This paper extends the Non-Circular MUltiple SIgnal Classification(MUSIC)(NC-MUSIC) method for the common array geometries including Uniform Circular Arrays(UCAs) and Uniform Rectangular Arrays(URAs),which enables the...This paper extends the Non-Circular MUltiple SIgnal Classification(MUSIC)(NC-MUSIC) method for the common array geometries including Uniform Circular Arrays(UCAs) and Uniform Rectangular Arrays(URAs),which enables the algorithm to estimate 2-D Direction Of Arrival(DOA).A comparison between UCAs and URAs of NC-MUSIC is made in this paper.The simulations show that the NC-MUSIC method doubles the maximum estimation number of standard MUSIC.Using non-circular signals,the performance of URAs is improved remarkably while the improvement of UCAs is not so significantly.Moreover,the influence of arrays structures on the NC-MUSIC method is discussed.展开更多
In this paper,an acoustic velocityindependent two-dimensional direction of arrival(2-D DOA)estimation for underwater application is presented to eliminate the effect of the inaccurate acoustic velocity estimation.Acco...In this paper,an acoustic velocityindependent two-dimensional direction of arrival(2-D DOA)estimation for underwater application is presented to eliminate the effect of the inaccurate acoustic velocity estimation.According to the geometric relationship between the linear arrays,the proposed method employs the cross correlation matrix(CCM)of the data received by three crossed linear arrays to remove the acoustic velocity factor.The simulation results demonstrate that the proposed method is not susceptible to the acoustic velocity.For a single source,the proposed method outperforms the conventional method in all conditions.For multiple sources,there is a little performance degradation for the proposed method compared with the conventional method.However,the proposed method displays a better performance than the conventional method in situations where the signal to noise ratio(SNR)is extremely low or the acoustic velocity estimation error is non-negligible.Furthermore,the computational complexity of the proposed method is lower than that of the conventional method using the same amount of sensors in total,while the performance is still acceptable.展开更多
文摘This paper extends the Non-Circular MUltiple SIgnal Classification(MUSIC)(NC-MUSIC) method for the common array geometries including Uniform Circular Arrays(UCAs) and Uniform Rectangular Arrays(URAs),which enables the algorithm to estimate 2-D Direction Of Arrival(DOA).A comparison between UCAs and URAs of NC-MUSIC is made in this paper.The simulations show that the NC-MUSIC method doubles the maximum estimation number of standard MUSIC.Using non-circular signals,the performance of URAs is improved remarkably while the improvement of UCAs is not so significantly.Moreover,the influence of arrays structures on the NC-MUSIC method is discussed.
基金This work was supported by the National Natural Science Foundation of China under grants of 61871191,62171187,and 62192711the Guangdong Provincial Key Laboratory of Short-Range Wireless Detection and Communication under grant 2017B030314003the Science and Technology Planning Project of Guangzhou under grant 201804010209。
文摘In this paper,an acoustic velocityindependent two-dimensional direction of arrival(2-D DOA)estimation for underwater application is presented to eliminate the effect of the inaccurate acoustic velocity estimation.According to the geometric relationship between the linear arrays,the proposed method employs the cross correlation matrix(CCM)of the data received by three crossed linear arrays to remove the acoustic velocity factor.The simulation results demonstrate that the proposed method is not susceptible to the acoustic velocity.For a single source,the proposed method outperforms the conventional method in all conditions.For multiple sources,there is a little performance degradation for the proposed method compared with the conventional method.However,the proposed method displays a better performance than the conventional method in situations where the signal to noise ratio(SNR)is extremely low or the acoustic velocity estimation error is non-negligible.Furthermore,the computational complexity of the proposed method is lower than that of the conventional method using the same amount of sensors in total,while the performance is still acceptable.
