Abstract
Frequency diverse array (FDA) radar has attracted much attention due to the angle and range dependence of the beam pattern. Multiple-input-multiple-output (MIMO) radar has high degrees of freedom (DOF) and spatial resolution. The FDA-MIMO radar, a hybrid of FDA and MIMO radar, can be used for target parameter estimation. This paper investigates a tensor-based reduced-dimension multiple signal classification (MUSIC) method, which is used for target parameter estimation in the FDA-MIMO radar. The existing subspace methods deteriorate quickly in performance with small samples and a low signal-to-noise ratio (SNR). To deal with the deterioration difficulty, the sparse estimation method is then proposed. However, the sparse algorithm has high computation complexity and poor stability, making it difficult to apply in practice. Therefore, we use tensor to capture the multi-dimensional structure of the received signal, which can optimize the effectiveness and stability of parameter estimation, reduce computation complexity and overcome performance degradation in small samples or low SNR simultaneously. In our work, we first obtain the tensor-based subspace by the high-order-singular value decomposition (HOSVD) and establish a two-dimensional spectrum function. Then the Lagrange multiplier method is applied to realize a one-dimensional spectrum function, estimate the direction of arrival (DOA) and reduce computation complexity. The transmitting steering vector is obtained by the partial derivative of the Lagrange function, and automatic pairing of target parameters is then realized. Finally, the range can be obtained by using the least square method to process the phase of transmitting steering vector. Method analysis and simulation results prove the superiority and reliability of the proposed method.
Highlights
MIMO radar has received widespread attention in the field of target parameter estimation, which has great development potential [1,2,3,4,5]
With constantly developing and progressing in the field of wireless communication, MIMO radar has gained popularity in the fields of national defense, navigation, remote sensing, and unmanned driving [6,7,8,9]. It has been confirmed in practical applications that MIMO radar transmits orthogonal waveforms to form virtual array elements to enlarge the array aperture, which has a higher degree of freedom and spatial resolution [10,11,12]
We introduce the computation complexity to reflect the efficiency of the proposed method, which can be expressed as follows: (1) The high-order-singular value decomposition (HOSVD) computation complexity of X ∈ C M× N × L is O( MNL( M + N + L) ) in Equation (11); (2) The signal subspace estimation needs O(4PLMN ) in Equation (15); (3) In Equation (21), the dimensionality reduction of the two-dimensional search requires
Summary
MIMO radar has received widespread attention in the field of target parameter estimation, which has great development potential [1,2,3,4,5]. The FDA radar sets the frequency increment on the transmitting array elements so that the beam pointing changes with the target range [19] This can be applied for the joint DOA and range estimation, which has great potential in practical applications [1,20,21]. A target location method via real-valued subspace decomposition has been proposed in [34], which implements the unitary ESPRIT method in sub-array FDA-MIMO These methods rely on the rotation invariant structure of the received signal and are only suitable when the frequency of the transmitting antenna increases linearly. (1) In the tensor domain, the proposed method realizes joint angle and range estimation of FDA-MIMO radar. Q × Q zero matrix conjugate of matrix transpose of matrix conjugation-transpose of matrix diagonalization of matrix extract phase
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