An essentialtask of the electronic support measures system is to deinterleave the intercepted radar pulse stream. To improve the radars’ antijamming capability and adapt the radars to different application scenarios, the pulse signals emitted by various radars are becoming abundant, causing the pulse stream intercepted by the reconnaissancereceiver to becomemore and more complicated. Apart from fixed pulse repetition interval (PRI) signals, other PRI modulated signals, such as jitter PRI and staggered PRI, also appear in the intercepted radar pulse stream. The aforementioned pulse stream is defined as mixed radar signals in this article. The deinterleaving of mixed radar signals is an inevitable challenge in the field of electronic countermeasures. A novel algorithm for deinterleaving mixed radar signals is proposed in this article. It is flexible for deinterleaving different PRI modulated signals. A sequence retrieval method based on clustering ideas is also proposed in the algorithm, which enables different pulse sequences to be accurately separated. Simulation results show that the new algorithm has great PRI estimation accuracy for mixed radar signals in a large pulse missing rate.

End-to-End paradigms use a unified framework to implement multi-tasks in an autonomous driving system. Despite simplicity and clarity, the performance of end-to-end autonomous driving methods on sub-tasks is still far behind the single-task methods. Meanwhile, the widely used dense BEV features in previous end-to-end methods make it costly to extend to more modalities or tasks. In this paper, we propose a Sparse query-centric paradigm for end-to-end Autonomous Driving (SparseAD), where the sparse queries completely represent the whole driving scenario across space, time and tasks without any dense BEV representation. Concretely, we design a unified sparse architecture for perception tasks including detection, tracking, and online mapping. Moreover, we revisit motion prediction and planning, and devise a more justifiable motion planner framework. On the challenging nuScenes dataset, SparseAD achieves SOTA full-task performance among end-to-end methods and significantly narrows the performance gap between end-to-end paradigms and single-task methods.

Multiple-input-multiple-output synthetic aperture radar (MIMO-SAR) technology is an effective scheme to achieve there-dimensional (3-D) imaging in millimeter wave and terahertz band. Compared with the 2-D array, the system complexity and the number of array elements are greatly reduced. In this paper, an efficient image reconstruction algorithm in wavenumber domain based on phase shift migration (PSM) was proposed for MIMO-SAR 3-D real-time imaging application. By decomposing the range-wavenumber based on binomial approximation theory, the PSM operation can be achieved only by matrix multiplication and Fourier transform (FT) algorithm without time-consuming wavenumber accumulation. Proof-of-principle experiments were also carried out in 0.1THz band based on a MIMO-SAR prototype system. Both simulation and experimental results of different targets show that the image quality of the proposed algorithm is comparable to that of back-projection algorithm (BPA). The partial enlarged images of the imaging results validate that compared with the range migration algorithm (RMA) the proposed algorithm can achieve higher reconstruction accuracy with comparable efficiency.

The 3D object detector usually uses a framework similar to 2D detection and benefits from the advancements of 2D detection tasks. In these frameworks, it is necessary to make the unstructured, sparse point cloud features into dense grids to be compatible with popular 2D operators such as convolution and transformers, which also causes extra computational costs. In this paper, we propose a simple and efficient Fully Sparse TRansformer (FSTR) for LiDAR-based 3D object detection, which is able to combine with state-of-the-art sparse backbones to form a fully sparse, end-to-end, simple, and efficient detection framework. FSTR uses the sparse voxel feature from the sparse backbone as the input token without any custom operators. Further, we introduce the dynamic queries to provide a priori location and context of the foreground for the decoder and drop the high-confidence background tokens to further reduce redundant computations. We propose Gaussian denoising queries to speed up the decoder training and make it more adaptable to the distribution of sparse voxel features. Extensive experiments on the nuScenes benchmark and the Argoverse2 benchmark validate the effectiveness of the proposed method. FSTR outperforms all LiDAR real-time methods by 69.5 mAP and 72.9 NDS on the official benchmark of nuScenes dataset. On the long-range detection benchmark Argoverse2, the proposed method achieves a new state-of-art performance of 39.9 mAP which outperforms the existing LiDAR detectors, even the LiDAR-Camera detectors by a large margin (+9.4 mAP and +7.5mAP), showing the great advantage of the proposed method for long-range detection.

Aeromagnetic noise compensation is a vital part of aerial survey measurement, and its compensation effect directly determines the quality of aeromagnetic survey data. At present, the commonly used compensation model is the T-L model, and the least squares method is used to solve for the coefficients. However, the noise source modeled in the T-L model is incomplete. Since the tail boom cannot be completely rigid, tail-boom swing is an unavoidable problem in aeromagnetic measurement. This kind of swing is the most obvious when the aircraft is maneuvering, and it will significantly interfere with the measurement data of the sensor. In this article, two causes of the swing noise are analyzed, and the nonlinear relationship between the swing displacement and the noise is derived. Since it is difficult to express the nonlinear relationship with mathematical forms to compensate for the aeromagnetic data, we propose a new compensation method that uses a 1-D convolutional neural network to perform secondary compensation on the data already compensated by the T-L model in order to remove the effect of tail-boom swing. The flight experiment data show that the proposed method can significantly improve the quality of aeromagnetic data. Compared with the T-L method, the improve ratio is increased by 60%–100%. It shows that the proposed method has a remarkable compensation effect for aeromagnetic noise.

Inspired by the circular-aperture synthetic aperture radar (Circular-SAR) imaging regime that can provide higher azimuth image resolution, the imaging regime employing the multiple-input–multiple-output (MIMO) arc array is proposed. Moreover, no efficient imaging algorithm is now available for these MIMO arc array setups. This article proposes a modified Omega-K algorithm for near-field azimuth image reconstruction based on the geometry transformation from the single-frequency MIMO arc array to the equivalent MIMO linear array. Note that this geometry transformation relies on the calculation of the phase difference and the definition of the corresponding compensation function. Compared with the single-input–single-output (SISO) arc scan requiring more elements, simulations with good agreement were performed to validate the azimuth image reconstructions of single/multiple targets. Although the computing efficiency of the proposed algorithm for MIMO arc array is lower than that of the traditional Omega-K algorithm for the SISO arc scan, it still has an obvious advantage relative to the traditional backprojection (BP) algorithm that is always taken as the gold standard. Furthermore, three realistic imaging geometries, including the metal strips at different radii, the tilted metal plate, and the human body torso cross section, were simulated to test the fidelity of the proposed reconstruction algorithm. Proof-of-principle experiments were also carried out based on an MIMO arc array with the help of one arc scanner. Finally, both simulation and experimental results validate the effectiveness of the proposed algorithm on azimuth image reconstruction quality.