Search

Conventional semi-active laser guidance takes advantage of the laser designator to illuminate the stable and uniform laser spot on target precisely. The seeker collects the reflected light by a quadrant detector and outputs the relative position information to guide the missile to the illuminating laser spot. However, the designation and guidance accuracy could be jeopardized by the randomly drifting of laser spot caused by the instability of designation platform and air turbulence. In this work, ghost imaging technique is adapted to a quadrant detector semi-active seeker by utilizing structured illumination on the target. With a series of structured illumination masks, the signals from the quadrant detector are multiplexed to perform calculation of the target relative position as well as image reconstruction of the illuminated area simultaneously. Automatic target recognition methods could be further applied to the reconstructed image to calculate the designating error and correct the guidance. The results of simulation and experiment demonstrate that the proposed method could improve the guidance accuracy in many circumstances which would lead to attacking deviation if conventional semi-active laser guidance is used.

Underwater target recognition is one core technology of underwater unmanned detection. To improve the accuracy of underwater automatic target recognition, a sonar image recognition method based on convolutional neural network was proposed and the underwater target recognition model was established according to the characteristics of sonar images. Firstly, the sonar image was segmented and clipped with a saliency detection method to reduce the dimension of input data, and to reduce the interference of image background to the feature extraction process. Secondly, by using stacked convolutional layers and pooling layers, the high-level semantic information of the target was automatically learned from the input sonar image, to avoid damaging the effective information caused by extracting image features manually. Finally, the spatial pyramid pooling method was used to extract the multi-scale information from the sonar feature maps, which was to make up for the lack of detailed information of sonar images and solve the problem caused by the inconsistent size of input images. On the collected sonar image dataset, the experimental results show that the target recognition accuracy of the present method can recognize underwater targets more accurately and efficiently than the conventional convolutional neural networks.

Existing Terahertz radar using CSAR mode has the problem of unsatisfactory imaging quality. The target and the noise are mixed in the full-aperture imaging result, which is difficult to use for automatic target recognition; and its sub-aperture-based imaging method not only does not completely solve this problem, but also brings about the problems of generating fake scattering points and the dependence on manual debugging. In view of the shortcomings of existing sub-aperture-based imaging methods, a Terahertz radar’s high-quality imaging method based on the sub-aperture is proposed by us. Among our research, 4 new methods, namely the sub-aperture division method which divides overlapping sub-apertures, the sub-aperture boundary point selection method, two new methods of selecting the sub-aperture boundary point, are used. The imaging method proposed by us further improves the signal-to-noise ratio of the target in the imaging result, and can be applied to targets with more complex shapes. Moreover, our imaging method solves the problems of generating fake scattering points and the dependence on manual debugging. After field test, our imaging method has better imaging quality than existing methods, and it is less dependent on manual debugging.

We give an overview of the EDA CAT B R&D project ""Signal Processing for Enhanced Radar Imaging"" (SPERI). In this project, the benefits of applying two super-resolution methods Super Spatially Variant Apodization (SSVA) and Compressed Sensing (CS) to two-dimensional Inverse Synthetic Aperture Radar (ISAR) images of airborne radar targets were investigated with respect to the improvements in automatic target identification rates. The algorithms have been tested over a database of more than 1200 real radar images.

A novel multiview inverse synthetic aperture radar (ISAR) imaging method is proposed to simulate high-resolution and identifiable ISAR image of complex targets by handling large-angle and wide-bandwidth scattering data. The scattering data are simulated with the shooting and bouncing ray (SBR) method. The bidirectional ray-tracing algorithm is developed to reduce the computation time. Simulation results indicate that the improved method is efficient and reliable to calculate electromagnetic (EM) scattering of electrically large targets. To implement the multiview ISAR imaging method after data simulation, we divide the large-angle and wide-bandwidth scattering data into subaperture data and conduct ISAR image processing for each subaperture datum locally. Transforming each subaperture ISAR image to the global coordinate system and summing them together will produce the high-resolution ISAR image that is meaningful for the database set up for synthetic aperture radar automatic target recognition (SAR ATR). The final simulation ISAR images further validate the great performance of our scattering calculation algorithm and ISAR imaging method.

Most research regarding passive radar exploiting `illuminators of opportunity', such as FM radio, has focused on detecting and tracking targets. This study explores adding automatic target recognition (ATR) capabilities to such systems. The ATR algorithms described here use the radar cross-section (RCS) of potential targets, collected over a short period of time. The received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. One proposed algorithm uses a coordinated flight model to estimate aircraft orientations, while a more sophisticated algorithm uses an extended Kalman filter to estimate the target orientations along with measures of uncertainty in those estimates. In both cases, the orientations are estimated using velocity measurements obtained from a tracking algorithm. The radar return of each aircraft in the target library is simulated as though each is executing the same manoeuvre as the target detected by the system. To improve the robustness of the result, the more sophisticated algorithm jointly optimises over feasible orientation profiles and target types via dynamic programming.

