Range-spread target (RST) detection is a concerned issue in high-resolution radar. Exploitation of target scattering characteristic can improve the detection performance of a RST, but is usually unavailable because of the non-cooperative nature of the target. In this work, a two-stage implemented detector based on an optimized radar workflow is proposed to address this problem. In the first stage, named high-resolution range profile-pursuing (HRRP-pursuing), a fast Fourier transform-initialized Akaike information criterion (AIC) algorithm is proposed to estimate target HRRP using historical echoes. In the second stage, the proposed detector uses the estimated HRRP as a reference signal and performs matched filtering to realize RST detection. Theoretical analysis of the false alarm property of the detector is derived. The effectiveness of the proposed detector is verified using simulated and real radar data experiments and the influences of the number of used historical echoes and target sparseness on detection performance are discussed.

Range-spread target (RST) detection is an important issue for high-resolution radar (HRR). Traditional detectors relying on manually designed detection statistics have their performance limitations. Therefore, in this work, two deep learning-based detectors are proposed for RST detection using HRRPs, i.e., an NLS detector and DFCW detector. The NLS detector leverages domain knowledge from the traditional detector, treating the input HRRP as a low-level feature vector for target detection. An interpretable NLS module is designed to perform noise reduction for the input HRRP. The DFCW detector takes advantage of the extracted high-level feature map of the input HRRP to improve detection performance. It incorporates a feature cross-weighting module for element-wise feature weighting within the feature map, considering the channel and spatial information jointly. Additionally, a nonlinear accumulation module is proposed to replace the conventional noncoherent accumulation operation in the double-HRRP detection scenario. Considering the influence of the target spread characteristic on detector performance, signal sparseness is introduced as a measure and used to assist in generating two datasets, i.e., a simulated dataset and measured dataset incorporating real target echoes. Experiments based on the two datasets are conducted to confirm the contribution of the designed modules to detector performance. The effectiveness of the two proposed detectors is verified through performance comparison with traditional and deep learning-based detectors.

以道统和德性来书写并动员抗战,可视为郭沫若的战时牺牲美学与文化政治策略.具体而言,"甘愿做炮灰"的道德理想,是郭沫若的自我鼓动和政治告白,尽显浪漫与激情.他对传统伦理的挪用,向祝颂悼祭传统的借力,不仅召唤着智识阶层,更为抗战建国和民族国家的信仰添补宗教般的神秘趋力.另外,他通过对优良德性的发现和提炼,将民众写入历史,赋予其为国牺牲的主体地位.郭沫若在关于牺牲的表达中展示的道德的真诚和成仁的意气,既关联着把握历史脉络的雄强自信,亦潜藏着富有韧性的生存哲学.

Low-light image enhancement (LLIE) is often used to improve the quality of video images in dark light conditions. For LLIE, global image enhancement is performed on the entire image which often leads to a waste of computational resources and ineffectiveness. In this paper, we proposed a radar-camera fusion framework-based approach to enhance the region of interest (ROI). The radar was utilized to locate target areas in video data for local enhancement. Target location was then projected onto the image based on the spatio-temporal calibration and a bounding box centered on the target was got. The corresponding part of the image within the bounding box was the ROI, and only this part of the image was considered for LLIE. An obvious benefit for image local enhancement is the computational efficiency. Experimental results showed that the local image enhancement scheme improved the visual effect while reducing the computation time. One more inspiring benefit is the improvement of object detection performance. It can be seen that the accuracy and the metric of mAP@0.5:0.95 trained by the network based on locally enhanced images were better than the training results using unprocessed images or globally enhanced images.

19世纪末,借助翻译,"牺牲"的现代意义自 日本传入中国."牺牲"及其负载的文化内涵经过晚清知识分子的激活与再造,成为诠释和想象未来国家与社会的"元语言".无限增殖的"牺牲",预示着国家认同的出现,为救国提供了思想动力和评判依据,并影响到晚清小说对为国牺牲的言说."牺牲"的外衣下纷繁的救国叙事,不仅意味着民族国家的价值体系尚不完善,而且表明"为君王"规约的"牺牲"崩塌后,文学自身被开启了无限可能.立足跨国、跨文化的视野,追溯"牺牲"观念的源头,并深入分析其生成初期对晚清文学产生的影响,能为反思文学史的知识生产提供窗口,对国民文化建设亦具有重要意义.

