Along-track interferometry (ATI) has the ability to generate high-quality synthetic aperture radar (SAR) images and concurrently detect and estimate the positions of ground moving target indicators (GMTI) with moderate processing requirements. This paper focuses on several different ATI system configurations, with an emphasis on low-cost configurations employing no active electronic scanned array (AESA). The objective system has two transmit phase centers and four receive phase centers and supports agile adaptive radar behavior. The advantages of multistatic, multiple input multiple output (MIMO) ATI system configurations are explored. The two transmit phase centers can employ a ping-pong configuration to provide the multistatic behavior. For example, they can toggle between an up and down linear frequency modulated (LFM) waveform every other pulse. The four receive apertures are considered in simple linear spatial configurations. Simulated examples are examined to understand the trade space and verify the expected results. Finally, actual results are collected with the Space Dynamics Laboratorys (SDL) FlexSAR system in diverse configurations. The theory, as well as the simulated and actual SAR results, are presented and discussed.
We present an efficient and computationally simple approach for synthetic aperture radar (SAR) imaging in cases when the radar data have gaps, due to missing pulses and/or notches in the frequency band. Our method is a simple variation of gradient projection, in which the search path in each iteration is obtained by projecting the negative-gradient of the L1 norm onto a hyper-plane defining solutions which are consistent with the data. The computations are not complicated since the L1 gradient is simply equal to the sign() of the pixels in the image. Computational efficiency is obtained by incorporating the polar format algorithm, which accomplishes the projection operation using a fast Fourier transform. Sample results are presented using the AFRL Gotcha 2006 radar data set and the Space Dynamics Laboratory FlexSAR system.
Clutter suppression interferometry (CSI) has received extensive attention due to its multi-modal capability to detect slow-moving targets, and concurrently form high-resolution synthetic aperture radar (SAR) images from the same data. The ability to continuously augment SAR images with geo-located ground moving target indicators (GMTI) provides valuable real-time situational awareness that is important for many applications. CSI can be accomplished with minimal hardware and processing resources. This makes CSI a natural candidate for applications where size, weight and power (SWaP) are constrained, such as unmanned aerial vehicles (UAVs) and small satellites. This paper will discuss the theory for optimal CSI system configuration focusing on sparse time-varying transmit and receive array manifold due to SWaP considerations. The underlying signal model will be presented and discussed as well as the potential benefits that a sparse time-varying transmit receive manifold provides. The high-level processing objectives will be detailed and examined on simulated data. Then actual SAR data collected with the Space Dynamic Laboratory (SDL) FlexSAR radar system will be analyzed. The simulated data contrasted with actual SAR data helps illustrate the challenges and limitations found in practice vs. theory. A new novel approach incorporating sparse signal processing is discussed that has the potential to reduce false- alarm rates and improve detections.
Simultaneously estimating position x and velocity v of moving targets using only the measured phase ' from single-channel SAR is impossible because the mapping from (x, v) to φis many-to-one. This paper defines classes of equivalent target motion and solves the GMTI problem up to membership in an equivalence class using single-channel SAR phase data. Definitions are presented for endo- and exo-clutter that are consistent with the equivalence classes, and it is shown that most target motion can be detected, i.e. the set of endo-clutter targets is very small. We exploit the sparsity of moving targets in the scene to develop an algorithm to resolve target motion up to membership in an equivalence class, and demonstrate the effectiveness of the proposed technique using simulated data.
Three dimensional scene reconstruction with synthetic aperture radar (SAR) is desirable for target recognition and improved scene interpretability. The vertical aperture, which is critical to reconstruct 3D SAR scenes, is almost always sparsely sampled due to practical limitations, which creates an underdetermined problem. This papers explores 3D scene reconstruction using a convex model-based approach. The approach developed is demonstrated on 3D scenes, but can be extended to SAR reconstruction of sparsely sampled signals in the spatial and, or, frequency domains. The model-based approach enables knowledge-aided image formation (KAIF) by incorporating spatial, aspect, and sparsity magnitude terms into the image reconstruction. The incorporation of these terms, which are based on prior scene knowledge, will demonstrate improved results compared to traditional image formation algorithms. The SAR image formation problem is formulated as a second order cone program (SOCP) and the results are demonstrated on 3D scenes using simulated data and data from the GOTCHA data collect.<sup>1</sup> The model-based results are contrasted against traditional backprojected images.
The Eyesafe Ladar Test-bed (ELT) is an experimental ladar system with the capability of digitizing return laser pulse waveforms at 2 GHz. These waveforms can then be exploited off-line in the laboratory to develop signal processing techniques for noise reduction, range resolution improvement, and range discrimination between two surfaces of similar range interrogated by a single laser pulse. This paper presents the results of experiments with new deconvolution algorithms with the hoped-for gains of improving the range discrimination of the ladar system. The sparsity of ladar returns is exploited to solve the deconvolution problem in two steps. The first step is to estimate a point target response using a database of measured calibration data. This basic target response is used to construct a dictionary of target responses with different delays/ranges. Using this dictionary ladar returns from a wide variety of surface configurations can be synthesized by taking linear combinations. A sparse linear combination matches the physical reality that ladar returns consist of the overlapping of only a few pulses. The dictionary construction process is a pre-processing step that is performed only once. The deconvolution step is performed by minimizing the error between the measured ladar return and the dictionary model while constraining the coefficient vector to be sparse. Other constraints such as the non-negativity of the coefficients are also applied. The results of the proposed technique are presented in the paper and are shown to compare favorably with previously investigated deconvolution techniques.
Synthetic aperture radar (SAR) collections that integrate over a wide range of aspect angles hold the potentional
for improved resolution and fosters improved scene interpretability and target detection. However, in practice
it is difficult to realize the potential due to the anisotropic scattering of objects in the scene. The radar cross
section (RCS) of most objects changes as a function of aspect angle. The isotropic assumption is tacitly made
for most common image formation algorithms (IFA). For wide aspect scenarios one way to account for anistropy
would be to employ a piecewise linear model. This paper focuses on such a model but it incorporates aspect and
spatial magnitude filters in the image formation process. This is advantageous when prior knowledge is available
regarding the desired targets’ RCS signature spatially and in aspect. The appropriate filters can be incorporated
into the image formation processing so that specific targets are emphasized while other targets are suppressed.
This is demonstrated on the Air Force Research Laboratory (AFRL) GOTCHA1 data set to demonstrate the
utility of the proposed approach.