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

Improving Landmark Localization with Semi-Supervised Learning

We present two techniques to improve landmark localization in images from partially annotated datasets. Our primary goal is to leverage the common situation where precise landmark locations are only provided for a small data subset, but where class labels for classification or regression tasks related to the landmarks are more abundantly available. First, we propose the framework of sequential multitasking and explore it here through an architecture for landmark localization where training with class labels acts as an auxiliary signal to guide the landmark localization on unlabeled data. A key aspect of our approach is that errors can be backpropagated through a complete landmark localization model. Second, we propose and explore an unsupervised learning technique for landmark localization based on having a model predict equivariant landmarks with respect to transformations applied to the image. We show that these techniques, improve landmark prediction considerably and can learn effective detectors even when only a small fraction of the dataset has landmark labels. We present results on two toy datasets and four real datasets, with hands and faces, and report new state-of-the-art on two datasets in the wild, e.g. with only 5\% of labeled images we outperform previous state-of-the-art trained on the AFLW dataset.Comment: Published as a conference paper in CVPR 201

Global Context Vision Transformers

We propose global context vision transformer (GC ViT), a novel architecture that enhances parameter and compute utilization for computer vision tasks. The core of the novel model are global context self-attention modules, joint with standard local self-attention, to effectively yet efficiently model both long and short-range spatial interactions, as an alternative to complex operations such as an attention masks or local windows shifting. While the local self-attention modules are responsible for modeling short-range information, the global query tokens are shared across all global self-attention modules to interact with local key and values. In addition, we address the lack of inductive bias in ViTs and improve the modeling of inter-channel dependencies by proposing a novel downsampler which leverages a parameter-efficient fused inverted residual block. The proposed GC ViT achieves new state-of-the-art performance across image classification, object detection and semantic segmentation tasks. On ImageNet-1K dataset for classification, the tiny, small and base variants of GC ViT with 28M, 51M and 90M parameters achieve 83.4%, 83.9% and 84.4% Top-1 accuracy, respectively, surpassing comparably-sized prior art such as CNN-based ConvNeXt and ViT-based Swin Transformer. Pre-trained GC ViT backbones in downstream tasks of object detection, instance segmentation, and semantic segmentation on MS COCO and ADE20K datasets outperform prior work consistently, sometimes by large margins. Code and pre-trained models are available at https://github.com/NVlabs/GCViT.Comment: 15 pages, 8 figure

Classification of pedestrians, vehicles and animals using an automotive radar

El interés creciente en los ámbitos de seguridad y prevención de accidentes en carretera ha propiciado en los últimos años una gran evolución en el desarrollo de sistemas de detección automática de obstáculos buscando evitar las colisiones. En concreto, en países como Finlandia se ha realizado especial hincapié, debido al gran número de accidentes causados por animales (principalmente renos) que se cruzan en las carreteras, causando grandes daños, físicos y económicos. Es por eso que la investigación centrada en evitar colisiones es de suma importancia allí. La Universidad Tecnológica de Tampere ha comenzado a trabajar en uno de estos sistemas, centrando el trabajo en los departamentos de procesado de señal e ingeniería de comunicaciones. El objetivo de este proyecto es desarrollar un sistema que sea capaz de detectar objetos y, además, clasificarlos distinguiendo tres grupos: peatones, vehículos y animales. Para ello se parte desde cero, lo cual implica que el trabajo abarcará desde la toma inicial de muestras de los posibles objetivos, hasta el desarrollo de todas las etapas posteriores, incluyendo una primera aproximación al futuro clasificador. Como primer paso, se analizarán varios sistemas de detección (radar, videocámaras, etc.) y se elegirá uno de ellos de forma razonada. Se tomarán muestras de los diferentes objetivos con dicho sistema, y serán analizadas. Para ello se elegirá el método de filtrado y análisis más apropiado. Una vez se tenga una representación gráfica de las diferentes muestras, el segundo paso será la implementación de un decisor y un posterior clasificador, que sea capaz de discriminar entre las tres posibles categorías que se estudian: peatones, vehículos y animales (renos). De nuevo se considerarán varias opciones y se elegirá aquella que pueda proporcionar mejores resultados. Terminados estos pasos, cualquier señal procedente del detector debería ser automáticamente filtrada y analizada, para ser capaz rápidamente de decidir si hay un obstáculo o no, y clasificarlo en alguno de los tres grupos. El objetivo de este proyecto es que, más adelante, pueda ser aplicado a un sistema capaz de generar una respuesta mecánica en el coche después de haber detectado un obstáculo en la carretera, para evitar posibles colisiones