Robust Separation of Background and Target Signals in Radar Cross Section Measurements

摘要

Coherent measurements of radar cross-section on a target moving along the system line-of-sight in free space will trace a circle centered on the origin of the complex (I, Q) plane. The presence of additional complex background signals (including stationary clutter, target support, and averaged target-mount interactions), which do not depend on target position, will translate the origin of the circle to some complex point (I_(0), Q_(0)). The presence of outliers (mostly due to radio-frequency interference) can introduce significant errors in the determination of the radius and center of the I-Q circle. We have implemented a combination of a robust and efficient least median square and an orthogonal-distance regression algorithm to eliminate or to reduce the influence of outliers, and then to separate the target and background signals. Concurrently, the influence of noise is also reduced. Thus, we can obtain both target-independent estimates of the background and a background-free estimates of the radar cross-sections of calibration artifacts. In measurements on low-observable targets, the subtraction of the background signal from the measurement and calibration significantly improves the measurement accuracy. This technique is especially useful for subwavelength translations at very and ultra-high-frequencies, where spectral techniques are not applicable because the available arc of data is limited.