Target Recognition Using Late-Time Returns from Ultra-Wideband, Short-Pulse Radar

The goal of this research is to develop algorithms that recognize targets by exploiting properties in the late-time resonance induced by ultra-wide band radar signals. A new variant of the Matrix Pencil Method algorithm is developed that identifies complex resonant frequencies present in the scattered signal. Kalman filters are developed to represent the dynamics of the signals scattered from several target types. The Multiple Model Adaptive Estimation algorithm uses the Kalman filters to recognize targets. The target recognition algorithm is shown to be successful in the presence of noise. The performance of the new algorithms is compared to that of previously published algorithms

Sampling and Super-resolution of Sparse Signals Beyond the Fourier Domain

Recovering a sparse signal from its low-pass projections in the Fourier domain is a problem of broad interest in science and engineering and is commonly referred to as super-resolution. In many cases, however, Fourier domain may not be the natural choice. For example, in holography, low-pass projections of sparse signals are obtained in the Fresnel domain. Similarly, time-varying system identification relies on low-pass projections on the space of linear frequency modulated signals. In this paper, we study the recovery of sparse signals from low-pass projections in the Special Affine Fourier Transform domain (SAFT). The SAFT parametrically generalizes a number of well known unitary transformations that are used in signal processing and optics. In analogy to the Shannon's sampling framework, we specify sampling theorems for recovery of sparse signals considering three specific cases: (1) sampling with arbitrary, bandlimited kernels, (2) sampling with smooth, time-limited kernels and, (3) recovery from Gabor transform measurements linked with the SAFT domain. Our work offers a unifying perspective on the sparse sampling problem which is compatible with the Fourier, Fresnel and Fractional Fourier domain based results. In deriving our results, we introduce the SAFT series (analogous to the Fourier series) and the short time SAFT, and study convolution theorems that establish a convolution--multiplication property in the SAFT domain.Comment: 42 pages, 3 figures, manuscript under revie

Applications of airborne remote sensing in atmospheric sciences research

This paper explores the potential for airborne remote sensing for atmospheric sciences research. Passive and active techniques from the microwave to visible bands are discussed. It is concluded that technology has progressed sufficiently in several areas that the time is right to develop and operate new remote sensing instruments for use by the community of atmospheric scientists as general purpose tools. Promising candidates include Doppler radar and lidar, infrared short range radiometry, and microwave radiometry

On the Resolution Improvement of Radar Target Identification with Filtering Antenna Effects

An investigation on the improvement of the resolution of a radar target identification system is presented in this paper. Degradation of resolution is mainly due to influence factors associated with antennas, including the strong coupling between transmitting and receiving antennas and the variation in the antenna response. A filtering technique was therefore introduced to mitigate the underlying problem. In the technique, the antenna effects were filtered out of the total response backscattered from the objects in the radar target identification system. The short-time matrix pencil method (STMPM) was then employed to extract the poles from the backscattered response in order to identify the object. Simulation and experimentation examples are illustrated to confirm the improvement of the resolution by filtering the antenna effects. The simulation and experimentation were divided into several categories, that is, different antennas and differently shaped objects, in order to validate the advantage of filtering the antenna effects. They were setup in order to demonstrate that the poles obtained from performing the STMPM without the filtering technique were mainly because of the antenna rather than the object’s characteristic. The results showed that the resolution of the identification was significantly increased when performing pole extraction and filtering the antenna effects

Radar Technology

In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design

Accuracy of Singularity Expansion Method in Time and Frequency Domains to Characterize Antennas in Presence of Noise

International audienceIn this paper, the accuracy of the singularity expansion method (SEM) used for antenna characterization is investigated. A well-known limitation of the SEM is that pole extraction is very sensitive to noise. A comparison between two main methods of pole extraction is presented. The matrix pencil (MP) method and the Cauchy's method are used to extract poles from the radiated fields of a dipole antenna and two bowtie antennas. Results are presented for simulated fields, and the robustness to a white Gaussian noise is also analyzed. We show that the MP method allows working with lower SNR than Cauchy's method and is more accurate for field reconstruction

Bistatic SAR data acquisition and processing using SABRINA-X, with TerraSAR-X as the opportunity transmitter

This thesis investigates the acquisition and processing of Bistatic SAR data using SABRINA-X, and with TerraSAR-X as the transmitter of opportunity. SABRINA-X is an X-band receiver system that has been recently designed at the UPC Remote-Sensing Laboratory, while TerraSARX is a German satellite for SAR-based active remote-sensing. Prior to the particular case of acquiring TerraSAR-X signals, the hardware aspects of SABRINAX have been investigated further, and improved as necessary (or suggested for up-gradation in future). Two successful data acquisitions have been carried out, to obtain bistatic SAR images of the Barcelona harbor, with the receiver set-up at the close-by Montjuïc hill. Each acquisition campaign necessitated an accurate prediction of the satellite overpass time and precise orientation of the antennas to acquire the direct signal from the satellite and the backscattered signals off the viewed terrain. The thesis also investigates the characteristics of the acquired signals, which is critical as regards the subsequent processing for imaging and interferometric applications. The hardware limitations, combined with ‘off-nominal’ transmissions of the satellite, necessitate improved range processing of the acquired signals. The thesis expounds the possible range compression techniques, and suggests ways for improved compression, thereby improving the quality of the subsequently processed images

Ultra Wideband Transient Scattering and Its Applications to Automated Target Recognition

Reliable radar target recognition has long been the holy grail of electromagnetic sensors. Target recognition based on the singularity expansion method (SEM) uses a time-domain electromagnetic signature and has been well studied over the last few decades. The SEM describes the late time period of the transient target signature as a sum of damped exponentials with natural resonant frequencies (NRFs). The aspect-independent and purely target geometry and material-dependent nature of the NRF set make it an excellent feature set for target characterization. In this chapter, we aim to review the background and the state of the art of resonance-based target recognition. The theoretical framework of SEM is introduced, followed by signal processing techniques that retrieve the target-dependent NRFs embedded in the transient electromagnetic target signatures. The extinction pulse, a well-known target recognition technique, is discussed. This chapter covers recent developments in using a polarimetric signature for target recognition, as well as using NRFs for subsurface sensing applications. The chapter concludes with some highlights of the ongoing challenges in the field

Phase History Decomposition for Efficient Scatterer Classification in SAR Imagery

A new theory and algorithm for scatterer classification in SAR imagery is presented. The automated classification process is operationally efficient compared to existing image segmentation methods requiring human supervision. The algorithm reconstructs coarse resolution subimages from subdomains of the SAR phase history. It analyzes local peaks in the subimages to determine locations and geometric shapes of scatterers in the scene. Scatterer locations are indicated by the presence of a stable peak in all subimages for a given subaperture, while scatterer shapes are indicated by changes in pixel intensity. A new multi-peak model is developed from physical models of electromagnetic scattering to predict how pixel intensities behave for different scatterer shapes. The algorithm uses a least squares classifier to match observed pixel behavior to the model. Classification accuracy improves with increasing fractional bandwidth and is subject to the high-frequency and wide-aperture approximations of the multi-peak model. For superior computational efficiency, an integrated fast SAR imaging technique is developed to combine the coarse resolution subimages into a final SAR image having fine resolution. Finally, classification results are overlaid on the SAR image so that analysts can deduce the significance of the scatterer shape information within the image context