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Spotlight Synthetic Aperture Radar Signal Processing Algorithms

This is a book review of 'The Micro-Doppler Effect in Radar' by V. C. Chen. Artech House, 16 Sussex Street, London, SW1V 4RW, UK. 2011. 290pp + diskette. Illustrated. £90. ISBN 978-1-60807-057-2.

'The Micro-Doppler Effect in Radar' by V.C. Chen

Inverse synthetic aperture radar (ISAR) imaging of maneuvering target plays an important role in the field of national defense and surveillance. In this case, the conventional range Doppler algorithm is unsuitable to generate a well-focused ISAR image due to the time-varying property of the Doppler frequency during the integrated imaging interval. Furthermore, the dihedral or trihedral components of the imaging target or the phenomena of migration through resolution cell for the target's complex motion will induce the time-varying performance of the amplitude for the received signal. In this paper, the received signal in a range bin is depicted as multicomponent quadratic frequency-modulated signal with time-varying amplitude, and the generalized cubic phase function is used to estimate the parameters of it. Associated with the range instantaneous Doppler technique, the images quality can be enhanced distinctly compared with the conventional constant amplitude signal model. Results of simulated and read data demonstrate the effectiveness of the novel algorithm proposed in this paper.

ISAR Imaging of Maneuvering Target Based on the Quadratic Frequency Modulated Signal Model With Time-Varying Amplitude

Although the deep convolutional neural network (DCNN) has been successfully applied to automatic target recognition (ATR) of ground vehicles based on synthetic aperture radar (SAR), most of the available techniques are not suitable for inverse synthetic aperture radar (ISAR) because they cannot tackle the inherent unknown deformation (e.g., translation, scaling, and rotation) among the training and test samples. To achieve robust polarimetric-ISAR (Pol-ISAR) ATR, this paper proposes the spatial transformer-multi-channel-deep convolutional neural network, i.e., ST-MC-DCNN. In this structure, we adopt the double-layer spatial transformer network (STN) module to adjust the image deformation of each polarimetric channel and then perform a robust hierarchical feature extraction by MC-DCNN. Finally, we carry out feature fusion in the concatenation layer and output the recognition result by the softmax classifier. The proposed network is end-to-end trainable and could learn the optimal deformation parameters automatically from training samples. For the fully Pol-ISAR image database generated from electromagnetic (EM) echoes of four satellites, the proposed structure achieves higher recognition accuracy than traditional DCNN and MC-DCNN. Additionally, it has shown robustness to image scaling, rotation, and combined deformation.

Robust Pol-ISAR Target Recognition Based on ST-MC-DCNN

The micro-Doppler phenomenon in the echo signal received from a ballistic target (BT) with micro-motion is commonly used to discriminate BTs such as warheads and decoys. The joint time-frequency (JTF) analysis of the echo signal has been considered as useful two-dimensional (2-D) information in BT discrimination, which generally requires a framework based on the processing of the 2-D JTF image with various conventional feature extraction techniques. However, these techniques are inefficient for time-critical BT discrimination task due to the complicated 2-D image processing. In this paper, we propose new echo signal models to formulate the fundamental difference between the micro-motions of warheads and decoys, leading to a novel BT discrimination framework via new feature extraction paradigm and multi-aspect fusion concept. The most attractive attribute of this framework is that it can provide substantial savings with regard to computational resources as well as robustness to noise. The experimental results illustrate that the proposed discrimination scheme shows considerable promise for application in real-time BT discrimination.

Efficient Discrimination of Ballistic Targets With Micromotions

This paper proposes an efficient method to classify inverse synthetic aperture radar (ISAR) images. The proposed method achieves invariance to translation and rotation of ISAR images by using two-dimensional (2D) Fourier transform (FT) of ISAR images, polar mapping of the 2D FT image, and a simple nearest-neighbor classifier. In simulations using ISAR images measured in a compact range, the proposed method yielded high classification ratios with small-sized data regardless of the location of the rotation center, whereas the existing method was very sensitive to the location of it.

