Abstract
This paper introduces a new direction-of-arrival (DOA) estimation method for wideband signal sources. The new method estimates the DOA of wideband signal sources based on squared test of orthogonality of projected subspaces (Squared TOPS) which is an improved method of TOPS. TOPS and Squared TOPS use the signal and noise subspaces of multiple frequency components of wideband signal sources. Although coherent wideband method, such as coherent signal subspace method (CSSM), performs high DOA estimation accuracy, it requires the initial estimate of signal source directions. On the other hand, TOPS and Squared TOPS can provide good performance of DOA estimation without the initial value of signal sources; however, some false peaks appear in spatial spectrum based on these methods. The proposed method, called weighted Squared TOPS (WS-TOPS), uses the modified squared matrix and selective weighted averaging process to improve DOA estimation performance. The performance of WS-TOPS is compared with those of TOPS, Squared TOPS, incoherent MUSIC, and test of orthogonality of frequency spaces (TOFS) through computer simulations. The simulation results show that WS-TOPS can suppress all false peaks in spatial spectrum and improve DOA estimation accuracy and also keep the same resolution performance as Squared TOPS.
Highlights
Direction-of-arrival (DOA) estimation for wideband signals has been attracting much attention for decades because wideband signals are commonly used in real world for such as signal source localization in wireless communication and radar systems
6 Conclusions In this paper, we propose a new DOA estimation method for wideband signals called weighted Squared test of orthogonality of projected subspaces (TOPS) (WS-TOPS)-based on Squared TOPS
The simulation results show that WS-TOPS can suppress all false peaks in the spatial spectrum, while TOPS and Squared TOPS cannot
Summary
Direction-of-arrival (DOA) estimation for wideband signals has been attracting much attention for decades because wideband signals are commonly used in real world for such as signal source localization in wireless communication and radar systems. Applying KR subspace approach to CSSM algorithm, some DOA estimation methods of wideband signal sources were proposed [14]. The methods achieve higher DOA estimation accuracy and resolution performance than the conventional CSSM even if there are fewer sensors or antennas than the incoming signal sources. Sparse signal representation algorithms have been received much attention, which can provide new approaches for wideband DOA estimation [15,16,17] These DOA estimation methods based on the sparse signal representation perform higher resolution than the conventional methods without requiring the number of sources. Using modified squared matrix and selective weighted averaging process, WS-TOPS can suppress all false peaks in spatial spectrum and improve DOA estimation accuracy of wideband signal sources and keep the same resolution performance as Squared TOPS.
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