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

Published

Author(s)

Lorant A. Muth, C. M. Wang, Timothy Conn

Abstract

Coherent radar cross section measurements 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 (I0,Q0). The presence of outliers (mostly due to rf 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 (1) to eliminate or to reduce the influence of outliers, and then (2) to separate the target and background signals. Concurrently, the influence of noise is also reduced. Thus, we can obtain both a target-independent estimate of the background and a background-free estimate of the radar cross section of calibration artifacts. In measurements of low-observable targets, the subtraction of the background signal from the measurement and calibration significantly improves the measurement accuracy. This technique is especially useful at sub-wavelength translations at VHF and UHF, where spectral techniques are not applicable since only a limited arc of data is available.
Citation
IEEE Transactions on Instrumentation and Measurement
Volume
54
Issue
6

Keywords

background, clutter, coherent RCS measurements, least-median squares, orthogonal-distance regression, outliers, radar cross section, RCS calibration, RCS uncertainty

Citation

Muth, L. , Wang, C. and Conn, T. (2005), Robust Separation of Background and Target Signals in Radar Cross Section Measurments, IEEE Transactions on Instrumentation and Measurement (Accessed April 13, 2024)
Created November 30, 2005, Updated October 12, 2021