3D interferometric ISAR imaging of noncooperative targets Martorella, Marco; Stagliano, Daniele; Salvetti, Federica ...
IEEE transactions on aerospace and electronic systems,
2014-October, 2014-10-00, 20141001, Letnik:
50, Številka:
4
Journal Article
Recenzirano
Inverse synthetic aperture radar (ISAR) images are frequently used in target classification and recognition applications. Nevertheless, the interpretation of ISAR images remains problematic for ...several reasons. One of these is the fact that the image plane cannot be defined by the user but instead depends on the target's own motions and on its relative position with respect to the radar. In order to overcome the problem of interpreting two-dimensional (2D) ISAR images, a method for three-dimensional (3D) reconstruction of moving targets is presented. This method is based on the use of a dual interferometric ISAR system. The interferometric phases measured from two orthogonal baselines are used to jointly estimate the target's effective rotation vector and the heights of the scattering centers with respect to the image plane. The scattering center extraction from the ISAR image is performed by applying a multichannel CLEAN technique. Finally, a 3D image of the moving target is reconstructed from the 3D spatial coordinates of the scattering centers. The effectiveness and robustness of the proposed algorithm is first proven theoretically and then tested against several radar-target scenarios as well as in the presence of noise.
Ground moving target imaging finds its main applications in both military and homeland security applications, with examples in operations of intelligence, surveillance and reconnaissance (ISR) as ...well as border surveillance. When such an operation is performed from the air looking down towards the ground, the clutter return may be comparable or even stronger than the target's, making the latter hard to be detected and imaged. In order to solve this problem, multichannel radar systems are used that are able to remove the ground clutter and effectively detect and image moving targets. In this feature paper, the latest findings in the area of Ground Moving Target Imaging are revisited that see the joint application of Space-Time Adaptive Processing and Inverse Synthetic Aperture Radar Imaging. The theoretical aspects analysed in this paper are supported by practical evidence and followed by application-oriented discussions.
Most of the existing Non-Cooperative Target Recognition (NCTR) systems follow the “closed world” assumption, i.e., they only work with what was previously observed. Nevertheless, the real world is ...relatively “open” in the sense that the knowledge of the environment is incomplete. Therefore, unknown targets can feed the recognition system at any time while it is operational. Addressing this issue, the Openmax classifier has been recently proposed in the optical domain to make convolutional neural networks (CNN) able to reject unknown targets. There are some fundamental limitations in the Openmax classifier that can end up with two potential errors: (1) rejecting a known target and (2) classifying an unknown target. In this paper, we propose a new classifier to increase the robustness and accuracy. The proposed classifier, which is inspired by the limitations of the Openmax classifier, is based on proportional similarity between the test image and different training classes. We evaluate our method by radar images of man-made targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Moreover, a more in-depth discussion on the Openmax hyper-parameters and a detailed description of the Openmax functioning are given.
The atmosphere affects the propagation of radar signals by provoking unwanted signal phase changes. In interferometric applications, such as coherent change detection and displacement measurements, ...this effect may significantly degrade the system performances. Moreover, atmosphere-induced phase changes are both time and space variants, and therefore, they are not easy to be removed. This article proposes a novel method to remove atmospheric effects by using a parametric model of the refractive index, which is derived as an extension of the International Telecommunication Union-Radiocommunication model. The proposed algorithm has been tested on real data acquired by using a ground-based synthetic aperture radar system in conjunction with data collected by a weather station. Data have been acquired continuously for three consecutive days, approximatively every 5 min. Results have shown how the proposed method can effectively remove atmospheric effects and restore the signal phase.
Deep-learning-based synthetic aperture radar automatic target recognition (SAR-ATR) plays a significant role in the military and civilian fields. However, data limitation and large computational cost ...are still severe challenges in the actual application of SAR-ATR. To improve the performance of the convolutional neural network (CNN) model with limited data samples in SAR-ATR, this article proposes a novel multidomain feature subspace fusion representation learning method, i.e., a lightweight cascaded multidomain attention network, namely, LW-CMDANet. First, we design a four-layer CNN model to perform hierarchical feature representation learning via the hinge loss function, which can efficiently alleviate the overfitting problem of the CNN model by a nongreedy training style with a small dataset. Then, a cascaded multidomain attention module, based on discrete cosine transform and discrete wavelet transform, is embedded into the previous CNN to further complete the class-specific feature extraction from both the frequency and wavelet transform domains of the input feature maps. Thus, the multidomain attention can enhance the feature extraction ability of previous nongreedy learning manner, to effectively improve the recognition accuracy of the CNN model. Experimental results on small SAR datasets show that our proposed method can achieve better or competitive performance than that of many current existing state-of-the-art methods in terms of recognition accuracy and computational cost.
