As a summary for this introduction to UWB surveillance radars, there has been significant advances in the past 40 years to enable this important capability for both military and geophysical applications. However, the short-range and low altitude of piloted airborne systems made practical battlefield radar systems unattractive. RPVs (or UAVs in today's vocabulary) were starting to be developed. More importantly, the availability of data links would enable significant processing and image interpretation on the ground. Data links provided line-of-sight connection to command and control centers at a safer distance to the airborne platform.

The development of coherent radars and digital processing provided several critical innovations to improve the detection and characterization of man-made targets in cultural clutter. The first aspect of mode development was having an understanding of the propagation of radar waveforms, along with the signal loss due to target scintillation and propagation through media. Propagation in free space was well understood. However, when there existed anisotropic media between the radar and the desired target or clutter return, significant measurements of these characteristics were undertaken. This is especially true for propagation through foliage, use of polarization and understanding the dependence of wavelength on the radar return.

This chapter covers the image formation processing for ultra-wideband (UWB) syn-thetic aperture radar (SAR) applications. As the desire for more spatial resolution is considered, the signal bandwidth and SAR integration angles needed to be pushed to the limit of current component capabilities. However, if Moore's Law continues, these radio frequency (RF) and digital bandwidths will continue to be available from the device development communities.

This chapter investigates ground moving target radar modes for surveillance from air-to-ground radar systems. This mode has been important, starting with both Multiple Aperture Surveillance Radar (MASR) and Standoff Target Acquisition System (SOTAS) to detect slow-moving targets in a battlefield environment. Its technical challenges are the detection of slow-moving targets that are masked by the clutter Doppler within the main beam of the radar antenna. A wide range of algorithms have been developed over the past 50 years, as the radio frequency (RF) components and high-speed signal processing techniques have been improved [1]. But as the technology advances, several new algorithms and system architectures were invented, based on the advances in component technologies. These techniques include obtaining high resolution of the targets for discrimination, and moving to longer wavelength radar systems to counter the masking and techniques that the adversary uses to avoid detection [2]. In addition to military ground moving target indication (GMTI) techniques, there are several civilian and geoscience applications for characterizing terrain, forests and ecological measurements using radar images.

This chapter presents a history and some future directions for multimode ultra-wideband (UWB) surveillance radars. Many radar applications are now multimode operation, including moving target detection and track, synthetic aperture radar (SAR), interferometric SAR and adaptive processing to cancel radio frequency interference (RFI) and clutter. For analog radars, the components were limited by lack of correlation or coherence between the channels, waveforms and target characteristics. The solution to this approach was to create high-resolution modes and post-process the signal returns to eliminate spatial and spectral differences between the hardware signal paths. As these efforts were repeatedly reported in the literature, there are significant improvements in performance as a result of paralleling the transmit, receiver and signal processing paths in the system. The goal of many UWB multimode radar is to maintain a high area coverage rate (ACR) in search and selectively improve the resolution of the imaging or detection by additional signal processing.