Correlation between Infrared Radiation Characteristic Signals and Target Maneuvering Modes

Tian, Ke; Zhou, Zucheng; Hongqiang, Liu; Yang, Yuekui

doi:10.3788/aos201838.0204003

Cited by 2 publications

(3 citation statements)

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“…Table 1 provides a comprehensive overview of the specific details of the CNN-GRU component. Assuming that the length of the input sequential data is 200 with a dimension of 8, the input shape is defined as (None, 1,200,8), where None represents the batch_size.…”

Section: Cnn-gru Componentmentioning

confidence: 99%

“…Wang et al [7] proposed a method based on the judgment of target-effective imaging pixels for measuring infrared radiation, distinguishing between small targets and surface targets. Kou et al [8] established a dynamic feature model to study the correlation between infrared radiation characteristic signals and target maneuvering modes. Chen et al [9] studied the variation in the infrared radiation brightness of the sky background under different weather conditions, providing a reference value for infrared target detection and recognition.…”

Section: Introductionmentioning

confidence: 99%

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Classification of High-Altitude Flying Objects Based on Radiation Characteristics with Attention-Convolutional Neural Network and Gated Recurrent Unit Network

Dai,

Cao,

Liu

et al. 2023

Remote Sensing

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In the task of classifying high-altitude flying objects, due to the limitations of the target flight altitude, there are issues such as insufficient contour information, low contrast, and fewer pixels in the target objects obtained through infrared detection technology, making it challenging to accurately classify them. In order to improve the classification performance and achieve the effective classification of the targets, this study proposes a high-altitude flying object classification algorithm based on radiation characteristic data. The target images are obtained through an infrared camera, and the radiation characteristics of the targets are measured using radiation characteristic measurement techniques. The classification is performed using an attention-based convolutional neural network (CNN) and gated recurrent unit (GRU) (referred to as ACGRU). In ACGRU, CNN-GRU and GRU-CNN networks are used to extract vectorized radiation characteristic data. The raw data are processed using Highway Network, and SoftMax is used for high-altitude flying object classification. The classification accuracy of ACGRU reaches 94.8%, and the F1 score reaches 93.9%. To verify the generalization performance of the model, comparative experiments and significance analysis were conducted with other algorithms on radiation characteristic datasets and 17 multidimensional time series datasets from UEA. The results show that the proposed ACGRU algorithm performs excellently in the task of high-altitude flying object classification based on radiation characteristics.

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Section: Cnn-gru Componentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of High-Altitude Flying Objects Based on Radiation Characteristics with Attention-Convolutional Neural Network and Gated Recurrent Unit Network

Dai,

Cao,

Liu

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The infrared radiation characteristics of highly maneuverable targets and their information interpretation are an important basis for target detection, recognition, and tracking [1]. The technical tackling of stealth materials on the surface of aircraft and the precise strike of suspicious targets in the air are in a great need of the real radiation characteristics of aircraft targets [2,3].…”

Section: Introductionmentioning

confidence: 99%

Research on a Measurement Method for Middle-Infrared Radiation Characteristics of Aircraft

et al. 2022

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Aiming at the problem wherein temperature inversion accuracy is unstable due to the major differences in atmospheric transmittance under various observation paths, a method for measuring radiation characteristics of an aircraft engine’s hot parts and skin using a cooled middle-wave infrared camera is proposed. Based on the analysis of the aircraft’s infrared radiation characteristics, the atmospheric transmission model of any observation path was revised, the absolute radiation correction model was established, and the temperature inversion equation was calculated. Then, we used the quasi-Newton method to calculate the skin temperature and discussed uncertainty sources. After the theoretical study, an outfield test was carried out. A middle-wave infrared camera with a wavelength of 3.7–4.8 μm was applied to the actual experimental observation of the turbofan civil aviation aircraft. The ground observation distance was 15 km, and the flying height was 3 km. When implementing temperature inversion with the method presented in this paper, the surface temperature of the aircraft engine hot parts was 381 K, the correction uncertainty was ±10 K, the surface temperature of the skin was 296 K, and the correction uncertainty was ±6 K. As the experiment showed, the method in this paper can effectively implement infrared target temperature inversion and provide a reference for the quantification of infrared data.

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Correlation between Infrared Radiation Characteristic Signals and Target Maneuvering Modes

Cited by 2 publications

References 0 publications

Classification of High-Altitude Flying Objects Based on Radiation Characteristics with Attention-Convolutional Neural Network and Gated Recurrent Unit Network

Classification of High-Altitude Flying Objects Based on Radiation Characteristics with Attention-Convolutional Neural Network and Gated Recurrent Unit Network

Research on a Measurement Method for Middle-Infrared Radiation Characteristics of Aircraft

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