In order to improve the performance of two-dimensional (2-D) direction-of-arrival (DOA) estimation, an L-shaped sparse array structured by two new sparse linear arrays is proposed. Each part of the proposed L-shaped array is used for one-dimensional (1-D) azimuth and elevation estimation, respectively. The new sparse linear array configuration is consisted of two subarrays which have N and M physical sensors, respectively. Owing to the advantages of the proposed sparse linear array configuration, higher degrees-of-freedom (DOF) and larger array aperture can be achieved when the second-order statistics of the received data is used. To match the azimuth and elevation angles automatically, the cross-covariance matrix of the two parts of the proposed L-shaped array is used to estimate the paired DOA angles. Based on the proposed L-shaped array configuration, (M + 1)(N − M/2) signal directions can be estimated with M + N sensors in each part and totally 2M + 2N − 1 sensors. Through the comparative analysis of the parameters with other sparse planar arrays, the proposed L-shaped array can achieve better performance due to its higher DOF and larger array aperture. Finally, numerical simulation results verify the superiority of the 2-D DOA method based on the proposed L-shaped array.

A rigorous mathematical analysis of cross-eye jamming in a radar system scenario and an expression for the induced angular error due to the cross-eye jammer are presented. The simulation results show that there is a Doppler difference between jamming and target return. The Doppler difference increases with the decrease of the distance between the monopulse radar and the platform protected by cross-eye jammer. When the power of target return is not small enough with respect to the power of jamming transmitted by one of the two sources, maybe the sign of the indicated angle is uncontrollable. The simulation results also show that although the cross-eye gain is maximized if two jamming signals are equal amplitude and antiphase, it is not a reasonable choice.

In order to reduce the mutual coupling ratio of sparse linear array, a new linear array structure with two sparse uniform linear arrays interleaved nested, is proposed in this paper. The new sparse linear array structure is consisted of two subarrays which have N and M physical sensors, respectively. By setting appropriate interelement spacing and interleaving, the degrees-of-freedom, uniform degrees-of-freedom and array aperture of the proposed sparse array can reach 2NM + 2N , 2(N + 2M) + 1 and max{(N − 1)R 1 , (M − 1)R 2 + d }, respectively. On one hand, the proposed sparse array has closed-form expressions for the sensor locations. On the other hand, through comparative analysis, although the proposed array structure has lower uniform degrees-of-freedom, it can greatly increase the degreesof-freedom, extend the array aperture, and reduce the mutual coupling ratio between physical sensors, which means better estimation performance of direction-of-arrival estimation can be achieved. Finally, the direction-of-arrival estimation performance of the proposed array structure is verified by numerical simulations. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Given the problem of the direction of arrival (DOA) estimation of quasi-stationary signals, a sparse reconstruction algorithm based on the sparse array is proposed in this study to improve the DOA estimation performance. Specifically, the quasi-stationary signal is modelled based on the interleaved array (IA), where the algorithm makes full use of the high degree of freedom (DOF) and large array aperture of the interleaved array in the virtual domain. Then, the angle parameters are estimated depending on sparse reconstruction and the Khatri-Rao transform, which can achieve a higher number of DOF. Compared with the angle measurement algorithm based on uniform linear array and nested linear array, the algorithm proposed in this study not only increases the DOF of angle measurement significantly, but also improves the accuracy of angle measurement. Finally, numerical simulations demonstrate the validity of the proposed method.

No abstract