A Sliding Window Data Compression Method for Spatial-Time DOA Estimation
This paper presents a sliding window data compression method for spatial-time direction-of-arrival (DOA) estimation using coprime array. The signal model is firstly formulated by jointly using the temporal and spatial information of the impinging sources. Then, a sliding window data compression proc...
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| Auteurs principaux: | , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Hindawi Limited
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/64f91c8c873242528ea4e1ad34d9a735 |
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| Résumé: | This paper presents a sliding window data compression method for spatial-time direction-of-arrival (DOA) estimation using coprime array. The signal model is firstly formulated by jointly using the temporal and spatial information of the impinging sources. Then, a sliding window data compression processing is performed on the array output matrix to realize fast calculation of time average function, and the computational burden has been reduced accordingly. Based on the concept of sum and difference co-array (SDCA), the vectorized conjugate augmented MUSIC is adopted, with which more sources than twice of the physical sensors can be resolved. Additionally, the sparse array robustness to sensor failure has been evaluated by introducing the concept of essential sensors. The theoretical analysis and numerical simulations are provided to confirm the effectiveness performance of the proposed method. |
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