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Using the sparse property of the signal from a marine target with quadratic modulated frequency (QFM) micromotion signature, the detection in sparse domain is studied. An algorithm for separation of sea clutter and micro-Doppler signal is proposed based on the Morphological Component Analysis (MCA). At first, the signal model of a marine target with micromotion is established, which can be modelled...
In this paper, we consider sparse reconstruction techniques for motion detection in through-the-wall radar imaging applications. We use change detection formulations to mitigate the clutter caused by strong exterior and interior wall scatterings in order to detect moving people inside enclosed structures. Performance of the proposed sparsity-based scheme is evaluated using real-data collected with...
We present an autofocus algorithm for Compressed SAR Imaging. The technique estimates and corrects for 1-D phase errors in the phase history domain, based on prior knowledge that the reflectivity field is sparse, as in the case of strong scatterers against a weakly-scattering background. The algorithm relies on the Sparsity Driven Autofocus (SDA) method and Augmented Lagrangian Methods (ALM), particularly...
With meter-resolution images delivered by modern SAR satellites like TerraSAR-X and TanDEM-X, it is now possible to map urban areas from space in very high level of details using advanced interferometric techniques such as persistent scatterer interferometry and tomographic SAR (TomoSAR), whereas these multi-pass interferometric techniques are based on a great number of images. We aim at improving...
In the inverse synthetic aperture radar (ISAR) number of target reflectors is small resulting in the fact that ISAR images are sparse. Since the ISAR image is obtained using the Fourier transform of the input signal then this problems can be treated within processing of sparse signals. The compressive sensing (CS) theory proves that, under some conditions, exact reconstruction of sparse signals is...
In this paper, we generalize Huber's criterion to multichannel sparse recovery problem of complex-valued measurements where the objective is to find good recovery of jointly sparse unknown signal vectors from the given multiple measurement vectors which are different linear combinations of the same known elementary vectors. This requires careful characterization of robust complex-valued loss functions...
Synthetic Aperture Radar (SAR) tomography has seen a strong evolution in the past years as it has shown to be a worthwhile tool for analysing data obtained with high range resolution interferometric SAR sensors at pixel size. In particular by transforming them into a 3-D, 4-D, and in general Multi-Dimensional SAR image. The resolution in the higher dimensions, for instance in elevation and time, depends...
We experimentally demonstrate a photonic RF sampling system that utilizes chirp processing of ultrafast laser pulses to achieve all-optical high-rate pseudorandom patterning and inner product integration for compressed sensing measurement. We successfully acquire multi-tone sparse radio frequency (RF) signals at arbitrary offsets from the reconstruction basis frequencies in an 11.95 GHz bandwidth...
We consider the problem of estimating a finite number of atoms from a dictionary embedded in white noise, using a sparse signal representation (SSR) approach, a problem which is relevant in many radar applications. In particular, the estimation of a radar scene consisting of targets with wide amplitude range can be challenging since the sidelobes of a strong target can disrupt the estimation of a...
Morphological Component Analysis is a technique to separate morphological different components from an image or signal. Morphological difference is in this case measured by the dictionary incoherence of the corresponding components. We propose to use a local discrete cosine transform to represent the periodic ground clutter and an undecimated wavelet transform to represent the piecewise smooth target...
Compressive sensing followed by direct processing of the measurements with no reconstruction has the potential to enable high resolution measurement acquisition and processing with inexpensive hardware and at low computational cost. Compressive sensing and processing (CSP) is then a suitable option for high resolution delay-Doppler radar. CSP, however, increases ambiguity function surface (AFS) sidelobes...
Synthetic Aperture Radar (SAR) Tomography is able to detect targets, measuring their position extended in the height dimension. SAR tomography has the problem that, in order to achieve high resolution in the vertical direction, a Multi-Baseline (MB) radar geometry with a great vertical aperture has to be designed. In SAR Tomography, the tomographic resolution is also inversely proportional to the...
Satellite Synthetic Aperture Radar (SAR) systems can achieve vertical resolution by using multiple platform passes to create a synthetic aperture in the elevation direction. Often the distribution of the orbits is not optimal for traditional forms of tomographic processing. Due to the random nature of the orbit positions and the relatively small number of measurements, a compressive sensing (CS) approach...
In this paper, the problem of detecting and localizing multiple scatterers in SAR tomography, starting from compressed measurements is considered. This problem can be addressed as the detection of a sparse signal within the compressed domain and can be approached in the framework of Compressive Sensing (CS) theory. While CS literature has focused on the problem of signal reconstruction, this is frequently...
By varying the rate of linear frequency modulation (LFM) signal, radar could effectively penalize a Digital Radio Frequency Memory (DRFM) detective jammer to some extent. Since the enemy utilizes inaccurate information of the radar signal to generate false targets. However, with the increment of false targets and the disturbance-signal-ratio (DSR) of radar echoed signal, the capacity of traditional...
Utilizing the time differences of arrival of a signal, impinging on a distributed sensor network, is a well known approach for the location estimation of a radio wave emitter. The high sampling rate necessary for a precise positioning implies a huge amount of data exchange between the sensor nodes. Contrary, the absence of high data rate enabled backbone links, connecting the nodes, restricts the...
Since a nested phased array cannot directly estimate the range of targets due to range ambiguity, this paper proposes a nested array with diverse time-delayers for target range and angle estimation. The essence is to construct a new array structure by systematically nesting two uniform linear arrays through diverse time-delays which yields a range-dependent receiving array beampattern. Using second-order...
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