The traditional target recognition method for the remote sensing image is difficult to accurately identify the specified targets from the massive remote sensing image data. Based on the theory of multitemporal recognition, an automatic target recognition method for the remote sensing image is proposed in this article. The proposed recognition method includes four modules: automatic segmentation of multitemporal remote sensing image, automatic target extraction of multitemporal remote sensing image, automatic processing of multitemporal remote sensing image, and automatic recognition of multitemporal remote sensing image. The automatic segmentation of the image target is introduced. The effectiveness of the segmentation technology is verified through the kernel function bandwidth algorithm. Linear feature extraction is used to extract the segmented image. The image extraction processing is described, which includes image profile analysis, image preprocessing, image feature analysis, the region of interest localization, image enhancement processing, recognition processing, and result output. According to the theory of pattern recognition, three different feature recognition images are given, which are partial separable recognition, weakly separable recognition, and fully separable recognition, and then, a new image recognition method is designed. To verify the practical application effect of the recognition method, the proposed method is compared with the traditional recognition method. Experimental results show that the proposed method can accurately identify the specified objects from the massive remote sensing image data and has a high potential for development. This article has an important guiding significance for image recognition.

Synthetic Aperture Radar (SAR) imaging systems operate by emitting radar signals from a moving object, such as a satellite, towards the target of interest. Reflected radar echoes are received and later used by image formation algorithms to form a SAR image. There is great interest in using SAR images in computer vision tasks such as classification or automatic target recognition. Today, however, SAR applications consist of multiple operations: image formation followed by image processing. In this work, we train a deep neural network that performs both the image formation and image processing tasks, integrating the SAR processing pipeline. Results show that our integrated pipeline can output accurately classified SAR imagery with image quality comparable to those formed using a traditional algorithm, showing that fully neural network based SAR processing pipeline is feasible.

Synthetic aperture radar (SAR) is an advanced microwave imaging system of great importance. The recognition of real-world targets from SAR images, i.e., automatic target recognition (ATR), is an attractive but challenging issue. The majority of existing SAR ATR methods are designed for single-view SAR images. However, multiview SAR images contain more abundant classification information than single-view SAR images, which benefits automatic target classification and recognition. This paper proposes an end-to-end deep feature extraction and fusion network (FEF-Net) that can effectively exploit recognition information from multiview SAR images and can boost the target recognition performance. The proposed FEF-Net is based on a multiple-input network structure with some distinct and useful learning modules, such as deformable convolution and squeeze-and-excitation (SE). Multiview recognition information can be effectively extracted and fused with these modules. Therefore, excellent multiview SAR target recognition performance can be achieved by the proposed FEF-Net. The superiority of the proposed FEF-Net was validated based on experiments with the moving and stationary target acquisition and recognition (MSTAR) dataset.

Automatic Target Recognition (ATR) is an important research area in the field of radar information processing. Because the deep Convolution Neural Network (CNN) does not need to carry out feature engineering and the performance of image classification is superior, it attracts more and more attention in the field of radar automatic target recognition. The application of CNN to radar image processing is reviewed in this paper. Firstly, the related knowledges including the characteristics of the radar image is introduced, and the limitations of traditional radar automatic target recognition methods are pointed out. The principle, composition and development of CNN the field of computer vision are introduced. Then, the research status of CNN in radar automatic target recognition is provided. The detection and recognition method of SAR image are presented in detail. Then, the challenge of radar automatic target recognition is analyzed. Finally, the new theory and model of convolution neural network, the new imaging technology of radar and the application to complex environments in the future are prospected.

In the widespread field of underwater robotics applications, the demand for increasingly intelligent vehicles is leading to the development of Autonomous Underwater Vehicles (AUVs) with the capability of understanding and engaging the surrounding environment. Consequently, the automatic recognition of targets is becoming one of the most investigated topics and Deep Learning-based strategies have shown astonishing results. In the context of this work, two different neural network architectures, based on the Single Shot Multibox Detector (SSD) and on the Faster Region-based Convolutional Neural Network (Faster R-CNN), have been trained and validated, respectively, on optical and acoustic datasets. The models have been trained with the images acquired by FeelHippo AUV during the European Robotics League (ERL) competition, which took place in La Spezia, Italy, in July 2018. The proposed ATR strategy has then been validated with FeelHippo AUV in an on-board postprocessing stage by exploiting the images provided by both a 2D Forward Looking Sonar (FLS) as well as an IP camera mounted on-board on the vehicle., https://youtu.be/6e_Ks924daQ

