High-resolution maritime surveillance radars need to detect smalltargets and moderate/large targets such as ships in short/medium-distance regions. At a single-range resolution, radar echoes of moderate/large targets...
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High-resolution maritime surveillance radars need to detect smalltargets and moderate/large targets such as ships in short/medium-distance regions. At a single-range resolution, radar echoes of moderate/large targets severely affect the detection of smalltargets around them. In this article, a simple digital filtering method is given to realize range resolution conversion by which multiresolution radar echoes data are generated from high-range resolution data. As one of the main contributions, the compound-Gaussian model with inverse Gaussian textures (CGIG) is extended to characterize multiresolution sea clutter. Moreover, an across-resolution parameter estimation method is proposed to estimate the parameters of the multiresolution CGIG model based on across-resolution moment relationship. The other contribution is a hierarchical target detection scheme in the multiresolution CGIG clutter model. Simple nonadaptive noncoherent integration detectors are used at lower resolution channels to detect moderate/large targets and adaptive near-optimum coherent detectors in the CGIG-distributed sea clutter are adopted at higher resolution channels to detect smalltargets using the detections from the lower resolution channels as the prior information on environment. Finally, the multiresolution CGIG model and hierarchical target detection scheme are examined by an X-band island-based measured data using an unmanned aerial vehicle as a test target.
In this study, data obtained from near-range radar responses collected by an X-band noise radar targeting small aerial objects such as drones and seagulls have been analyzed for the purpose of target discrimination. T...
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ISBN:
(纸本)9798350388978;9798350388961
In this study, data obtained from near-range radar responses collected by an X-band noise radar targeting small aerial objects such as drones and seagulls have been analyzed for the purpose of target discrimination. The research aims to identify distinctive features in terms of range, speed, and micro-Doppler signatures of small air targets using a noise radar operating at the same frequency as maritime ship radars. This approach has not only facilitated the detection of small air targets with low Radar Cross Section (RCS) but also contributed additional information for classifying these targets. Considering that the noise radar used during the measurements possesses characteristics typical of a marine radar, this work has effectively demonstrated the extraction of micro-Doppler signatures of drones and seagulls performing various maneuvers in a maritime environment.
targets on the sea surface are of great concern, especially the classification of smalltargets, which is crucial for marine safety. Therefore, we propose a machine learning classifier for marine radar smalltargets b...
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It is well-known that classical direction of arrival (DOA) estimation methods work well in the case of large samples. However, these methods may be theoretically invalid in the case of small samples, which frequently ...
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It is well-known that classical direction of arrival (DOA) estimation methods work well in the case of large samples. However, these methods may be theoretically invalid in the case of small samples, which frequently occur in large array systems. Such a large array has two effects: i) The number of samples is relatively quite small, and ii) the dimension of samples is very large. To handle the above problems, a more appropriate method for solving DOA estimators in the case of high-dimensional and small samples is proposed in this paper. First, considering the special structure of received samples, an alternative well-estimated second-order statistic, known as the Gram matrix, is originally constructed to better utilize the spatial and statistical information of signals and noise contained by small samples. Second, two novel methods for estimating the number of targets are derived by combining the Gram matrix and information-theoretic criteria. Third, a novel object function and the corresponding algorithm based on the Gram matrix are designed to estimate the signal subspace more efficiently, and then the DOAs of targets are obtained by multiple signal classification methods. In particular, the theoretical analysis indicates that the improved signal subspace estimation algorithm only needs to decompose the low-dimensional Gram matrix instead of the high-dimensional sample covariance matrix. Finally, simulation results are provided to demonstrate the high accuracy and lower computational complexity of the proposed methods in the case of high-dimensional and small samples.
This paper presents a novel mid-wave infrared (MWIR) small target detection dataset (MWIRSTD) comprising 14 video sequences containing approximately 1053 images with annotated targets of three distinct classes of smal...
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ISBN:
(纸本)9798350349405;9798350349399
This paper presents a novel mid-wave infrared (MWIR) small target detection dataset (MWIRSTD) comprising 14 video sequences containing approximately 1053 images with annotated targets of three distinct classes of small objects. Captured using cooled MWIR imagers, the dataset offers a unique opportunity for researchers to develop and evaluate state-of-the-art methods for small object detection in realistic MWIR scenes. Unlike existing datasets, which primarily consist of uncooled thermal images or synthetic data with targets super-imposed onto the background or vice versa, MWIRSTD provides authentic MWIR data with diverse targets and environments. Extensive experiments on various traditional methods and deep learning-based techniques for small target detection are performed on the proposed dataset, providing valuable insights into their efficacy. The dataset and code are available at https://***/avinres/MWIRSTD.
