The authors consider a bistatic configuration with a stationary transmitter transmitting unknown waveforms of opportunity and a single moving receiver and present a deep learning (DL) framework for passive synthetic aperture radar (SAR) imaging. They approach DL from an optimisation based perspective and formulate image reconstruction as a machine learning task. By unfolding the iterations of a proximal gradient descent algorithm, they construct a deep recurrent neural network (RNN) that is parameterised by the transmitted waveforms. They cascade the RNN structure with a decoder stage to
form a recurrent auto-encoder architecture. They then use backpropagation to learn transmitted waveforms by training the network in an unsupervised manner using SAR measurements. The highly non-convex problem of backpropagation is guided to
a feasible solution over the parameter space by initialising the network with the known components of the SAR forward model. Moreover, prior information regarding the waveform structure is incorporated during initialisation and backpropagation. They
demonstrate the effectiveness of the DL-based approach through numerical simulations that show focused, high contrast imagery using a single receiver antenna at realistic signal-to-noise-ratio levels.
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Clustering on Sparse Data in Non-overlapping Feature Space with Applications to Cancer Subtyping
This paper presents a new algorithm, Reinforced and Informed Network-based Clustering (RINC), for finding unknown groups of similar data objects in sparse and largely non-overlapping feature space where a network structure among features can be observed. Sparse and non-overlapping unlabeled data become increasingly common and available
especially in text mining and biomedical data mining. RINC inserts a domain informed model into a modelless neural network. In particular, our approach integrates physically
meaningful feature dependencies into the neural network architecture and soft computational constraint. Our learning algorithm efficiently clusters sparse data through integrated
smoothing and sparse auto-encoder learning. The informed design requires fewer samples for training and at least part of the model becomes explainable. The architecture of the reinforced network layers smooths sparse data over the network dependency in the feature space. Most importantly, through back-propagation, the weights of the reinforced smoothing layers are simultaneously constrained by the remaining sparse auto-encoder layers that set the target values to be equal to the raw inputs. Empirical results demonstrate that RINC achieves improved accuracy and renders physically meaningful clustering results.
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- Award ID(s):
- 1743010
- NSF-PAR ID:
- 10096099
- Date Published:
- Journal Name:
- 2018 IEEE International Conference on Data Mining (ICDM)
- Page Range / eLocation ID:
- 1079 - 1084
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
