Novel Classification Algorithm for Ballistic Target Based on HRRP Frame

Abstract

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.

Research on the micromotion characteristics of a chaff cloud

“…According to the theory of the precession model of the chaff dipoles and assuming a conical angular velocity Ω con = 12 rad/s and a spin angular velocity Ω spin = 3 rad/s, we introduce Ω con and Ω spin into Formulas (8) and (10). Based on the method of multipoint synthesis (where the real target echo is obtained by superimposing the corresponding signals of each scattering point when the relative positions, scattering intensities and phases of the corresponding echo signals are different), the micro-Doppler frequency of a single chaff dipole in the chaff cloud is calculated and simulated based on the short-time Fourier transform, as shown from Figs.…”

“…Chen et al [4] proposed two basic micromotion models, including the rotation and the oscillation. Then on that basis, Deng et al [5], Li et al [6], and Persico et al [7,8] proposed the micromotion models of several kinds of radar targets, such as aircrafts, ships, and missiles, respectively.…”

Novel Parameter Estimation Method for a Ballistic Warhead with Micromotion

“…To demonstrate the effectiveness of the proposed method, we considered a conical CAD model (Fig. 4(a)), which is similar to an actual ballistic warhead [4][5][6]. The echo signals were computed using Virtual Aircraft Framework (VIRAF) software (Table 1).…”

“…However, conventional methods based on the range profile and the inverse synthetic aperture image in [1] and [2] have limited ability to discriminate them because of the similarity in shape and the overall trajectory of the warhead and the decoy. The problems of existing methods can be overcome by exploitingthe difference in their micro-Doppler (MD) effect [3][4][5][6][7][8]; the warhead is much heavier due to various devices, and thus its micromotion is dif-ferent from that of the decoy. The parameter estimation for a ballistic warhead with micromotion can be utilized to solve the problem of discrimination between warhead and decoy.…”

One‐dimensional convolutional neural networks for high‐resolution range profile recognition via adaptively feature recalibrating and automatically channel pruning

“…Compared with twodimensional synthetic aperture radarand inverse synthetic aperture radar images, which have obtained intensive attention in radar target recognition as well, HRRP has the advantages of easy acquisition and storing as well as low computation complexity. Therefore, it is widely used in recognition of a variety of radar targets, such as ballistic missiles, [1][2][3][4] ships, [5][6][7] tanks, 8,9 airplanes, [10][11][12][13][14] and so on, it has become a hotspot in the community of radar automatic target recognition (RATR).…”