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Research report | Signal Separation Of Helicopter Radar Returns Using Wavelet-Based Sparse Signal Optimisation


A novel wavelet-based sparse signal representation technique is used to separate the main and tail rotor blade components of a helicopter from the composite radar returns. The received signal consists of returns from the rotating main and tail rotor blades, the helicopter body, possible land or sea clutter, and other residual components, which may all overlap in time and frequency; and therefore conventional time and frequency separation techniques cannot be applied. A sparse signal representation technique is now proposed for this problem with the tunable Q wavelet transform used as the dictionary. The proposed algorithm is demonstrated using both simulated and real radar data (X and Ku-band), and is capable of extracting the components of interest successfully.


Executive Summary

The radar return from a helicopter target in flight is a complex multi-component signal com- prising of returns from the main body, the main and tail rotor hubs and blades. Temporal and Doppler characteristics of these components are quite distinguishable providing a potential basis for automatic target recognition and classification. For example, the blade returns are highly non-stationary and have a broad Doppler spectrum in periodic short time intervals. On the other hand, the body return is relatively stationary with a narrow Doppler line. Separating composite radar return into individual components provides useful information for number of Defence applications. In this report, we propose robust algorithms for separating main rotor blade component, as well as that of the tail rotor blade, from the other components using state-of-the-art wavelet transforms and sparse signal representation techniques. Wavelet transforms have been used extensively to transform a signal into the time-scale do- main, simultaneously representing time and frequency information. Among the recently developed classes of discrete wavelets, the tunable Q wavelet transform (TQWT) offers great flexibility to represent the signal components of interest, and can also be efficiently implemented. In this work, the TQWT is used to represent the main and tail rotor blade returns, which can then be extracted by sparse signal optimisation. A variation of basis pursuit denoising (BPD), an l1 norm based sparse optimisation technique, is used to compute the TQWT coefficients. Two algorithms are presented in the report to separately extract main rotor blade returns and tail rotor blade returns from the composite signal. The algorithms are demonstrated on both simulated and real helicopter data. The experimental data at X-band (9:5 GHz) and Ku-band (16:8 GHz) were collected by the DST Wandana II radar of a Squirrel AS350BA helicopter at various aspect angles. The algorithms are shown to be capable of extracting the main rotor blade signal as well as the tail rotor blade signal almost completely, leaving only the hub and body components in the residual signal. Also, no artifacts in the extracted signals were observed despite the presence of the dominant body component and noise. Comparisons of the original and extracted signals in time and frequency domains show an excellent match. The proposed method is also tested in a typical sea clutter environment. Simulated sea clutter was added to X-band helicopter data at different signal-to-clutter ratios (SCR), and the performance of the methods is verified. Even at a total SCR of - 11:7 dB (the main blades SCR is - 25 dB), the main rotor blade returns were successfully extracted from other signal components and clutter. Future work is required to test the performance of the proposed methods when applied to a highly manoeuvring helicopter. Also, the performance of the algorithms at other operating frequencies such as L or S-band is still to be evaluated using real data. More work on automatic tuning of the TQWT filter is also warranted. The proposed methods are highly flexible, and can be adapted to analyse radar returns from other rotating blade structures such as wind turbines.

Key information


Si Tran Nguyen Nguyen, Sandun Kodituwakku, Rocco Melino  and Hai-Tan Tran

Publication number


Publication type

Research report

Publish Date

October 2016


Unclassified - public release


Helicopter radar returns; Signal separation; Wavelet transform; Sparse signal representation