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.

Reconstruction of Generative Adversarial Networks in Cross Modal Image Generation with Canonical Polyadic Decomposition

Generating pictures from text is an interesting, classic, and challenging task. Benefited from the development of generative adversarial networks (GAN), the generation quality of this task has been greatly improved. Many excellent cross modal GAN models have been put forward. These models add extensive layers and constraints to get impressive generation pictures. However, complexity and computation of existing cross modal GANs are too high to be deployed in mobile terminal. To solve this problem, this paper designs a compact cross modal GAN based on canonical polyadic decomposition. We replace an original convolution layer with three small convolution layers and use an autoencoder to stabilize and speed up training. The experimental results show that our model achieves 20% times of compression in both parameters and FLOPs without loss of quality on generated images.