Alphonso A. Samuel

Time Reversal Synthetic Aperture Radar Imaging In Multipath

Conventional spotlight synthetic aperture radar (SAR) assumes a single reflection of transmitted waveforms from targets [1]. Multiple reflections of targets due to surrounding scatterers appear as ghosting artifacts in conventional SAR images, which obscures true target image and leads to poor resolution. In this paper, we develop image formation techniques using time reversal, time reversal SAR (TR-SAR), to remove ghosting artifacts and achieve high resolution. The TR-SAR algorithm is tested using phase history data collected by a rail-mounted SAR sensor operated by Raytheon.

A combined particle/Kalman filter for improved tracking of beam aspect targets

Track continuity is difficult to maintain when tracking beam aspect targets. The loss of Doppler discrimination allows clutter to mask the target return, making it nearly impossible to detect. In order to improve tracking performance, a combination particle/Kalman filter has been developed. The tracking filters obviate each other as appropriate. When a target enters a Doppler blind zone, the particle filter replaces the Kalman filter as the tracking algorithm until the target exits the zone. The particle filter expands over the uncertainty region so that when the target is once again visible, it can immediately resume track via re-initialization of the Kalman filter. This paper discusses the design and simulation of this algorithm and shows the resulting improvement in track continuity. We briefly discuss how our combined particle/Kalman filter approach can be used to address the problem of targets obscured in altitude return.

On the use of space-time adaptive processing and time-frequency data representations for detection of near-stationary targets in monostatic clutter

The detection of near-stationary targets in mainlobe clutter is a problem that has recently generated a great deal of interest within the Department of Defense community. Some examples of these types of targets are surface vehicles, missile launchers and loitering (micro-) unmanned aerial vehicles (UAV). The root of the difficulty lies in the fact that conventional radar processing loses the ability to use the Doppler of the target to discriminate it from the clutter. Indeed, the target need not even be nearly stationary for this to be a problem - even a rapidly moving target can exhibit low Doppler if its velocity vector is nearly perpendicular to the velocity vector of the observation platform. Raytheon Systems Company (Raytheon) has been investigating a number of advanced algorithmic solutions to this problem within the context of providing a dual-mission capability to currently fielded RF missile systems. This paper describes a processing architecture that combines preprocessing, time-frequency transforms and best bases algorithms and discusses some preliminary results.

Polarization coherency through various scattering mechanisms

A backscattered signal is coherently or incoherently polarized depending on the nature of the scattering surface and the bandwidth of the incident signal. In various applications, and more realistic scenarios, multi spectra or Ultra Wide Band (UWB) have certain advantages over narrow band signals especially in target detection or resolution. Under these circumstances, we investigate the depolarizing effects of wide band signals to understand the relationship of coherency or incoherency with various scattering mechanisms such as reflection/transmission or diffraction. In other words, these major scattering mechanisms may depolarize a signal incoherently in one instance while coherently in another. In this paper we present results showing that the coherency or incoherency of a signal is highly dependent on the nature of the scatterer in relationship to the bandwidth of the incident signal. We use the Finite Difference Time Domain (FDTD) methodology to analyze signals scattering off various homogenous or inhomogeneous surfaces.

Model order identification and parameters estimation using array processings for small sample support

We propose a sequential algorithm for determining the number of narrow band source signals, and estimation of the parameters associated with the signal (angles and powers) and of the receiver noise power using the noisy sample observed at the receiver sensor arrays. Previous parameter estimates (or initial estimates) are refined multiple times in our sequential approach and also the Newton-based refinement gives continuous-valued estimates so the estimation performance is not limited to the grid resolution. By benchmarking against the Cramer Rao Lower Bound (CRLB), the estimation performance for all parameters of the proposed algorithm achieves near optimal performance even in the low SNR and small sample support region, in which, the sample size can be smaller than the number of sensors in the array. At the same time, the detection (or model order identification) performance outperforms other relevant algorithms.