Abstract
In the paper, joint angle and range estimation issue for monostatic frequency diverse array multiple-input multiple-output (FDA-MIMO) is proposed, and a tensor-based framework is addressed to solve it. The proposed method exploits the multidimensional structure of matched filters in FDA-MIMO radar. Firstly, stack the received data to form a third-order tensor so that the multidimensional structure information of the received data can be acquired. Then, the steering matrices contain the angle and rang information are estimated by using the parallel factor (PARAFAC) decomposition. Finally, the angle and range are achieved by utilizing the phase characteristic of the steering matrices. Due to exploiting the multidimensional structure of the received data to further suppress the effect of noise, the proposed method performs better in angle and range estimation than the existing algorithms based on ESPRIT, simulation results can prove the proposed method’s effectiveness.
Highlights
Multiple-input multiple-output (MIMO) radar was first proposed in [1,2,3], which is a key research point in today’s radar field
It is noted that these methods for MIMO radar with narrow-band signals cannot achieve the rang information which is very important for target localization in practice
rk,t, rk2, where θk,t and rk,t are the estimation of direction of arrival (DOA) θk and range rk of the kth target for the tth Monte Carlo trials, respectively, T denotes the total amount of Monte Carlo trials, and T 500 is preset in this simulation
Summary
Multiple-input multiple-output (MIMO) radar was first proposed in [1,2,3], which is a key research point in today’s radar field. E statistical MIMO radar is composed with separated transmit and receive antennas for obtaining both the waveform and spatial diversity gain. To take advantage of the multidimensional structural characteristics of the signal, the PARAFAC decomposition is used for angle estimation in unknown target localization by modelling tensor signals [26,27,28]. There are few studies on tensor-based FDA MIMO target estimation methods. Tensor-Based Data Model of Monostatic FDAMIMO Radar e paper is based on a narrowband monostatic FDAMIMO radar It is composed by M-element transmitting antennas and N-element receiving antennas. We apply the tensor signal model and parallel factor decomposition to the monostatic FDA-MIMO radar and derive the PARAFAC-based angle and range estimation method. We will derive the method in more detail
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