Military radar can not only provide kinematic information (position, speed, and course) of land, sea, and air targets during day and night in all weather conditions, but also provides information about the type of target using micro Doppler properties. The micro -Doppler properties of a target are determined by the moving parts on the target body. The number, location, and type of motion of these parts are specific for a given target type and consequently the related micro -Doppler signature can be exploited for classification. Analysis of the micro -Doppler signature may provide detailed properties of rotating parts, such as the rotation rate, number of blades, and blade length. However, for operators and/or image analysts, the interpretation and understanding of radar micro -Doppler spectrograms is much more difficult and time-consuming than the analysis of optical images because of the different nature of the radar imaging principle and target scattering mechanisms. Consequently, there is a need for automatic target recognition (ATR) in radar using micro -Doppler spectrograms.

Synthetic aperture sonar (SAS) provides high-resolution acoustic imaging by processing coherently the backscattered acoustic signal recorded over consecutive pings. Traditionally, object detection and classification tasks rely on high-resolution seafloor mapping achieved with widebeam, broadband SAS systems. However, aspect- or frequency-specific information is crucial for improving the performance of automatic target recognition algorithms. For example, low frequencies can be partly transmitted through objects or penetrate the seafloor providing information about internal structure and buried objects, while multiple views provide information about the object's shape and dimensions. Sub-band and limited-view processing, though, degrades the SAS resolution. In this paper, SAS imaging is formulated as an l1-norm regularized least-squares optimization problem which improves the resolution by promoting a parsimonious representation of the data. The optimization problem is solved in a distributed and computationally efficient way with an algorithm based on the alternating direction method of multipliers. The resulting SAS image is the consensus outcome of collaborative filtering of the data from each ping. The potential of the proposed method for high-resolution, narrowband, and limited-aspect SAS imaging is demonstrated with simulated and experimental data.

Using deep convolutional neural networks to achieve automatic target recognition (ATR) is effective but it will bring heavy computation burden. Here we propose an optronic convolutional neural network (OPCNN) to realize ATR in optics. By using OPCNN, the computation cost is dramatically reduced and good performance of recognition accuracy is obtained simultaneously. Experimental results on Moving and Stationary Target Acquisition and Recognition dataset demonstrate the feasibility of our proposed OPCNN architecture.

The key factor in obtaining flight vehicle guidance information is the automatic recognition of strapdown images. When using strap-down image guidance, it is more difficult to obtain guidance information due to the influence of environment and other factors on the information collection. Aiming at the fact that deep learning algorithms cannot be directly applied to the target recognition of strapdown images, a new target recognition framework is proposed. The framework extracts features from target template images and strapdown images, and uses the extracted feature maps as the input of the deep neural network, so that the improved target recognition framework can be directly applied to the strapdown environment. Experimental results show that the proposed framework can effectively recognize strapdown images, improve recognition accuracy, and achieve precise guidance.

Synthetic aperture radar (SAR) is an imaging modality used for a variety of military and civilian tasks, many of which could benefit greatly from computer automation. The increase in machine learning-based computer vision techniques in recent years has created a number of helpful methods to advance this goal, but most of these algorithms are designed for electro-optical RGB imagery. Applying these off-the-shelf algorithms to magnitude only SAR imagery has shown promise in tasks such as automatic target recognition (ATR). However, very few algorithms exploit the complex-valued nature of radar data. We present a survey of several complex neural network techniques as applied to a SAR data set consisting of military targets. We comment on the merits of each approach and demonstrate the accuracy of each technique when 1) training data are limited, and 2) when the training and testing data exhibit a domain mismatch.

Automatic Target Detection (ATD) and Recognition (ATR) are critical for video analysis and image understanding for many military and commercial applications deployed on satellites and UAV platforms. Infrared (IR) sensors can be used to detect targets during day and night time but there are few effective ATR algorithms that can exploit these sensors. Several years ago, Defense Systems Information Analysis Center (DSIAC) released an ATR Algorithm Development Image Database containing a large collection of mid-wave infrared (MWIR) imagery with multiple military and civilian vehicles as labelled targets. With the DSIAC database, we have developed AI models, which combine layers of the open-source You Only Look Once (YOLOv2) detection model with customized Convolutional Neural Network (CNN) feature extraction layers. The CNN layers are trained to extract the key features of the vehicle targets from the IR images. YOLOv2 provides target detection, classification, and localization. We have trained and tested the CNN YOLOv2 models with four different military and civilian vehicles (T72, ZSU -23-4, SUV, pickup truck) at distances ranging from 2,000m to 5,000m. Our ATR results with IR datasets show high mean average precision (mAP) between 97.25%-99.5% for day and night time images at distances of 2,000m to 5,000m. That is, we can both reliably detect and recognize different targets with only a few missed detections, and without a falsely recognized target (e.g., mistakenly classifying a civilian vehicle as a military vehicle) from as far as 5,000m. This work represents significant progress in being able to perform ATR at all times (day and night).

