A Super-Resolution DOA Estimation Method for Fast-Moving Targets in MIMO Radar

Direction of arrival (DOA) estimation is an essential problem in the radar systems. In this paper, the problem of DOA estimation is addressed in the multiple-input and multiple-output (MIMO) radar system for the fast-moving targets. A virtual aperture is provided by orthogonal waveforms in the MIMO radar to improve the DOA estimation performance. Different from the existing methods, we consider the DOA estimation method with only one snapshot for the fast-moving targets and achieve the super-resolution estimation from the snapshot. Based on a least absolute shrinkage and selection operator (LASSO), a denoise method is formulated to obtain a sparse approximation to the received signals, where the sparsity is measured by a new type of atomic norm for the MIMO radar system. However, the denoise problem cannot be solved efficiently. Then, by deriving the dual norm of the new atomic norm, a semidefinite matrix is constructed from the denoise problem to formulate a semidefinite problem with the dual optimization problem. Finally, the DOA is estimated by peak-searching the spatial spectrum. Simulation results show that the proposed method achieves better performance of the DOA estimation in the MIMO radar system with only one snapshot

An Improved 2-D DOA Estimation with L-shaped Arrays Based on PM

In this paper, an improved two-dimensional (2-D) direction of arrival (DOA) estimation method is proposed for narrow signals impinging on an L-shaped arrays. Based on the propagator method (PM), the computational loads of the proposed method can be significantly smaller since the PM does not require any eigenvalue decomposition of the received data. With a propagator matrix, the proposed method constructs a new extended matrix to estimate the elevation angle, which improves the DOA estimation performance in low SNR. By exploiting the covariance matrix of the received data, another propagator matrix is achieved, then pair matching and peak searching are used to achieve the corresponding 2-D azimuth angles, which reduces the occurrence of estimation failure and errors. In the case of DOA estimation for two signals, at RMSE = 0.2, the proposed method results in a gain improvement of about 5dB over the joint singular value decomposition (SVD) method and 9.5 dB over the PM method

Weighted incoherent signal subspace method for DOA estimation on wideband colored signals

Wideband direction-of-arrival (DOA) estimation is a key part in array signal processing. Existing algorithms for the wideband DOA estimation are often studied in the situation of uniformly distributed energy. And all the frequency bins are weighted equally in these algorithms. However, these algorithms perform unsatisfactorily when encountering wideband colored signals with nonuniform energy spectrum. To improve the performance of DOA estimation for wideband colored signals, we proposed two weighting methods, which are based on the perturbed subspace theory and random matrix theory respectively. The two methods weight the space spectrum from all the frequency bins according to the mean square error (MSE) of DOA estimation in each frequency bin. Numerical results show that the random matrix theory based method performs well, due to the inference premise that the dimensions of matricesincrease at the same rate. The perturbed subspace based method, which is concise in calculating the weights, shows high accuracy only at high signal to noise ratio (SNR) and with adequate snapshots. The effectiveness of the two algorithms are also demonstrated by comparing them to various existing algorithms and the Cramér-Rao bound

Robust Space Time Adaptive Processing Methods for Synthetic Aperture Radar

This paper proposes two modified space time adaptive processing (STAP) methods based on piecewise sub-apertures and data constraints for non-stationary interference cancellation in synthetic aperture radar (SAR) applications. In these methods, the entire synthetic aperture time is divided into several sub-apertures so that the interference can be considered as stationary in each sub-aperture. At the same time, the consistency of the echo phase in the slow time domain is preserved by the data constraint to ensure the null depth of the antenna pattern for non-stationary interference cancellation and the performance of azimuth focusing in SAR. The proposed algorithms are validated through the model simulation and measured data

Multi-Scale Feature Aggregation by Cross-Scale Pixel-to-Region Relation Operation for Semantic Segmentation

Exploiting multi-scale features has shown great potential in tackling semantic segmentation problems. The aggregation is commonly done with sum or concatenation (concat) followed by convolutional (conv) layers. However, it fully passes down the high-level context to the following hierarchy without considering their interrelation. In this work, we aim to enable the low-level feature to aggregate the complementary context from adjacent high-level feature maps by a cross-scale pixel-to-region relation operation. We leverage cross-scale context propagation to make the long-range dependency capturable even by the high-resolution low-level features. To this end, we employ an efficient feature pyramid network to obtain multi-scale features. We propose a Relational Semantics Extractor (RSE) and Relational Semantics Propagator (RSP) for context extraction and propagation respectively. Then we stack several RSP into an RSP head to achieve the progressive top-down distribution of the context. Experiment results on two challenging datasets Cityscapes and COCO demonstrate that the RSP head performs competitively on both semantic segmentation and panoptic segmentation with high efficiency. It outperforms DeeplabV3 [1] by 0.7% with 75% fewer FLOPs (multiply-adds) in the semantic segmentation task.Comment: Accepted to RA-L 2021. in IEEE Robotics and Automation Letters. The contents of this paper were also selected by the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021) Program Committee for presentation at the Conferenc