A New Approach to Achieve a Trade-off Between Direction-of-Arrival Estimation Performance and Computational Complexity

2020 ◽  
pp. 1-1
Author(s):  
Veerendra Dakulagi
2020 ◽  
Vol 14 ◽  
pp. 174830262094337
Author(s):  
Geng Chen ◽  
Bo Tian ◽  
Jian Gong ◽  
Cunqian Feng

A method based on sparse array is applied to direction-of-arrival estimation of passive radar in this paper to increase the number of resolvable sources and improve the direction-of-arrival estimation performance for coprime array. The virtual symmetric non-uniform linear array of coprime array based on passive radar signal model is introduced. Considering the impact of direct wave, extensive cancellation algorithm is used to cancel the direct wave, with the conventional MUSIC with spatial smoothing algorithm and virtual aperture filling applied on the sparse array of passive radar; the resolution of target is low in the low signal-noise-ratio. To effectively improve the estimation of the target under the low signal-noise-ratio, a noise subspace reconstruction method is proposed. The proposed direction-of-arrival estimation method can improve the direction-of-arrival estimation performance of passive radar. The simulations are provided to demonstrate the effectiveness of the proposed method.


Author(s):  
David Díaz-Guerra Aparicio ◽  
José Ramón Beltrán Blázquez

The Steered Response Power with phase transform (SRP-PHAT) is one of the most employed techniques for Direction of Arrival (DOA) estimation with microphone arrays, but its computational complexity grows when the search space increases. To solve this issue, we propose the use of Neural Networks (NN) to obtain the DOA from low-resolution SRP-PHAT power maps.


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