Efficient classification of ISAR images using 2d fourier transform and polar mapping

Micro-Doppler (MD) caused by the motion of the ballistic missile can contribute to successful recognition of the ballistic missile. Considering the real observation scenario. This paper proposes a method to derive the MD image of the ballistic missile by applying the range-Doppler algorithm (RDA) based on the real ∞ight scenario and analyzes the factor for the real-time MD imaging. Simulation results using the ∞ight trajectory constructed using the real target parameter demonstrate that we need a new cost function for phase adjustment and a new method for range alignment. In addition, matched-flltering needs to be performed in the baseband, and a su-cient PRF is required to prevent discontinuity of the MD image. Dechirping of MD and flltering of the random movement are also needed for a clear MD image. Among the various weapons used in the modern battlefleld, the ballistic missile in∞icts the biggest threat due to its high maneuvering speed and low radar cross section (RCS), and thus defending against the ballistic missile is a major issue. Recently, a theory has been developed to explain micro-Doppler (MD) efiect caused by the micro motion of the target and applied for radar target recognition purpose (1,2). In the case of the ballistic missile, three motion components | spinning, conning and nutation | cause MD, and they can be utilized for target recognition in combination with the motion parameter (1,3,4). However, very little research has been reported on its application to the real ∞ight scenario and the factor that needs to be considered for the real-time MD imaging. In this paper, considering the real observation scenario by a radar, we propose a method to extract an MD signature of a ballistic missile engaged in the real ∞ight scenario by applying the range-Doppler algorithm (RDA), which is generally used to form the inverse synthetic aperture radar (ISAR) image (5{8), and analyze various factors for the real-time high quality MD image. For this purpose, we constructed the ∞ight trajectory by using the real motion parameters of a 500km range scud missile conducted a translation motion compensation (TMC). Then, the time-varying MD image was formed by applying the time-frequency transform (TFT). Various simulations were performed by using the obtained MD image to study the requirement for the real-time MD imaging. Simulation results obtained by using a target composed of the point scatter demonstrate that the MD signature can be successfully constructed by using the range-Doppler algorithm. However, a new method for TMC is required for real-time high-quality MD imaging. In addition, matched-flltering (MF) in the baseband is required to form a focused image, and a su-cient PRF is needed to remove discontinuity. The re∞ected signal needs to be dechirped to reduce the required PRF, and a fllter needs to be designed to remove the random movement.

/pdf/micro-doppler-extraction-and-analysis-of-the-ballistic-3sdz1skta5.pdf

Micro-Doppler Extraction and Analysis of the Ballistic Missile Using RDA Based on the Real Flight Scenario

This letter presents a novel wideband miniaturized-element frequency selective surface (MEFSS). The simulation and measurement results show that the bandwidth of the proposed MEFSS is remarkably enhanced compared to that of an original second-order MEFSS while its size and total thickness are still small. A parametric study is also conducted to understand the operating mechanism of the proposed structure. The phenomenon observed in the parametric study is explained with an equivalent circuit model.

/pdf/a-novel-wideband-miniaturized-element-frequency-selective-2uf1h9ekow.pdf

A Novel Wideband Miniaturized-Element Frequency Selective Surface

The range proflle (RP) and the inverse synthetic aperture radar (ISAR) image are the useful radar signatures for classifying unknown targets because they can be used regardless of day- night and weather conditions. Since classiflcation that uses RP and ISAR is heavily dependent on ∞ight conditions, however, much more study is required on this topic. This paper proposes an e-cient method of classifying targets by using a classifler-level fusion of RP and ISAR as well as a scenario-based construction method of the training database. Simulation results using the flve targets composed of point scatterers prove that the proposed method yields high classiflcation results when the targets are ∞ying in a variety of directions at both short and long ranges.

/pdf/classification-of-targets-improved-by-fusion-of-the-range-342774a7r2.pdf

Classification of Targets Improved by Fusion of the Range Profile and the Inverse Synthetic Aperture Radar Image

This paper presents the classification result of various features obtained from the micro-Doppler(MD) signature of drones and birds considering the real flight scenarios. Classification results using the rotating blade and the flapping wing made of the plate and the ellipsoid demonstrate that the MD bandwidth is the most efficient feature.

Joo-Ho Jung

Papers

Efficient classification of ISAR images using 2d fourier transform and polar mapping

Micro-Doppler Extraction and Analysis of the Ballistic Missile Using RDA Based on the Real Flight Scenario

A Novel Wideband Miniaturized-Element Frequency Selective Surface

Classification of Targets Improved by Fusion of the Range Profile and the Inverse Synthetic Aperture Radar Image

In-depth Analysis of the Micro-Doppler Features to Discriminate Drones and Birds