FDA-MIMO for target localization via multi-pulse tensor decomposition

By combining multiple input multiple output (MIMO) technology and multiple matched filters with frequency diverse array (FDA), FDA-MIMO radar can be used to achieve two-dimensional target localization with range and angle. In this paper, we propose two FDA-MIMO multi-pulse target localization methods based on tensor decomposition. Based on the canonical polyadic decomposition theory, the signal models of CPD-DP-FDA with double-pulse and CPD-SP-FDA with stepped frequency pulses are established. By analyzing the signal processing procedures of the two schemes, the indicator beampattern used for target localization is obtained. The parameter estimation accuracy of the proposed method is investigated in single target and multiple targets scenarios, and the proposed method is compared with the traditional double-pulse method. The results show that the target localization method based on tensor decomposition can effectively solve the problem of multi-target indication ambiguity. The target positioning effect can be further improved by combining stepped frequency pulses. The derivation of Cramer–Rao Lower Bound (CRLB) demonstrates the superiority of the method.

Signal Model and Parameter Estimation for Colocated Mimo Radar

In this paper, we consider the signal model and parameter estimation for multiple-input multiple-output (MIMO) radar with colocated antennas on stationary platforms. Considering internal clutter motion, a closed form of the covariance matrix of the clutter signal is derived. Based on the proposed closed form and low rank property of the clutter covariance matrix and by using the singular value decomposition, we have proposed a subspace model for the clutter signal. Following the proposed signal model, we have provided maximum likelihood (ML) estimation for its unknown parameters. Finally, the application of the proposed ML estimation in space time adaptive processing (STAP) is investigated in simulation results. Our ML estimation needs no secondary training data and it can be used in scenarios with nonhomogeneous clutter in range.

MIMO Radar Parallel Simulation System Based on CPU/GPU Architecture

The data volume and computation task of MIMO radar is huge; a very high-speed computation is necessary for its real-time processing. In this paper, we mainly study the time division MIMO radar signal processing flow, propose an improved MIMO radar signal processing algorithm, raising the MIMO radar algorithm processing speed combined with the previous algorithms, and, on this basis, a parallel simulation system for the MIMO radar based on the CPU/GPU architecture is proposed. The outer layer of the framework is coarse-grained with OpenMP for acceleration on the CPU, and the inner layer of fine-grained data processing is accelerated on the GPU. Its performance is significantly faster than the serial computing equipment, and satisfactory acceleration effects have been achieved in the CPU/GPU architecture simulation. The experimental results show that the MIMO radar parallel simulation system with CPU/GPU architecture greatly improves the computing power of the CPU-based method. Compared with the serial sequential CPU method, GPU simulation achieves a speedup of 130 times. In addition, the MIMO radar signal processing parallel simulation system based on the CPU/GPU architecture has a performance improvement of 13%, compared to the GPU-only method.