Tensor-Based Reduced-Dimension MUSIC Method for Parameter Estimation in Monostatic FDA-MIMO Radar

Frequency diverse array (FDA) radar has attracted much attention due to the angle and range dependence of beam pattern. Multiple-input-multiple-output (MIMO) high degrees freedom (DOF) spatial resolution. The FDA-MIMO radar, a hybrid FDA MIMO can be used for target parameter estimation. This paper investigates tensor-based reduced-dimension multiple signal classification (MUSIC) method, which is estimation in radar. existing subspace methods deteriorate quickly performance with small samples low signal-to-noise ratio (SNR). To deal deterioration difficulty, sparse method then proposed. However, algorithm computation complexity poor stability, making it difficult apply practice. Therefore, we use tensor capture multi-dimensional structure received signal, optimize effectiveness stability estimation, reduce overcome degradation or SNR simultaneously. In our work, first obtain by high-order-singular value decomposition (HOSVD) establish two-dimensional spectrum function. Then Lagrange multiplier applied realize one-dimensional function, estimate direction arrival (DOA) complexity. transmitting steering vector obtained partial derivative automatic pairing parameters realized. Finally, using least square process phase vector. Method analysis simulation results prove superiority reliability proposed method.