Multi-modality three-dimensional (3D) object detection is a crucial technology for the safe and effective operation of environment perception systems in autonomous driving. In this study, we propose a method called context clustering-based radar and camera fusion for 3D object detection (ConCs-Fusion) that combines radar and camera sensors at the intermediate fusion level to achieve 3D object detection. We extract features from heterogeneous sensors and input them as feature point sets into the fusion module. Within the fusion module, we utilize context cluster blocks to learn multi-scale features of radar point clouds and images, followed by upsampling and fusion of the feature maps. Then, we leverage a multi-layer perceptron to nonlinearly represent the fused features, reducing the feature dimensionality to improve model inference speed. Within the context cluster block, we aggregate feature points of the same object from different sensors into one cluster based on their similarity. All feature points within the same cluster are then fused into a radar–camera feature fusion point, which is self-adaptively reassigned to the originally extracted feature points from a simplex sensor. Compared to previous methods that only utilize radar as an auxiliary sensor to camera, or vice versa, the ConCs-Fusion method achieves a bidirectional cross-modal fusion between radar and camera. Finally, our extensive experiments on the nuScenes dataset demonstrate that ConCs-Fusion outperforms other methods in terms of 3D object detection performance.

An amplitude weighted cross-correlation detector is proposed for the detection of the Doppler-spread target (DST), based on the frequency modulated continuous waveform (FMCW) radar. The concurrent range-Doppler matrices (RDM) make cross-correlation optimized with higher accumulation gain and significantly reduced computation effort. A novel measurement of signal sparseness is defined. The quantitative analysis of the proposed detector, compared with the energy integration detector, is realized based on the signal sparseness. Two generalizations of proposed detector are discussed for the number of antennas greater than two. Data from a walking pedestrian are got and used to verify the effectiveness of the proposed detector.

To minimise the noise interference in the radar received echoes and optimise the detection issue for a wideband radar system, a novel wideband radar track-before-detect detector via Wiener Filter is proposed. The Wiener Filter can effectively remove the noise mixed in the echoes and make the filtered signal consistent with the original signal. After applying maximum likelihood estimation to the echoes, the authors can reconstruct the desired signal required for Wiener filtering by extracting the range information of each scatterer. Then Wiener Filtering is performed on the echo containing noise with the desired signal to complete the denoising. Subsequently, we exploit the correlation between the adjacent echoes for detection. The effectiveness of the detector can be verified by the numerical simulation results of synthetic and real radar data. The proposed detector is superior to the waveform contrast detector, inter-period correlation processor and modified correlation matrix detector.

Parameter estimation of polynomial-phase signals (PPSs) observed in additive white Gaussian noise (AWGN) is addressed. Most of the existing estimators cannot work on a fully identifiable region. Using the algebraic number theory, McKilliam et al. proposed a least squares unwrapping (LSU) estimator, which can operate on the entire identifiable region. However, its computational load may be large, especially when the number of samples is large. In this study, the authors first extend the amplitude-weighted phase-based estimator (AWPE) for sinusoidal and chirp signals to PPSs and derive a time domain maximum likelihood estimator. The performance is analysed and compared with the Cramer-Rao lower bound (CRLB). Then, the authors propose an iterative compound time-frequency domain parameter estimation method, which includes a coarse estimation step and a fine estimation step conducted by the discrete polynomial phase transform and AWPE estimator, respectively. Monte-Carlo simulations show that the proposed method can work on the entire identifiable region and that it outperforms the existing state-of-the-art estimators. Its computational complexity is considerably lower than that of the LSU estimator, while its threshold signal-to-noise ratio is a few decibels higher than that of the LSU estimator.

提出了利用压缩感知解微多普勒模糊的方法.由于目前雷达无法实现发射脉冲的随机性,采用电磁仿真软件FE-KO来实现具有随机发射时刻的雷达回波模拟,用于算法验证.仿真过程中,考虑微动目标运动的复杂性,采用FEKO结合MATLAB的方式实现目标姿态的动态更新.首先由MATLAB计算出每个稀疏时刻目标的运动状态,然后把参数传递给FEKO,获得不同时刻对应的姿态角下目标的电磁散射数据,最后利用本文算法解出无模糊的微多普勒频率.对5种不同的微动模型进行仿真,仿真结果均与理论结果一致,由此验证了该算法的可行性和仿真模型的有效性.

When using a long range radar (LRR) to track a target with micromotion, the micro-Doppler embodied in the radar echoes may suffer from ambiguity problem. In this paper, we propose a novel method based on compressed sensing (CS) to solve micro-Doppler ambiguity. According to the RIP requirement, a sparse probing pulse train with its transmitting time random is designed. After matched filtering, the slow-time echo signals of the micromotion target can be viewed as randomly sparse sampling of Doppler spectrum. Select several successive pulses to form a short-time window and the CS sensing matrix can be built according to the time stamps of these pulses. Then performing Orthogonal Matching Pursuit (OMP), the unambiguous micro-Doppler spectrum can be obtained. The proposed algorithm is verified using the echo signals generated according to the theoretical model and the signals with micro-Doppler signature produced using the commercial electromagnetic simulation software FEKO.