Radar Target Modeling Using In-Flight Radar Cross-Section Measurements

Persson, Björn

doi:10.2514/1.c033932

Cited by 12 publications

(5 citation statements)

References 19 publications

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“…In this study, the unimodal parametric densities considered are 1) Peter Swerlings statistical models, 2) gamma distribution, and 3) generalized Pareto distribution (GPD). The Peter Swerlings statistical models have been widely used to describe the radar scattering characteristics of airborne military aircraft and ground vehicles/tanks [45], [46]. The Peter Swerling RCS density is described by the generalized Chi-Squared (χ 2 or CS) distribution with N = 2m degrees of freedom given as:…”

Section: A Unimodal Uav Rcs Statistical Distributionmentioning

confidence: 99%

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Comparative Analysis of Radar Cross Section Based UAV Classification Techniques

Ezuma¹,

Anjinappa²,

Semkin³

et al. 2021

Preprint

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This work investigates the problem of unmanned aerial vehicles (UAVs) identification using their radar crosssection (RCS) signature. The RCS of six commercial UAVs are measured at 15 GHz and 25 GHz in an anechoic chamber, for both vertical-vertical and horizontal-horizontal polarization. The RCS signatures are used to train 15 different classification algorithms, each belonging to one of three different categories: statistical learning (SL), machine learning (ML), and deep learning (DL). The study shows that while the classification accuracy of all the algorithms increases with the signal-tonoise ratio (SNR), the ML algorithm achieved better accuracy than the SL and DL algorithms. For example, the classification tree ML achieves an accuracy of 98.66% at 3 dB SNR using the 15 GHz VV-polarized RCS test data from the UAVs. We investigate the classification accuracy using Monte Carlo analysis with the aid of boxplots, confusion matrices, and classification plots. On average, the accuracy of the classification tree ML model performed better than the other algorithms, followed by the Peter Swerling statistical models and the discriminant analysis ML model. In general, the classification accuracy of the ML and SL algorithms outperformed the DL algorithms (Squeezenet, Googlenet, Nasnet, and Resnet 101) considered in the study. Furthermore, the computational time of each algorithm is analyzed. The study concludes that while the SL algorithms achieved good classification accuracy, the computational time was relatively long when compared to the ML and DL algorithms. Also, the study shows that the classification tree achieved the fastest average classification time of about 0.46 ms.

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Section: A Unimodal Uav Rcs Statistical Distributionmentioning

confidence: 99%

“…As a result, the GPD density is flexible to fit various RCS data structures. Several studies have shown that radar targets like aircraft and clutters can be modeled using the GPD and Pareto-based densities [45], [49], [50].…”

Section: A Unimodal Uav Rcs Statistical Distributionmentioning

confidence: 99%

Comparative Analysis of Radar Cross Section Based UAV Classification Techniques

Ezuma¹,

Anjinappa²,

Semkin³

et al. 2021

Preprint

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“…In view of the above problems, there are currently two main types of solutions. One is the traditional multitarget tracking method based on data association [9]. is method first uses data correlation technology to determine the correlation between sensor observations and target sources and then estimates the state of each target separately.…”

Section: Related Workmentioning

confidence: 99%

Research on Channel Optimization of Ads‐B Aviation Target Surveillance Radar Based on Improved Filtering Algorithm

Zheng¹,

Tang²,

2021

Complexity

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In order to improve the surveillance effect of the aviation target surveillance radar, this paper improves the traditional filtering algorithm and builds the channel optimization system of the ADS-B aviation target surveillance radar based on the improved filtering algorithm. Moreover, this paper uses algorithm improvement to ensure the positive definite or semipositive definiteness of the state covariance and uses the root mean square volume Kalman filter to avoid the filter divergence or tracking interruption caused by the nonpositive definiteness of the matrix; the filtering principle of the interactive multimodel is to use multiple filters for parallel processing and achieve the adaptive adjustment algorithm residual error by adjusting the one-step prediction covariance in the adjustment algorithm. In addition, this paper combines the actual needs to construct a system functional structure to optimize the channel of the ADS-B aviation target surveillance radar and uses software engineering methods to model and analyze the requirements. Finally, this paper designs experiments to verify system performance. The research results show that the performance of the system constructed in this paper meets actual needs.

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“…One requires a reasonable approximation for (5), so that when applied to (6), and subsequently to (4), it becomes possible to evaluate this integral.…”

Section: An Approximation For the Cutmentioning

confidence: 99%

“…In addition to this, an approximation is applied for the CUT, relative to the additivity associated with Rayleigh variates. A second, and more recent treatment, can be found in [5], who suggests in the case of more modern X-band maritime surveillance radar, the return in the presence of a target, can be modelled by a Pareto distribution, which is also used for the clutter measurements. The difference is that the Pareto parameters are modified to account for the presence of a target.…”

Section: Introductionmentioning

confidence: 99%

Distribution of the Cell Under Test in Sliding Window Detection Processes

Weinberg¹

2019

PIER Letters

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Analysis of sliding window detection detection processes requires careful consideration of the cell under test, which is an amplitude squared measurement of the signal plus clutter in the complex domain. Some authors have suggested that there is sufficient merit in the approximation of the cell under test by a distributional model similar to that assumed for the clutter distribution. Under such an assumption, the development of Neyman-Pearson detectors is facilitated. The purpose of the current paper is to demonstrate, in a modern maritime surveillance radar context, that such an approximation is invalid.

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Radar Target Modeling Using In-Flight Radar Cross-Section Measurements

Cited by 12 publications

References 19 publications

Comparative Analysis of Radar Cross Section Based UAV Classification Techniques

Comparative Analysis of Radar Cross Section Based UAV Classification Techniques

Research on Channel Optimization of Ads‐B Aviation Target Surveillance Radar Based on Improved Filtering Algorithm

Distribution of the Cell Under Test in Sliding Window Detection Processes

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