Deep Learning Approach for Fixed and Rotary-Wing Target Detection and Classification in Radars

Rizvi, Danish Raza; Ahmad, Shahzor; Khan, Khalid Amin; Hasan, Aamir; Masood, Ammar

doi:10.1109/maes.2021.3140064

Cited by 13 publications

(15 citation statements)

References 27 publications

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“…The false alarm rate is very low, creditably so from the surveillance perspective but not so useful to explore our performance prediction results. For this reason we introduce at this point some synthetic clutter, as in the model ( 37)- (38). Examples of binary images with synthetic clutter added are reported in the third row of Fig.…”

Section: Discussionmentioning

confidence: 99%

“…We generate the synthetic training sets Y k under H k , k = 0, 1, each composed by m y images. Under H 1 , The images in Y k are generated according to (37)- (38), with varying target positions, shapes and orientations; thus, the size of the target n j varies for j = 1, . .…”

Section: B Dependent Observations: Target Detection In Binary Imagesmentioning

confidence: 99%

“…where n = i≥1 θ i increases, and the randomness of the observations X (n) is given by the Bernoulli realizations in (37)- (38). Having fixed the shape of the target, we can generate the characterization set for each value of n that is of interest, and then we can numerically compute the asymptotic scaled LMGF and the relevant parameters of the exact asymptotics to construct the approximate error probability curves (2).…”

Section: B Dependent Observations: Target Detection In Binary Imagesmentioning

confidence: 99%

“…In video analysis and image understanding, DL methods represent the state of the art for object detection [29] and multi-object tracking [30]. DL is also used in Multiple Input Multiple Output (MIMO) communications [31], active sensing for communications [32], radar and sonar processing [33]- [38].…”

Section: Introductionmentioning

confidence: 99%

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Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis

Braca¹,

Millefiori²,

Aubry³

et al. 2022

Preprint

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We study the performance-and specifically the rate at which the error probability converges to zero-of Machine Learning (ML) classification techniques. Leveraging the theory of large deviations, we provide the mathematical conditions for a ML classifier to exhibit error probabilities that vanish exponentially, say ∼ exp (−n I + o(n)), where n is the number of informative observations available for testing (or another relevant parameter, such as the size of the target in an image) and I is the error rate. Such conditions depend on the Fenchel-Legendre transform of the cumulant-generating function of the Data-Driven Decision Function (D3F, i.e., what is thresholded before the final binary decision is made) learned in the training phase. As such, the D3F and, consequently, the related error rate I, depend on the given training set, which is assumed of finite size. Interestingly, these conditions can be verified and tested numerically exploiting the available dataset, or a synthetic dataset, generated according to the available information on the underlying statistical model. In other words, the classification error probability convergence to zero and its rate can be computed on a portion of the dataset available for training. Coherently with the large deviations theory, we can also establish the convergence, for n large enough, of the normalized D3F statistic to a Gaussian distribution. This property is exploited to set a desired asymptotic false alarm probability, which empirically turns out to be accurate even for quite realistic values of n. Furthermore, approximate error probability curves ∼ ζn exp (−n I) are provided, thanks to the refined asymptotic derivation (often referred to as exact asymptotics), where ζn represents the most representative sub-exponential terms of the error probabilities. Leveraging the refined asymptotic, we are able to compute an analytically accurate approximation of the classification performance for both the regimes of small and large values of n. Theoretical findings are corroborated by extensive numerical simulations. Real-world data, acquired by an X-band maritime radar system for surveillance, are exploited to assess the validity of the proposed theory.

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Section: Discussionmentioning

confidence: 99%

Section: B Dependent Observations: Target Detection In Binary Imagesmentioning

confidence: 99%

Section: B Dependent Observations: Target Detection In Binary Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis

Braca¹,

Millefiori²,

Aubry³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It originated from Zdzislaw I. Pawlak [ 1 ] and has been identified as a creative and innovative mathematical tool in the last two decades. The rough-set-based data mining approaches have superiority in that they need no prior information, in contrast with other widely utilized strategies, such as SVM, PCA, and DNN [ 2 , 3 , 4 , 5 , 6 ]. Attribute reduction, or feature selection, has become one of the hot spots in the research area of big data.…”

Section: Introductionmentioning

confidence: 99%

An Accelerating Reduction Approach for Incomplete Decision Table Using Positive Approximation Set

Yan

Han

Zhang

et al. 2022

Sensors

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Due to the explosive growth of data collected by various sensors, it has become a difficult problem determining how to conduct feature selection more efficiently. To address this problem, we offer a fresh insight into rough set theory from the perspective of a positive approximation set. It is found that a granularity domain can be used to characterize the target knowledge, because of its form of a covering with respect to a tolerance relation. On the basis of this fact, a novel heuristic approach ARIPA is proposed to accelerate representative reduction algorithms for incomplete decision table. As a result, ARIPA in classical rough set model and ARIPA-IVPR in variable precision rough set model are realized respectively. Moreover, ARIPA is adopted to improve the computational efficiency of two existing state-of-the-art reduction algorithms. To demonstrate the effectiveness of the improved algorithms, a variety of experiments utilizing four UCI incomplete data sets are conducted. The performances of improved algorithms are compared with those of original ones as well. Numerical experiments justify that our accelerating approach enhances the existing algorithms to accomplish the reduction task more quickly. In some cases, they fulfill attribute reduction even more stably than the original algorithms do.

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Deep learning-based lightweight radar target detection method

Liang

Chen

Duan³

et al. 2023

J Real-Time Image Proc

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For target detection tasks in complicated backgrounds, a deep learning-based radar target detection method is suggested to address the problems of a high false alarm rate and the difficulties of achieving high-performance detection by conventional methods. Considering the issues of large parameter count and memory occupation of the deep learning-based target detection models, a lightweight target detection method based on improved YOLOv4-tiny is proposed. The technique applies depthwise separable convolution (DSC) and bottleneck architecture (BA) to the YOLOv4-tiny network. Moreover, it introduces the convolutional block attention module (CBAM) in the improved feature fusion network. It allows the network to be lightweight while ensuring detection accuracy. We choose a certain number of pulses from the pulse-compressed radar data for clutter suppression and Doppler processing to obtain range–Doppler (R–D) images. Experiments are run on the R–D two-dimensional echo images, and the results demonstrate that the proposed method can quickly and accurately detect dim radar targets against complicated backgrounds. Compared with other algorithms, our approach is more balanced regarding detection accuracy, model size, and detection speed.

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Deep Learning Approach for Fixed and Rotary-Wing Target Detection and Classification in Radars

Cited by 13 publications

References 27 publications

Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis

Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis

An Accelerating Reduction Approach for Incomplete Decision Table Using Positive Approximation Set

Deep learning-based lightweight radar target detection method

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