Abstract
In this paper, we propose a computationally efficient direction-of-arrival (DoA) tracking scheme called the direct signal space construction Luenberger tracker (DSPCLT) with quadratic least square (QLS) regression. Also, we study analytically how the choice of observer gain affects the algorithm performance and illustrate how we can use the theoretical results in determining optimal observer gain value. The proposed scheme (DSPCLT) has several distinct features compared with existing algorithms. First, it requires only a fraction of computational complexity compared with other schemes. Secondly, it maintains robustness by treating separately the special case of object overlap in which subspace-based algorithms often suffer from lack of resolvability. Thirdly, the proposed scheme achieves enhanced performance by a method of delay compensation, which accounts for observation delay. Through numerical analysis, we show that DSPCLT achieves performance similar or superior to existing algorithms with only a fraction of computational requirement.
Highlights
We propose a computationally efficient direction-of-arrival (DoA) tracking scheme called the direct signal space construction Luenberger tracker (DSPCLT) with quadratic least square (QLS) regression
We proposed the direct signal space construction method Luenberger tracker with quadratic least square regression for computationally efficient DoA tracking
We note that the proposed algorithm combines the direct signal space construction method and Luenberger observer to achieve computational complexity significantly lower than other schemes
Summary
We propose a computationally efficient direction-of-arrival (DoA) tracking scheme called the direct signal space construction Luenberger tracker (DSPCLT) with quadratic least square (QLS) regression. Early subspacebased algorithms generally suffer from the issue of large computational complexity, typically due to the need for eigenvalue decomposition (ED) or singular-value decomposition (SVD) For this reason, various schemes have been proposed to reduce the system complexity, such as the propagator method (PM) [10], orthogonal propagator method (OPM) [11], subspace method without eigendecomposition (SWEDE) [12], direct signal space construction method (DSPCM) [13,14,15], and projection approximation subspace tracking-based deflation (PASTd) algorithm [16]. E proposed scheme has several distinct features compared with existing schemes It achieves very low complexity by employing DSPCM [14, 15] for subspace construction and Luenberger observer in place of the Kalman filter for estimation and filtering. Bold-faced letters are used for matrices and vectors. e complex conjugation, the transposition, and the Hermitian transposition of matrix A are denoted, respectively, by A∗, AT, and AH. e M × N zero matrix and N × N identity matrix are denoted, respectively, by OM×N and IN
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