access icon free Efficient estimation of signal parameters via rotational invariance technique-based algorithm with automatic pairing for two-dimensional angle and polarisation estimation using crossed dipoles

© The Institution of Engineering and Technology
Received 26/01/2013, Accepted 09/08/2013, Revised 25/06/2013, Published 20/09/2013

This study addresses an efficient algorithm for joint estimation of the two-dimensional (2D) angles and polarisations of the signals impinging on a planar array of co-centred crossed dipole pairs. Based on a novel multi-layered signal decomposition scheme, the proposed algorithm estimates 2D angles and polarisations alternatively in a coarse-to-fine manner by progressively decomposing the signals into finer groups using an angle- or polarisation-based beamforming process. Such a decomposition not only partitions the signals with close parameters into separate groups, but it also reduces the power of the noise, both of which can in turn enhance the estimation accuracy. Moreover, the data are properly stacked in each stage by exploiting the intrinsic data structures so that only the 1D estimation of signal parameters via rotational invariance techniques algorithms are required, thus entailing lower computational load. Conducted simulations show that the performance of the proposed algorithm is close to that of previous works, but its computational complexity is substantially lower and is free of pairing of the estimated parameters with the multi-layered parameter estimation scheme.

Inspec keywords: estimation theory; array signal processing

Other keywords: polarisation-based beamforming process; co-centred crossed dipole pairs; multi-layered parameter estimation scheme; joint estimation; novel multi-layered signal decomposition scheme; computational complexity; polarisation estimation; signal parameters; two-dimensional angle estimation; automatic pairing; rotational invariance technique-based algorithm; angle-based beamforming process; intrinsic data structures

Subjects: Signal processing and detection; Other topics in statistics; Signal processing theory; Other topics in statistics

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