1,251 research outputs found
DopNet:A Deep Convolutional Neural Network to Recognize Armed and Unarmed Human Targets
The work presented in this paper aims to distinguish
between armed or unarmed personnel using multi-static radar
data and advanced Doppler processing. We propose two modified
Deep Convolutional Neural Networks (DCNN) termed SCDopNet
and MC-DopNet for mono-static and multi-static micro-
Doppler signature (μ-DS) classification. Differentiating armed
and unarmed walking personnel is challenging due to the effect
of aspect angle and channel diversity in real-world scenarios.
In addition, DCNN easily overfits the relatively small-scale μ-DS
dataset. To address these problems, the work carried out in this
paper makes three key contributions: first, two effective schemes
including data augmentation operation and a regularization
term are proposed to train SC-DopNet from scratch. Next,
a factor analysis of the SC-DopNet are conducted based on
various operating parameters in both the processing and radar
operations. Thirdly, to solve the problem of aspect angle diversity
for μ-DS classification, we design MC-DopNet for multi-static μ-
DS which is embedded with two new fusion schemes termed
as Greedy Importance Reweighting (GIR) and `21-Norm. These
two schemes are based on two different strategies and have been
evaluated experimentally: GIR uses a “win by sacrificing worst
case” whilst `21-Norm adopts a “win by sacrificing best case”
approach. The SC-DopNet outperforms the non-deep methods
by 12.5% in average and the proposed MC-DopNet with two
fusion methods outperforms the conventional binary voting by
1.2% in average. Note that we also argue and discuss how to
utilize the statistics of SC-DopNet results to infer the selection
of fusion strategies for MC-DopNet under different experimental
scenarios
Deep Learning Methods for Remote Sensing
Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing
Remote Sensing
This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas
A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications
The commercial availability of low-cost millimeter wave (mmWave)
communication and radar devices is starting to improve the penetration of such
technologies in consumer markets, paving the way for large-scale and dense
deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the
same time, pervasive mmWave access will enable device localization and
device-free sensing with unprecedented accuracy, especially with respect to
sub-6 GHz commercial-grade devices. This paper surveys the state of the art in
device-based localization and device-free sensing using mmWave communication
and radar devices, with a focus on indoor deployments. We first overview key
concepts about mmWave signal propagation and system design. Then, we provide a
detailed account of approaches and algorithms for localization and sensing
enabled by mmWaves. We consider several dimensions in our analysis, including
the main objectives, techniques, and performance of each work, whether each
research reached some degree of implementation, and which hardware platforms
were used for this purpose. We conclude by discussing that better algorithms
for consumer-grade devices, data fusion methods for dense deployments, as well
as an educated application of machine learning methods are promising, relevant
and timely research directions.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys &
Tutorials (IEEE COMST
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