ArticleSensors (Basel, Switzerland)2015
Real-Valued Covariance Vector Sparsity-Inducing DOA Estimation for Monostatic MIMO Radar.
Article in Sensors (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Reweighted Off-Grid Sparse Spectrum Fitting for DOA Estimation in Sensor Array with Unknown Mutual Coupling.Sensors (Basel, Switzerland) · 2023Article
- Partial Angular Sparse Representation Based DOA Estimation Using Sparse Separate Nested Acoustic Vector Sensor Array.Sensors (Basel, Switzerland) · 2018Article
- Direction-Of-Arrival Estimation and Tracking Based on a Sequential Implementation of C-SPICE with an Off-Grid Model.Sensors (Basel, Switzerland) · 2017Article
- Joint Smoothed l₀-Norm DOA Estimation Algorithm for Multiple Measurement Vectors in MIMO Radar.Sensors (Basel, Switzerland) · 2017Article
- Direction of Arrival Estimation for MIMO Radar via Unitary Nuclear Norm Minimization.Sensors (Basel, Switzerland) · 2017Article
- DOA Estimation Based on Real-Valued Cross Correlation Matrix of Coprime Arrays.Sensors (Basel, Switzerland) · 2017Article
- DOA and Polarization Estimation Using an Electromagnetic Vector Sensor Uniform Circular Array Based on the ESPRIT Algorithm.Sensors (Basel, Switzerland) · 2016Article
- Sparsity-Aware DOA Estimation Scheme for Noncircular Source in MIMO Radar.Sensors (Basel, Switzerland) · 2016Article
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Authors and funding
4 authors.
Funding
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Abstract
In this paper, a real-valued covariance vector sparsity-inducing method for direction of arrival (DOA) estimation is proposed in monostatic multiple-input multiple-output (MIMO) radar. Exploiting the special configuration of monostatic MIMO radar, low-dimensional real-valued received data can be obtained by using the reduced-dimensional transformation and unitary transformation technique. Then, based on the Khatri-Rao product, a real-valued sparse representation framework of the covariance vector is formulated to estimate DOA. Compared to the existing sparsity-inducing DOA estimation methods, the proposed method provides better angle estimation performance and lower computational complexity. Simulation results verify the effectiveness and advantage of the proposed method.
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