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Multi-source DOA tracking with an adaptive superposition model for sparse array
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AI
DOI：10.1016/j.sigpro.2024.109877.png)
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Tracking the direction of arrival (DOA) with a passive sensor array is a well-known problem in signal processing. Traditional methods, such as subspace tracking and super-resolution techniques, struggle with data association and trajectory crossing. Although Kalman filter-based methods are excellent for tracking, they are difficult to apply to DOA tracking due to complex nonlinear transformations and unknown source signals in the array-received signals. To overcome these limitations, we propose two adaptive tracking methods using a sparse array (SA): one based on the extended Kalman filter (SA-AEKF) and the other on the unscented Kalman filter (SA-AUKF). Tracking is implemented using an adaptive superposition model that adapts to changes in signal and noise intensity. Moreover, it avoids the presence of multiple measurements at the same tracking time by superimposing multiple snapshot measurements into a single measurement. Finally, we analyze the Cram & eacute;r- Rao bound (CRB) and posterior Cram & eacute;r-Rao bound (PCRB) for the proposed algorithms. Simulation results show that our methods outperform conventional techniques in challenging scenarios with low signal-to-noise ratios, limited snapshots, trajectory crossovers, and fewer array elements than signal sources.
Keyword:
DOA tracking
Multi-source tracking
Sparse array
Extended Kalman filter
Unscented Kalman filter
Adaptive tracking
Superposition model
CRB
PCRB
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
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