Abstract
To suppress alpha-stable noise and co-channel interference, this paper defines a novel cyclic correlation function, and proposes a new MUSIC algorithm based on sigmoid cyclic correlation function. Furthermore, the proposed algorithm (SCC-MUSIC) is applied to estimate direction of arrival (DOA) in alpha stable distribution noise. Simulation results demonstrate that the proposed SCC-MUSIC can get a better performance than several existing algorithms especially in the highly impulsive noise environments.
Highlights
Source localization by direction of arrival (DOA) has been received considerable attention in radar, sonar and wireless communication over the past years[1]
We find that the correlation matrix estimation used in subspace algorithms is replaced by the sigmoid cyclic correlation matrix estimation
S is independent of the noise N and signal subspaces is orthogonal with noise subspaces, spatial spectrum of Sigmoid based cyclic correlation (SCC)-MUSIC can be got based on classical
Summary
Source localization by direction of arrival (DOA) has been received considerable attention in radar, sonar and wireless communication over the past years[1]. The MUSIC algorithm[2], one of the most well-known subspace methodology, has attracted much attention and is an asymptotically unbiased DOA estimator based on the Gaussian noise assumption[3, 4]. Most man-made signals encountered in communication, telemetry, radar and sonar systems, some parameters do vary periodically with time. There are many kinds of noise and interference in wireless communication, telemetry, radar and sonar systems, and the received signal may be submerged. To suppress impulse noise and co-channel interference, this paper defines a novel cyclic correlation function and propose a new algorithm based sigmoid cyclic correlation to estimate DOA. Simulations verified the superiority of the proposed estimation method over existing methods in the presence of both alpha-stable distribution noise and cochannel interference
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