Super-Resolution Time of Arrival Estimation Using Random Resampling in Compressed Sensing

There is a strong demand for super-resolution time of arrival (TOA) estimation techniques for radar applications that can that can exceed the theoretical limits on range resolution set by frequency bandwidth. One of the most promising solutions is the use of compressed sensing (CS) algorithms, which assume only the sparseness of the target distribution but can achieve super-resolution. To preserve the reconstruction accuracy of CS under highly correlated and noisy conditions, we introduce a random resampling approach to process the received signal and thus reduce the coherent index, where the frequency-domain-based CS algorithm is used as noise reduction preprocessing. Numerical simulations demonstrate that our proposed method can achieve super-resolution TOA estimation performance not possible with conventional CS methods

Frequency Estimation Using Complex-Valued Shifted Window Transformer

Estimating closely spaced frequency components of a signal is a fundamental problem in statistical signal processing. In this letter, we introduce 1-D real-valued and complex-valued shifted window (Swin) transformers, referred to as SwinFreq and CVSwinFreq, respectively, for line-spectra frequency estimation on 1-D complex-valued signals. Whereas 2-D Swin transformer-based models have gained traction for optical image super-resolution, we introduce for the first time a complex-valued Swin module designed to leverage the complex-valued nature of signals for a wide array of applications. The proposed approach overcomes the limitations of the classical algorithms such as the periodogram, MUSIC, and OMP in addition to state-of-the-art deep learning approach cResFreq. SwinFreq and CVSwinFreq boast superior performance at low signal-to-noise ratio SNR and improved resolution capability while requiring fewer model parameters than cResFreq, thus deeming it more suitable for edge and mobile applications. We find that the real-valued Swin-Freq outperforms its complex-valued counterpart CVSwinFreq for several tasks while touting a smaller model size. Finally, we apply the proposed techniques for radar range profile super-resolution using real data. The results from both synthetic and real experimentation validate the numerical and empirical superiority of SwinFreq and CVSwinFreq to the state-of-the-art deep learning-based techniques and traditional frequency estimation algorithms. The code and models are publicly available at https://github.com/josiahwsmith10/spectral-super-resolution-swin.Comment: Submitted to IEEE Geoscience and Remote Sensing Letter

Sparse Bases and Bayesian Inference of Electromagnetic Scattering

Many approaches in CEM rely on the decomposition of complex radiation and scattering behavior with a set of basis vectors. Accurate estimation of the quantities of interest can be synthesized through a weighted sum of these vectors. In addition to basis decompositions, sparse signal processing techniques developed in the CS community can be leveraged when only a small subset of the basis vectors are required to sufficiently represent the quantity of interest. We investigate several concepts in which novel bases are applied to common electromagnetic problems and leverage the sparsity property to improve performance and/or reduce computational burden. The first concept explores the use of multiple types of scattering primitives to reconstruct scattering patterns of electrically large targets. Using a combination of isotropic point scatterers and wedge diffraction primitives as our bases, a 40% reduction in reconstruction error can be achieved. Next, a sparse basis is used to improve DOA estimation. We implement the BSBL technique to determine the angle of arrival of multiple incident signals with only a single snapshot of data from an arbitrary arrangement of non-isotropic antennas. This is an improvement over the current state-of-the-art, where restrictions on the antenna type, configuration, and a priori knowledge of the number of signals are often assumed. Lastly, we investigate the feasibility of a basis set to reconstruct the scattering patterns of electrically small targets. The basis is derived from the TCM and can capture non-localized scattering behavior. Preliminary results indicate that this basis may be used in an interpolation and extrapolation scheme to generate scattering patterns over multiple frequencies

Study of processing techniques for radar non-cooperative target recognition.

Radar is a powerful tool for detecting and tracking airborne targets such as aircraft and missiles by day and night. Nowadays, it is seen as a genuine solution to the problem of target recognition. Recent events showed that cooperative means of identification such as the IFF transponders carried by most aircraft are not entirely reliable and can be switched off by terrorists. For this reason, it is important that target identification be obtained through measurements and reconnaissance based on non-cooperative techniques. In practice, recognition is achieved by comparing the electromagnetic sig nature of a target to a set of others previously collected and stored in a library. Such signatures generally represent the targets reflectivity as a function of space. A common representation is known as one-dimensional high-resolution range-profile (HRRP) and can be described as the projection of the reflectivity along the direction of propagation of the wave. When the measured signature matches a template, the target is identified. The main drawback of this technique is that signatures greatly vary with aspect-angle so that measurements must be made for many angles and in three dimensions. This implies a potentially large cost as large datasets must be created, stored and processed. Besides, any modification of the target structure may yield incorrect classification results. Instead, other processing techniques exist that rely on recent mathematical algorithms. These techniques can be used to extract target features directly from the radar data. Because of the direct relation with target geometry, these feature-based methods seem to be suitable candidates for reducing the need of large databases. However, their performances and their domains of validity are not known. This is especially true when it comes to real targets for at least three reasons. First, the performance of the methods varies with the signal-to-noise ratio. Second, man-made targets arc often more complex than just a set of independent theoretical point-like scatterers. Third, these targets are made up of a large number of scattering elements so that mathematical assumptions are not met. In conclusion, the physical correctness of the computational models are questionable. This thesis investigates the processing techniques that can be used for non-cooperative target recognition. It demonstrates that the scattering-centre extraction is not suitable for the model-based approach. In contrast, it shows that the technique can be used with the feature-based approach. In particular, it investigates the recognition when achieved directly in the z-domain and proposes a novel algorithm that exploits the information al ready in the database for identifying the signal features that corresponds to physical scatterers on the target. Experiments involving real targets show that the technique can enhance the classification performance and therefore could be used for non-cooperative target recognition

