Abstract
Accurate information acquisition is of vital importance for wireless sensor array network (WSAN) direction of arrival (DOA) estimation. However, due to the lossy nature of low-power wireless links, data loss, especially block data loss induced by adopting a large packet size, has a catastrophic effect on DOA estimation performance in WSAN. In this paper, we propose a double-layer compressive sensing (CS) framework to eliminate the hazards of block data loss, to achieve high accuracy and efficient DOA estimation. In addition to modeling the random packet loss during transmission as a passive CS process, an active CS procedure is introduced at each array sensor to further enhance the robustness of transmission. Furthermore, to avoid the error propagation from signal recovery to DOA estimation in conventional methods, we propose a direct DOA estimation technique under the double-layer CS framework. Leveraging a joint frequency and spatial domain sparse representation of the sensor array data, the fusion center (FC) can directly obtain the DOA estimation results according to the received data packets, skipping the phase of signal recovery. Extensive simulations demonstrate that the double-layer CS framework can eliminate the adverse effects induced by block data loss and yield a superior DOA estimation performance in WSAN.
Highlights
As a branch of array signal processing, direction of arrival (DOA) estimation has been a hot topic in many research fields, such as smart antennas, mobile communication and target tracking [1,2,3,4,5]
We consider a wireless sensor array networks (WSAN) consisting of a six-element uniform linear array (ULA) and one fusion center (FC), where the inter-element spacing in the ULA is set 0.2 m, which is smaller than half of the minimum wavelength of the impinging signals to avoid angle ambiguity effects
We investigate the adverse effects of block data loss on DOA estimation in WSAN
Summary
As a branch of array signal processing, DOA estimation has been a hot topic in many research fields, such as smart antennas, mobile communication and target tracking [1,2,3,4,5]. Utilizing CS reconstruction techniques rather than traditional interpolation methods for lost data recovery, they achieved an enhanced channel utilization and transmission reliability All of these works demonstrated their results in terms of the accuracy of signal recovery, without insight into the level of information acquisition, e.g., the DOA information in WSAN. Leveraging a joint frequency and spatial domain sparse representation of the sensor array data, the FC can directly obtain the DOA estimation results according to the same received data samples, skipping the stage of signal recovery. Extensive simulations are carried out to validate that the double-layer CS framework can dispel the detriment of block data loss and yield a superior DOA estimation performance in WSAN. A double-layer compressive sensing framework is proposed to eliminate the adverse effects of block data loss on DOA estimation in WSAN.
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