Staggered synthetic aperture radar (SAR), which operates with variable pulse repetition interval (PRI), staggers blind areas to solve the blind range problem caused by constant PRI in conventional ...high-resolution wide-swath SAR imaging. The PRI variation strategy determines the blind area distribution, and thus has a significant influence on the imaging performance in staggered mode. Generally, the existing strategies based on linear PRI variation can control the blind areas in a straightforward way, which has achieved impressive results. However, the linearity of the PRI variation imposes regularity or even periodicity on the locations of the blind areas, which limits the distribution of the blind areas. The imaging performance has the potential to be further improved by introducing much more irregularity into the PRI sequences. To this end, this article proposes an optimized nonlinear PRI variation strategy for staggered SAR mode. First, a novel objective function is defined that quantitatively measures the uniformity of the blind area distribution along the slant range and the discontinuity of the blind area distribution along the azimuth. Subsequently, the optimum nonlinear PRI variation strategy is found using an optimization problem and the proposed objective function. A knowledge-guided genetic algorithm is proposed to solve the optimization problem. Comparisons with the existing linear variation strategies show that the proposed strategy can provide a superior imaging performance after reconstruction with a lower objective function value. Simulations and experiments on raw data generated in staggered SAR mode are performed to verify the effectiveness of the optimized nonlinear PRI variation strategy.
The fast and uncontrolled rise of the space objects population is threatening the safety of space assets. At the moment, space awareness solutions are among the most calling research topic. In fact, ...it is vital to persistently observe and characterize resident space objects. Instrumental highlights for their characterization are doubtlessly their size and rotational period. The Inverse Radon Transform (IRT) has been demonstrated to be an effective method for this task. The analysis presented in this paper has the aim to compare various approaches relying on IRT for the estimation of the object's rotation period. Specifically, the comparison is made on the basis of simulated and experimental data.
Integrating an automatic target recognition (ATR) system into real-world applications presents a challenge as it may frequently encounter new samples from unseen classes. To overcome this challenge, ...it is necessary to adopt incremental learning, which enables the continuous acquisition of new knowledge while retaining previous knowledge. This article introduces a novel, multipurpose interpretability metric for ATR systems that employs synthetic aperture radar images. The metric leverages the local interpretable model-agnostic explanation algorithm, enhancing human decision-making by providing a secondary measure alongside the conventional classification score. In addition, the proposed metric is employed to analyze the robustness of convolutional neural networks by examining the impact of target features and irrelevant background correlations on recognition results. Finally, we demonstrate the effectiveness of the proposed metric in the context of incremental learning. By utilizing the proposed interpretability metric, we select exemplars in an incremental learning scenario, resulting in improved performance and showcasing the application potential of our proposed methodology. The network is fine-tuned sequentially with unknown samples recognized by the Openmax classifier and exemplars from the old known classes, which are selected based on the proposed interpretability metric. The effectiveness of this approach is demonstrated using the publicly available MSTAR dataset.
In this paper, we show the capabilities of a new maritime control system based on the processing of COSMO-SkyMed Synthetic Aperture Radar (SAR) images. This system aims at fast detection of ships ...that may be responsible for illegal oil dumping. In particular, a novel detection algorithm based on the joint use of the significance parameter, wavelet correlator and a two-dimensional Constant False Alarm Rate (2D-CFAR) is designed. Results show the effectiveness of such algorithms, which can be used by the maritime authorities to have a faster although still reliable response. The proposed algorithm, together with the short revisit time of the COSMO-SkyMed constellation, can help with tracking the scenario evolution from one acquisition to the next.
Long baseline bistatic radar systems herald enhanced sensitivity and metric accuracy for space objects in geosynchronous orbits and beyond. Radio telescopes are ideal participants in such a system; ...in particular, they often feature large apertures with low‐noise temperatures and have stable, synchronised clocks. Pairing radio telescopes with high‐power radars creates new methodologies for Space Domain Awareness. This paper describes long baseline bistatic measurements using the Millstone Hill Radar in the USA, the Tracking and Imaging Radar in Germany, multiple receivers of the enhanced multi‐element remotely linked interferometer network array in the United Kingdom, and the Westerbork Synthesis Radio Telescope in the Netherlands. The authors, a Research Task Group formed by the NATO Science and Technology Organisation Sensors and Electronic Technology Panel (SET‐293), performed novel bistatic and monostatic radar imaging experiments with real on‐orbit tumbling rocket bodies. These experiments on tumbling objects at near‐geosynchronous orbits highlight successful demonstrations of advanced bistatic Doppler characterisation across diverse imaging geometries. Specialised Doppler processing on tumbling targets, such as the Doppler superpulse algorithm, enables high‐fidelity rotation period estimation and determination of minimum target size.
This paper describes long baseline bistatic radar measurements using the Millstone Hill Radar (MHR) in the USA, the Tracking and Imaging Radar (TIRA) in Germany, multiple receivers of the e‐MERLIN array in the United Kingdom, and the Westerbork Synthesis Radio Telescope (WSRT) in the Netherlands. The authors, a Research Task Group (RTG) formed by the NATO Science and Technology Organisation (STO) Sensors and Electronic Technology Panel (SET‐293), performed novel bistatic and monostatic radar imaging experiments with real on‐orbit tumbling rocket bodies. These experiments on tumbling objects at near‐GEO orbits demonstrate bistatic Doppler characterisation using the Doppler superpulse algorithm, which enables high‐fidelity rotation period estimation and determination of minimum target size across diverse imaging geometries.
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