Synthetic aperture radar target detection, feature extraction, and image formation techniques

This report presents new algorithms for target detection, feature extraction, and image formation with the synthetic aperture radar (SAR) technology. For target detection, we consider target detection with SAR and coherent subtraction. We also study how the image false alarm rates are related to the target template false alarm rates when target templates are used for target detection. For feature extraction from SAR images, we present a computationally efficient eigenstructure-based 2D-MODE algorithm for two-dimensional frequency estimation. For SAR image formation, we present a robust parametric data model for estimating high resolution range signatures of radar targets and for forming high resolution SAR images

Subspace-based methodologies for the non-cooperative identification of aircraft by means of a synthetic database of radar signatures

Una de las principales preocupaciones dentro del mundo de la aviación es la identificación rápida, eficaz y fiable de cualquier objeto observado que se encuentre a cualquier distancia y bajo cualquier condición atmosférica. Gracias a los avances en tecnología radar, esto se ha conseguido. De hecho, los radares son los sensores más adecuados para el reconocimiento de blancos en vuelo ya que pueden operar en cualquier condición. El reconocimiento de blancos mediante radar es hoy un hecho, existiendo sistemas IFF (Identification Friend or Foe) capaces de comunicarse con una aeronave haciendo posible que ella misma se identifique por sí sola. Sin embargo, esta necesidad de comunicación directa puede ser un inconveniente en ciertos momentos. Así, aparecen las técnicas no cooperativas o NCTI (Non-Cooperative Target Identification), que no establecen ninguna comunicación con el blanco y normalmente hacen uso de radares de alta resolución. Éstos ven los blancos como compuestos por diversos puntos que dispersan la energía emitida por el radar, generando así una imagen de la reflectividad de un blanco, lo que se ha llamado su firma radar. Comparando dicha firma radar con una base de datos de firmas radar de blancos conocidos es posible establecer, mediante una serie de algoritmos de identificación, el tipo de blanco iluminado por el radar. Uno de los temas más cuestionados es cómo poblar y actualizar esta base de datos de firmas radar. De manera ideal, la base de datos debería de contener medidas de blancos reales en vuelo; desafortunadamente, la principal desventaja de esta estrategia radica en la dificultad de obtener firmas radar de aviones neutrales o enemigos. Por esta razón, esta tesis propone utilizar firmas radar de blancos ideales, generadas mediante simulaciones electromagnéticas, como base de datos. Con el avance de las herramientas de predicción electromagnética es posible obtener de manera rápida y a bajo coste firmas radar de cualquier blanco deseado y en cualquier orientación. De este modo, el principal objetivo de esta tesis yace en el desarrollo de algoritmos eficientes de identificación de aeronaves en vuelo de manera no cooperativa, con altas tasas de acierto y empleando una base de datos de blancos obtenida mediante simulación electromagnética. El escenario propuesto consiste en la comparación de firmas radar reales obtenidas en una campaña de medidas con una base de datos compuesta por firmas radar simuladas, con ello se pretende por un lado, simular un escenario más realista, en el que las firmas de los blancos recogidas por el radar no tienen porqué tener la misma calidad que aquellas de la base de datos y por otro, comprobar que la identificación de un avión real mediante simulaciones es posible

Bayesian super-resolution with application to radar target recognition

This thesis is concerned with methods to facilitate automatic target recognition using images generated from a group of associated radar systems. Target recognition algorithms require access to a database of previously recorded or synthesized radar images for the targets of interest, or a database of features based on those images. However, the resolution of a new image acquired under non-ideal conditions may not be as good as that of the images used to generate the database. Therefore it is proposed to use super-resolution techniques to match the resolution of new images with the resolution of database images. A comprehensive review of the literature is given for super-resolution when used either on its own, or in conjunction with target recognition. A new superresolution algorithm is developed that is based on numerical Markov chain Monte Carlo Bayesian statistics. This algorithm allows uncertainty in the superresolved image to be taken into account in the target recognition process. It is shown that the Bayesian approach improves the probability of correct target classification over standard super-resolution techniques. The new super-resolution algorithm is demonstrated using a simple synthetically generated data set and is compared to other similar algorithms. A variety of effects that degrade super-resolution performance, such as defocus, are analyzed and techniques to compensate for these are presented. Performance of the super-resolution algorithm is then tested as part of a Bayesian target recognition framework using measured radar data

Radio astronomical imaging in the presence of strong radio interference

Radio-astronomical observations are increasingly contaminated by interference, and suppression techniques become essential. A powerful candidate for interference mitigation is adaptive spatial filtering. We study the effect of spatial filtering techniques on radio astronomical imaging. Current deconvolution procedures such as CLEAN are shown to be unsuitable to spatially filtered data, and the necessary corrections are derived. To that end, we reformulate the imaging (deconvolution/calibration) process as a sequential estimation of the locations of astronomical sources. This not only leads to an extended CLEAN algorithm, the formulation also allows to insert other array signal processing techniques for direction finding, and gives estimates of the expected image quality and the amount of interference suppression that can be achieved. Finally, a maximum likelihood procedure for the imaging is derived, and an approximate ML image formation technique is proposed to overcome the computational burden involved. Some of the effects of the new algorithms are shown in simulated images. Keywords: Radio astronomy, synthesis imaging, parametric imaging, interference mitigation, spatial filtering, maximum likelihood, minimum variance, CLEAN.Comment: 27 pages, 7 figures. Paper with higher resolution color figures at http://cobalt.et.tudelft.nl/~leshem/postscripts/leshem/imaging.ps.g