotational micromotions.Dissertation/ThesisMasters Thesis Electrical Engineering 201

Spectral analysis of embolic signals

Tese de dout., Engenharia Electrónica e Computação, Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2005Os parâmetros espectrais do sinal Doppler são usados na caracterização de fluxo sanguíneo. No caso particular do fluxo em artéria cerebral média, a caracterização pode incluir a detecção e classificação de embolias. Para este efeito pretende-se estudar o desempenho de métodos de análise espectral, nomeadamente os que tenham demonstrado bons resultados quando aplicados a sinais Doppler em outras artérias. Para melhor quantificar o desempenho dos estimadores espectrais, é necessário conhecer à priori as características particulares do sinal, facto que se pretende como objectivo final deste estudo. No sentido de disponibilizar sinais-referência para a análise do desempenho dos estimadores espectrais na detecção de embolias, foi desenvolvido um simulador de sinais de artéria cerebral média, com e sem embolias. Como entradas do simulador são utilizadas curvas médias extraídas de sinais clínicos, recorrendo a um algoritmo criado para o efeito, o Sequential Phase Shift Averaging. São também definidas pelo utilizador características dos êmbolos, tais como, velocidade, dimensão efectiva e potência devolvidas pela instrumentação ultra-sónica. Durante este estudo considerou-se o fluxo sanguíneo caracterizado por quatro parâmetros espectrais: frequência máxima, frequência média, raiz quadrada de meia largura de banda, e, variação da potência ultra-sónica ao longo do tempo; este último como sendo o mais relevante para a identificação e diferenciação dos êmbolos. Recorrendo aos sinais simulados, e, analisando os espectros dos sinais de fluxo sem embolias, verifica-se que a Short Time Fourier Transform estima melhor os parâmetros espectrais referidos do que a distribuição tempo-frequência de Choi- Williams ou o método paramétrico tempo-frequência de Covariância Modificada. A análise de espectros de sinais simulados de fluxo com embolias demonstra uma performance idêntica entre os métodos de análise temporal e a Short Time Fourier Transform, esta na versão em que o espectro do ciclo cardíaco é composto por elevada taxa de sobreposição de espectros de segmentos desse ciclo. Esta condicionante associada à constatação de que uma mesma embolia é captada distintamente consoante iii o local do ciclo cardíaco em observação induziu a criação de uma nova representação espectral. A representação proposta, de nome Space-frequency representation, permite a identificação visual da passagem do êmbolo pela janela de observação ultra-sónica. A pesquisa da existência do êmbolo é feita em função da velocidade sanguínea máxima instantânea, e a visualização da potência ultra-sónica por ele retornada é dimensionada adaptativamente de acordo com a relação espaço-frequência instantânea calculada. Esta metodologia permitirá introduzir vantagens significativas no diagnóstico clínico da circulação do fluxo sanguíneo em artéria cerebral média.UNESCO. MAGIAS (Métodos Avanzados de Generación de Imágenes Acústicas)

Exploration of Micro-Doppler Signatures Associated with Humans and Dogs using UWB Radar

The work in this thesis has been a part of the task of using a radar to separate between humans and animals in a alarm and surveillance context. For the radar to be able to separate between humans and animals it would use a classifier that rely on features extracted from the radar data. The thesis considers two types of targets; either a human or a dog, and by using micro-Doppler signature, determines some fundamen- tal features which can be the used to classify them. The micro-Doppler signature is the superposition of frequency modulations represented in the joint time and Doppler frequency domain, where the modulations are caused by dierent moving components associated with the desired target. The micro-Doppler signature has been widely used for radar classification. The thesis has succeeded in developing algorithms and a system to extract micro- Doppler signatures from targets. Signatures from both humans and dogs has been produced and some simple features extracted from them. The major problem with the signatures created is that the radars pulse repetition frequency is a limiting factor and causes aliasing in the Doppler spectrum that corrupts the signatures. This has limited the study of targets to slow moving humans and dogs. Three important features for classification was extracted from the micro-Doppler signature by calculating the gait-Doppler map. They are, i) the average Doppler fre- quency fav(or average radial velocity vav), ii) fundamental gait frequency fg and iii) the stride length Ls which is derived from the two former features. The result points towards the possibility to separate humans and dogs using these parameters. The reason is that since the dogs limbs are shorter than a human it also has shorter stride length at a specific speed. However, this may not be sucient for decisions to be made in a robust alarm system, since it can be fooled by a smart intruder that could for example take unnatural short steps and simulate a dogs combination of the aforementioned features. In addition the determination of features are sensitive to large changes in radial speed. This can be mitigated by preprossing before the calculation of the features. The conclusion is, that based on substantial measurements of signatures ( approx. 50 series) and calculations of the three features one has arrived at a fairly robust method to distinguish between the two type of target in this thesis