A Sliding Window Data Compression Method for Spatial-Time DOA Estimation

This paper presents a sliding window data compression method for spatial-time direction-of-arrival (DOA) estimation using coprime array. The signal model is firstly formulated by jointly using the temporal and spatial information of the impinging sources. Then, a sliding window data compression processing is performed on the array output matrix to realize fast calculation of time average function, and the computational burden has been reduced accordingly. Based on the concept of sum and difference co-array (SDCA), the vectorized conjugate augmented MUSIC is adopted, with which more sources than twice of the physical sensors can be resolved. Additionally, the sparse array robustness to sensor failure has been evaluated by introducing the concept of essential sensors. The theoretical analysis and numerical simulations are provided to confirm the effectiveness performance of the proposed method.

Non-circular signal DOA estimation based on coprime array MIMO radar

Aiming at the problems of low degree of freedom, small array aperture, and phase ambiguity in traditional coprime array direction-of-arrival estimation methods, a non-circular signal DOA estimation method based on expanded coprime array MIMO radar is proposed. Firstly, this method combines the coprime array and the MIMO radar to form transmitter and receiver array. Secondly, the array is expanded using the non-circular signal characteristics to reconstruct the received signal matrix. Then the dimensionality reduction is performed. The two-dimensional spectral peak search is converted into an optimization problem, and the optimization of the two-dimensional MUSIC algorithm is reconstructed using constraints, and a cost function is constructed to solve the problem. In addition, use the power series of the noise eigenvalues to correct the noise subspace to further improve the accuracy of the algorithm. Finally, the problem of no phase ambiguity in the method in this article is derived. Simulation experiments show that the method in this article can effectively avoid phase ambiguity, greatly improve the degree of freedom, and expand the array aperture. Compared with the traditional MUSIC algorithm and the mutual prime array MUSIC algorithm, it has better resolution and DOA estimation accuracy.

DOA estimation method of spatial spectrum product for distributed coprime linear array

Coprime array isAsparse array composed of two uniform linear arrays with different spacing. When the two subarrays are inAnon-coherent distributed configuration, the direction of arrival (DOA) method based on the covariance analysis of the complete coprime array is no longer effective. According to the essential attribute that the distance between the elements of two subarrays can eliminate the angle ambiguity, based on the mathematical derivation, Aspatial spectral product DOA estimation method for incoherent distributed coprime arrays is proposed. Firstly, the spatial spectrum of each subarray is calculated by using the snapshot data of each subarray, and then the DOA estimation is realized by multiplying the spatial spectrum of each subarray. The simulation results show that the estimation accuracy and angle resolution of the present method are better than those of the traditional ambiguity resolution methods, and the estimation performance is good in the mutual coupling and low SNR environment, with the good adaptability and stability. Moreover, by using the flexibility of distributed array, the matching error is effectively solved through the rotation angle.

2-D DOA estimation method based on coprime array MIMO radar

Aiming at the problem that traditional direction of arrival (DOA) estimation methods cannot handle multiple sources with high accuracy while increasing the degrees of freedom (DOF), a new method for 2-D DOA estimation based on coprime array MIMO radar (SA-MIMO-CA) is proposed. First of all, in order to ensure the accuracy of multi-source estimation when the number of elements is finite, a new coprime array model based on MIMO (MIMO-CA) is proposed. This method is based on a new MIMO array-based co-prime array model (MIMO-CA), which improves the accuracy of multi-source estimation when the number of array elements is limited, and obtains a larger array aperture with a smaller number of array elements, and improves the estimation accuracy of 2-D DOA. Finally, the effectiveness and reliability of the proposed SM-MIMO-CA method in improving the DOF of array and DOA accuracy are verified by experiments.

COPRIME ARRAY PARAMETERS OPTIMIZATION FOR DOA ESTIMATION

Sparse array such as the coprime array is one of the most preferable sparse arrays for direction of arrival estimation due to its properties, like simplicity, capability of resolving more sources than the number of elements and resistance to mutual coupling issue. In this paper, a new coprime array model is proposed to increase the number of degree of freedom (DOF) and improve the performance of coprime array. The new designed array can avoid the mutual coupling by minimizing the lag redundancy and expand the central lags in the virtual difference co-array. Thus, the propose structure can resolve more sources than the prototype coprime array using the same number of elements with improved direction of arrival estimation. Simulation results demonstrate that the proposed array model is more efficient than the others coprime array model.