Drone-based synthetic aperture radar (SAR) systems have increasingly gained attention due to their potential for rapid surveillance in localized areas. This paper presents a novel approach to SAR processing for subsur...
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Drone-based synthetic aperture radar (SAR) systems have increasingly gained attention due to their potential for rapid surveillance in localized areas. This paper presents a novel approach to SAR processing for subsurface target detection from a lightweight drone platform. The limited processing capacity and memory resources of small SAR platforms demand efficient recovery performance for high-resolution imaging. Compressed sensing (CS) algorithms are widely used to mitigate data storage requirements, yet they often suffer from challenges related to computational burden and detection errors. CS theory exploits signal sparsity and the incoherence of sensing matrices to reconstruct target information from reduced data measurements. Although random sensing matrices are commonly employed to ensure the independence of measured data, they incur high computational cost and memory resources. While deterministic sensing matrices provide fast data recovery, they suffer from increased internal interference, leading to degraded performance in noisy environments. This paper proposes a novel hybrid sensing matrix and recovery algorithm for efficient target detection in small drone-based SAR platforms. After establishing the principles of signal sampling and recovery, SAR imaging simulations are conducted to evaluate the performance of the proposed method with respect to data compression, processing speed, and recovery accuracy. For verification, a custom-built drone SAR platform is utilized to recover subsurface targets obscured by high-clutter backgrounds. Experimental results demonstrate the effective recovery of buried target images, highlighting the potential of the proposed method for practical applications in high-clutter environments.
smalltargets in infrared imagery exhibit challenging characteristics due to their minimal semantic information and the extremely imbalanced distribution between the targets and the background. In this paper, we propo...
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Detecting small to tiny targets in infrared images is a challenging task in computer vision, especially when it comes to differentiating these targets from noisy or textured backgrounds. Traditional object detection m...
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ISBN:
(纸本)9798350344868;9798350344851
Detecting small to tiny targets in infrared images is a challenging task in computer vision, especially when it comes to differentiating these targets from noisy or textured backgrounds. Traditional object detection methods such as YOLO struggle to detect tiny objects compared to segmentation neural networks, resulting in weaker performance when detecting smalltargets. To reduce the number of false alarms while maintaining a high detection rate, we introduce an a contrario decision criterion into the training of a YOLO detector. The latter takes advantage of the unexpectedness of smalltargets to discriminate them from complex backgrounds. Adding this statistical criterion to a YOLOv7-tiny bridges the performance gap between state-of-the-art segmentation methods for infrared small target detection and object detection networks. It also significantly increases the robustness of YOLO towards few-shot settings.
Infrared smalltargets often exhibit small scale and weak semantic features, which makes it a great challenge to their detection. To address this situation, we propose a novel network for infrared small target detecti...
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ISBN:
(纸本)9798350344868;9798350344851
Infrared smalltargets often exhibit small scale and weak semantic features, which makes it a great challenge to their detection. To address this situation, we propose a novel network for infrared small target detection that combines local details information and global contextual information. To preserve the local and high-frequency details present in infrared images, we introduce a High-frequency Aware Encoder. To extract contextual information from multi-scale feature maps, we propose a Multi-scale Context Learning Bottleneck that incorporates contextual information repeatedly and performs cross-level fusion, which enables the recognition of smalltargets based on their surroundings. Finally, a lightweight Transformer Decoder is employed to restore the feature map, while placing attention on the target pixels. Experimental results on the IRSTD-1k dataset demonstrate that our method outperforms other state-of-the-art approaches.
Detection of multiple closely spaced targets with range-Doppler (RD) migration is a challenging issue for radars, because range cell migration (RCM) and Doppler frequency migration (DFM) during the coherent processing...
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Detection of multiple closely spaced targets with range-Doppler (RD) migration is a challenging issue for radars, because range cell migration (RCM) and Doppler frequency migration (DFM) during the coherent processing interval (CPI), as well as high sidelobes of strong targets, may deteriorate the performance of target detection and parameter estimation. To realize migration correction and sidelobe suppression simultaneously, a joint iterative adaptive approach (IAA) based on RD processing outputs (RD-JIAA) is first proposed in this article. The input data of RD-JIAA are selected within a smallprocessing window centered around the response peak trajectory in range-velocity domain obtained by the RD processing. Compared with IAA and wideband IAA (WIAA), RD-JIAA has low computational burden. Some instructive suggestions on the selection of processing window sizes are presented considering that most of the target energy should be included in the processing window. Then, a fast implementation, namely, RD-JIAA based on the signal sparsity (RD-SJIAA), is presented to further improve the computational efficiency with tolerable performance loss. Both RD-JIAA and RD-SJIAA are able to utilize the structure relationships between covariance matrices of adjacent range cells to reduce the computational complexity. Finally, the performance of the proposed methods is evaluated by numerical examples.
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