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Complex Reed-Solomon codes recently gained attention in deterministic Compressed Sensing (CS) schemes. This allows to exploit ideas and algorithms from algebraic coding theory for reconstruction or recovery in CS. We show how intrinsic soft decision information can be recursively enhanced, allowing decoding/recovery beyond the conventional capabilities. This recursive enhancement is based on error/erasure...
We introduce a dual-deconvolution algorithm for the purpose of black-space spectrum sharing. We consider the case of spectrum sharing between radio-frequency (RF) devices whose data source/scatterers are modeled as a convolution with a pulse-shaping filter. This model encompasses both single-carrier communications as well as pulse-Doppler radars. Preliminary results including the mean-square error...
We present a time delay estimation method for sparse translation-invariant signals, off the grid. We enforce sparsity by imposing penalties based on an atomic norm to consider signals in a continuous domain. Our formulation leads to an atomic norm minimization problem. A number of recent theoretical results demonstrate that an atomic norm minimization can be solved using a semidefinite programming...
The implementation of computational sensing strategies often faces calibration problems typically solved by means of multiple, accurately chosen training signals, an approach that can be resource-consuming and cumbersome. Conversely, blind calibration does not require any training, but corresponds to a bilinear inverse problem whose algorithmic solution is an open issue. We here address blind calibration...
A variety of problems arising in imaging, radar, and localization amount to recover a signal that is sparse in a continuous indexed dictionary. Line spectral estimation theory considers a particular instance of this problem, namely that of estimating the frequency and magnitude components of a sum of complex sinusoids from uniformly spaced samples. In this work we consider the significantly more general...
In this paper we consider the classical problem of blind deconvolution of multiple signals from its superposition, also called blind demixing and deconvolution. One is given a signal ∑ri=1 wi ∗ xi = y ∊ RL which is the superposition of r unknown source signals {xi}ri=1 and convolution kernels {wi}ri=1 The goal is to reconstruct the vectors w; and x;, which are elements of known but random subspaces...
The problem of chirped Synthetic Aperture Radar (SAR) is the high vulnerability of the received information, under Electromagnetic (EM) Corruption. This paper proposes a valid recovery solution of SAR Single Look Complex (SLC)1 images observed with low band signals. The recovery of high resolution images is made by Super-Resolution (SR) Signal Processing (SP), based on Spectrum Extrapolation (SE),...
Commonly ISAR/SAR images can be considered as sparse. Therefore they can be obtained from a reduced set of measurements (pulses) by using Compressive Sensing reconstruction techniques. In this paper we analyze influence of the pulse selection strategy to the uniqueness of the obtained radar image from a reduced set of pulses. A simple algorithm for optimal pulse selection strategy is proposed. It...
For multi-channel synthetic aperture radar (SAR), it is an essential task to estimate the covariance matrix of a particular resolution cell across channels. When sparsity is present in SAR imaging, compressive sensing (CS) has shown its superiority in both reducing the SAR system complexity and enhancing the performance. However, existing CS methods do not work effectively with multi-channel SAR systems...
In Synthetic Aperture Radar (SAR) image reconstruction, the quality of the image depends on the range and cross range resolution to resolve multiple target positions. If the distance between the targets is less than both SAR resolutions, the radar imaged them as one single target. To overcome this challenge, Orthogonal Frequency Division Multiplexing (OFDM) SAR has been proposed to improve the image...
Multi-circular synthetic aperture radar (MCSAR) has the full 360° 3-D imaging ability by using multiple circular tracks at different elevation angles. Compressive sensing (CS) based imaging methods provide a solution for MCSAR elevation reconstruction when the circular tracks distribute sparsely and non-uniformly. When processing the MCSAR real data, the issues of off-grid effect and the spurious...
A major difficulty in tracking unidentified vessels at sea is due to the need to observe large areas for long periods of time for sparsely appearing targets. A possible solution is to use a network of energy efficient underwater gliders that use buoyancy engines and are equipped with sensor array, processing, and communication hardware. Gliders, however, have a limited size and payload which limits...
We present the design and hardware implementation of a radar prototype that demonstrates the principle of a sub-Nyquist collocated multiple-input multiple-output (MIMO) radar. The setup allows sampling in both spatial and spectral domains at rates much lower than dictated by the Nyquist sampling theorem. Our prototype realizes an X-band MIMO radar that can be configured to have a maximum of 8 transmit...
We propose an online blind deconvolution approach to sequential through-the-wall-radar-imaging (TWI) where the received signal is contaminated by front wall ringing artifacts. The sequential measurements correspond to individual transmitter-receiver pairs where the front wall ringing induces a multipath kernel that corrupts the received target reflections. The convolution kernels may vary across sequential...
Radar-detection metrics are assessed in outcomes of sparse-signal processing (SSP) with test statistics based on the subgradient and the dual feasibility in the SSP optimization via an approach separating false alarms (FAs) from targets. In radar, SSP is aimed for estimating a sparse solution whose FAs are fixed and whose detection of targets is optimal as in traditional detection. Existing detection...
Radar bird detection and discrimination has many civilian and non-civilian applications such as collision avoidance, false alarm reduction for detection radars, stealthy target detection, classification of military unmanned aerial vehicles (UAVs) and civilian drones, and conservation ecology. In order to develop new and improve existing detection and discrimination algorithms, this paper proposes...
Synthetic Aperture Radar (SAR) systems produce a tremendous amount of redundant data if persistent radar surveillance of a specific area is implemented. This paper performs an efficient data reduction extrapolating maritime targets in motion from background subtraction. The technique is based on Robust Principal Component Analysis (RPCA). The algorithm is implemented by Convex Programming (CP). This...
In pulsed Time-of-Flight (ToF) systems the pulse width establishes a tradeoff between depth resolution and range. Ideally, one would wish to emit a pulse that is as short as allowed by the hardware, while keeping the depth range arbitrarily large, being able to sense more than one return per pixel. This suggests the use of compressive sensing (CS) as sensing paradigm, in order to exploit the sparsity...
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