Abstract
A compressive sensing joint sparse representation direction of arrival estimation (CSJSR-DoA) approach is proposed for wireless sensor array networks (WSAN). By exploiting the joint spatial and spectral correlations of acoustic sensor array data, the CSJSR-DoA approach provides reliable DoA estimation using randomly-sampled acoustic sensor data. Since random sampling is performed at remote sensor arrays, less data need to be transmitted over lossy wireless channels to the fusion center (FC), and the expensive source coding operation at sensor nodes can be avoided. To investigate the spatial sparsity, an upper bound of the coherence of incoming sensor signals is derived assuming a linear sensor array configuration. This bound provides a theoretical constraint on the angular separation of acoustic sources to ensure the spatial sparsity of the received acoustic sensor array signals. The Cramr–Rao bound of the CSJSR-DoA estimator that quantifies the theoretical DoA estimation performance is also derived. The potential performance of the CSJSR-DoA approach is validated using both simulations and field experiments on a prototype WSAN platform. Compared to existing compressive sensing-based DoA estimation methods, the CSJSR-DoA approach shows significant performance improvement.
Highlights
Direction of arrival (DoA) estimation using acoustic sensor arrays has attracted significant interest due to its wide applications [1,2,3,4]
We propose a novel DoA estimation approach that accomplishes low power, robust DoA estimation over a wireless sensor array networks (WSAN) platform, which reduces the volume of transmitted data without complicated local data compression operations
With channel data packet loss taken into account, we instead define rdc (≤ 1) to be the ratio of the number of acoustic samples successfully received at the fusion center versus the total number of samples acquired by the H acoustic sensors
Summary
Direction of arrival (DoA) estimation using acoustic sensor arrays has attracted significant interest due to its wide applications [1,2,3,4]. Incorporating a CS-based formulation, this approach is able to directly estimate the incident angles of acoustic sources from randomly-sampled acoustic signals This is made possible by fully exploiting the joint spatial and spectral sparse structure of the acoustic signals acquired by the sensor array. Some similar work [21] uses the angle domain sparsity of sources and formulates the narrow band signal of an antenna array within the BCS [22,23] framework It is utilized as an alternative DoA estimation approach, and no data compression is considered. DoA estimation approaches is that the CSJSR-DoA approach exploits both the spatial and spectral domain structure of the acoustic sensor array signals.
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