Measurement kernel design for HRR imaging of urban objects

Targets of interest in urban applications often include relatively small objects such as chairs, tables, doors, and potential contraband such as handheld weapons. Therefore, radar imaging and classification of these objects can impose extremely high bandwidth and aperture requirements. Regarding the radar bandwidth, the typical manner of obtaining for wideband waveforms is to implement swept or stepped waveforms that are instantaneously narrowband, but cover a wide bandwidth over time. On the other hand, some operational scenarios require relatively fast data collection and instantaneously wideband waveforms, which necessitate either expensive high-speed analog-to-digital converters or compressive, sub-Nyquist sampling. In this chapter, we investigate sub-Nyquist sampling of instantaneously wideband waveforms. Our objective is to optimize analog compression kernels for the underlying goals of imaging and/or recognition of small objects of interest in urban scenarios. We use Gaussian mixture models to represent prior information about a wide variety of target objects while also admitting (1) gradient-based optimization of the compression kernels and (2) injection of prior knowledge of the urban scenario. The models are trained using finite-difference time-domain (FDTD)-generated target signatures. Moreover, interfering objects such as walls between the radar and target are also incorporated into the optimization. Simulated performance of optimized kernels is compared with the performance of random-based compression and with Nyquist sampling of reduced-bandwidth waveforms. Targets of interest in many traditional radar applications are large vehicles such as trucks, tanks, and aircraft. These vehicles are several meters across, even at their narrowest points, such that waveform bandwidths on the order of tens of MHz are sufficient for resolving multiple range bins on the target. Objects encountered in urban applications, however, include relatively small objects such as chairs, tables, and handheld objects and weapons. These items are often less than 1 m in their longest dimension and can be as small as a few centimeters across their narrowest dimensions. To obtainmultiple range resolution cells on such targets, waveform bandwidths must be on the order of GHz. For example, consider a rifle that is approximately 1 m in length and a few centimeters wide. When viewed from the side, the radar system must have at least a few GHz of bandwidth before being able to resolve multiple range bins on the target. When viewed from an angle of 30? relative to the side, approximately 500 MHz of bandwidth is required in order for the radar to resolve more than one range bin on the rifle.