Novel Approach for Ballistic Targets Classification from HRRP Frame

Persico, Adriano Rosario; Ilioudis, Christos; Clemente, Carmine; Soraghan, John J.

doi:10.1109/sspd.2017.8233248

Cited by 7 publications

(8 citation statements)

References 10 publications

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“…All the details on the RCS models for both the two polarization are well described in [20]. Figure 7 shows the normalized HRRPs over 360 • , with ∆R = 0, from a cylinder whose height and diameter are 1 m and 0.7 m, respectively [17]. From Figure 7a it is noted that for each value of the aspect angle three scatterers are simultaneously visible at most.…”

Section: Radar Cross Section Modelmentioning

confidence: 99%

“…In this section the classification algorithm presented in [17] which is able to extract reliable feature from the HRRP frame based on the micro-motions exhibited by BTs is described. Figure 9 represents a scheme block of the presented algorithm.…”

Section: Hrrp Frame Based Classification Algorithm For Btsmentioning

confidence: 99%

“…Finally a 2-D image of target is reconstructed by applying the IRT on the echo TFD. In this paper the results and findings on an Automatic Target Recognition (ATR) algorithm proposed in [17] are presented, with the aim to classify targets in a BMD scenario from a sequence of HRRPs. The algorithm is based on the IRT of the HRRP frame, which leads to a 2-D target signature containing information on target motions and the space distribution of its principal scattering points.…”

Section: Introductionmentioning

confidence: 99%

“…For this specific classification approach the rotation invariance is fundamental to ensure robustness with respect to the initial phase of the target micro-motion. In this paper, the work presented in [17] is extended by introducing additional models for the radar return, investigating the effect of the micro-Doppler modulation and different acquisition scenario such as polarization diversity. Moreover, the algorithm is tested in the case of partial data frame available for the extraction of the target's signature.…”

Section: Introductionmentioning

confidence: 99%

“…Section II introduces the target model for BM warheads and decoys. Section III describes the classification framework proposed in [17], highlighting in details how the micro-Doppler effect due to target micro-motion affects the target signature. In Section IV the effectiveness of the proposed algorithm is demonstrated and the performance analysis obtained from simulated data is shown.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Novel Classification Algorithm for Ballistic Target Based on HRRP Frame

Persico

Ilioudis

Clemente

et al. 2019

IEEE Trans. Aerosp. Electron. Syst.

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Nowadays the identification of ballistic missile warheads in a cloud of decoys and debris is essential for defence systems in order to optimize the use of ammunition resources, avoiding to run out of all the available interceptors in vain. This paper introduces a novel solution for the classification of ballistic targets based on the computation of the inverse Radon transform of the target signatures, represented by a high resolution range profile frame acquired within an entire period of the main rotation of the target. Namely, the precession for warheads and the tumbling for decoys are taken into account. Following, the pseudo-Zernike moments of the resulting transformation are evaluated as the final feature vector for the classifier. The extracted features guarantee robustness against target's dimensions and rotation velocity, and the initial phase of the target's motion. The classification results on simulated data are shown for different polarizations of the electromagnetic radar waveform and for various operational conditions, confirming the validity of the algorithm.

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Section: Radar Cross Section Modelmentioning

confidence: 99%

Section: Hrrp Frame Based Classification Algorithm For Btsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Novel Classification Algorithm for Ballistic Target Based on HRRP Frame

Persico

Ilioudis

Clemente

et al. 2019

IEEE Trans. Aerosp. Electron. Syst.

Self Cite

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Evidence-Theoretic Reentry Target Classification Using Radar: A Fuzzy Logic Approach

et al. 2021

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This study focuses on the reentry target classification and fuses target features based on the generalized evidence theory. The features are extensively investigated, and the ballistic factor and length of the high-resolution range profile are selected. The evidence theory is advantageous for solving feature fusion, representing uncertainty, and is widely used in defense applications. However, determining the generalized basic probability assignment (GBPA) and dealing with uncertainty is a matter that requires further improvement. In this paper, we propose a new method to determine GBPA using uncertainty with time-series radar data. First, the samples of each known class are encoded as a generalized fuzzy number (GFN), and the power set comprising the frame of discernment (FOD) is calculated from the GFN and each intersection area. Subsequently, the test samples with uncertainty are encoded as triangular fuzzy numbers, reflecting the mean and standard deviation of a Kalman filter. Finally, the firing strength between the model and the input is calculated as the degree of support for the class hypothesis, which is used to determine the GBPA. The proposed algorithm is compared with the existing methods and exhibits high classification accuracy and a short classification time without leakage. In experiments with various input uncertainties, the results demonstrate that our method can effectively reflect the input uncertainty and determine the GBPA.

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