Signal processing techniques for modern radar systems
Date
2019-08-07
Authors
Elhoshy, Mostafa Kamal Kamel
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Abstract
This dissertation considers radar detection and tracking of weak fluctuating targets
using dynamic programming (DP) based track-before-detect (TBD). TBD combines target
detection and tracking by integrating data over consecutive scans before making a decision
on the presence of a target. A novel algorithm is proposed which employs order statistics in
dynamic programming based TBD (OS-DP-TBD) to detect weak fluctuating targets. The
well-known Swerling type 0, 1 and 3 targets are considered with non-Gaussian distributed
clutter and complex Gaussian noise. The clutter is modeled using the Weibull, K and
G0 distributions. The proposed algorithm is shown to provide better performance than
well-known techniques in the literature. In addition, a novel expanding window multiframe
(EW-TBD) technique is presented to improve the detection performance with reasonable
computational complexity compared to batch processing. It is shown that EW-TBD has
lower complexity than existing multiframe processing techniques. Simulation results are
presented which confirm the superiority of the proposed expanding window technique in
detecting targets even when they are not present in every scan in the window. Further, the
throughput of the proposed technique is higher than with batch processing. Depending
on the range and azimuth resolution of the radar system, the target may appear as a point
in some radar systems and there will be target energy spillover in other systems. This
dissertation considers both extended targets with different energy spillover levels and point
targets. Simulation results are presented which confirm the superiority of the proposed
algorithm in both cases.
Description
Keywords
Radar Signal Processing, Order Statistics Dynamic Programming Track-Before-Detect, Expanding Window (EW)-TBD Multiframe Technique, Non-Gaussian Clutter Distribution, Extended Targets