ArticleSensors (Basel, Switzerland)2016
The Real-Valued Sparse Direction of Arrival (DOA) Estimation Based on the Khatri-Rao Product.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Direction of Arrival Estimation in Elliptical Models via Sparse Penalized Likelihood Approach.Sensors (Basel, Switzerland) · 2019Article
- Partial Angular Sparse Representation Based DOA Estimation Using Sparse Separate Nested Acoustic Vector Sensor Array.Sensors (Basel, Switzerland) · 2018Article
- Real-Valued 2D MUSIC Algorithm Based on Modified Forward/Backward Averaging Using an Arbitrary Centrosymmetric Polarization Sensitive Array.Sensors (Basel, Switzerland) · 2017Article
- An Improved DOA Estimation Approach Using Coarray Interpolation and Matrix Denoising.Sensors (Basel, Switzerland) · 2017Article
- DOA Estimation Based on Real-Valued Cross Correlation Matrix of Coprime Arrays.Sensors (Basel, Switzerland) · 2017Article
- A Low-Complexity ESPRIT-Based DOA Estimation Method for Co-Prime Linear Arrays.Sensors (Basel, Switzerland) · 2016Article
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Authors and funding
3 authors.
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Abstract
There is a problem that complex operation which leads to a heavy calculation burden is required when the direction of arrival (DOA) of a sparse signal is estimated by using the array covariance matrix. The solution of the multiple measurement vectors (MMV) model is difficult. In this paper, a real-valued sparse DOA estimation algorithm based on the Khatri-Rao (KR) product called the L₁-RVSKR is proposed. The proposed algorithm is based on the sparse representation of the array covariance matrix. The array covariance matrix is transformed to a real-valued matrix via a unitary transformation so that a real-valued sparse model is achieved. The real-valued sparse model is vectorized for transforming to a single measurement vector (SMV) model, and a new virtual overcomplete dictionary is constructed according to the KR product's property. Finally, the sparse DOA estimation is solved by utilizing the idea of a sparse representation of array covariance vectors (SRACV). The simulation results demonstrate the superior performance and the low computational complexity of the proposed algorithm.
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