Image registration is the technique of aligning multiple images that are captured from diverse sources, dissimilar viewpoints, or different times. In general, the image registration algorithms construct a complete image by mapping the source images on the target image by rotation or translation. This mapping is executed by finding the relative transformation. There are several steps to obtain the desired goal. One of the most important steps is feature matching, as based on the obtained result of matched features, the relative transformation is found. In this paper, a comparison among multiple feature matching algorithms has been presented to provide a better understanding of the image registration technique. Additionally, the outcomes after applying those algorithms onto the images have also been presented to show which of them perform better in given circumstances. This can be useful in various fields of computer vision such as remote sensing, automatic target recognition, and medical imaging.

In recent image processing, beyond the object recognition problem, deep learning has been used in various aspects such as object detection, semantic segmentation. In addition, classic technique-based detection has been performed variously. These technologies are applied in various systems such as factory automation systems, automatic target recognition (ATR) systems, autonomous driving systems, etc. Object detection is performed in various categories such as people, vehicles and animals, etc. And it is operated for various situations which contain different object size, image size, distance range from near to remote, changeable environment, etc. For the situation analysis, indicators need to be used appropriately. And when researchers make some algorithm for object detection, if there are no any evaluation indicators, the algorithm can’t be demonstrated. So, it is important to know about performance indicators of object detection. Various indicators are used in object detection. As a result, this paper introduces performance indicators of object detection. The main purpose of the survey is that researchers find the proper performance indicator for object detection. And It can help to compare the detection result with a different algorithm result, exactly and effectively.

Automatic target recognition (ATR) in the sonar image is usually divided into two steps: The localization part aims to locate the region which most likely contains the targets. The classification part uses the information from the located region to determine the targets' class. Classical methods complete the ATR task with two steps by different techniques. However, some papers have shown that CNN can realize the ATR task in one-step given sufficient training data. In this paper, we try to use a one-step CNN to automatically recognize (locate and classify) target in sonar images. For the problem of insufficient sonar image data which easily causes over-fitting, we try to find the common ground of CNN in extracting features from the optical image and sonar image. We use style-transfer method to help CNNs extract shape features from optical image data, thus the pre-trained CNN model can be used to improve the CNNs' ability for ATR tasks in the sonar image.

In recent years, seabed inspection has become one of the most sought-after tasks for Autonomous Underwater Vehicles (AUVs) in various applications. Forward-Looking Sonars (FLS) are commonly favored over optical cameras, which are not-negligibly affected by environmental conditions, to carry out inspection and exploration tasks. Indeed, sonars are not influenced by illumination conditions and can provide high-range data at the cost of lower-resolution images compared to optical sensors. However, due to the lack of features, sonar images are often hard to interpret by using conventional image processing techniques, leading to the necessity of human operators analyzing thousands of images acquired during the AUV mission to identify the potential targets of interest. This paper reports the development of an Automatic Target Recognition (ATR) methodology to identify and localize potential targets in FLS imagery, which could help human operators in this challenging, demanding task. The Mask R-CNN (Region-based Convolutional Neural Network), which constitutes the core of the proposed solution, has been trained with a dataset acquired in May 2019 at the Naval Support and Experimentation Centre (Centro di Supporto e Sperimentazione Navale - CSSN) basin, in La Spezia (Italy). The ATR strategy was then successfully validated online in the same site in October 2019, where the targets were replaced and relocated on the seabed.

Real-time embedded vision tasks require extraction of complex geometric and morphological features from the raw 3D point cloud acquired using range scanning systems like lidar, radar etc. and depth cameras. Such applications are found in autonomous navigation, surveying, 3D mapping and localization tasks such as automatic target recognition (ATR). Typically, a dataset acquired during surveying by remote sensing lidar scanners, known as point cloud, is (1) huge in size and requires a big chunk of memory for processing at a single instance and, (2) experiences missing information due to rapid change in orientation of the sensor while scanning. In our work, we are addressing both the issues combinedly by proposing an online point cloud super-resolution approach for translating a low dimensional point cloud to a high dimensional dense point cloud by learning dictionaries in the low-dimensional subspace. We are presenting our approach for an urban road scenario by reconstructing dense point clouds of 3D objects and comparing results based on PSNR and Hausdorff distance.

A convolutional neutral network has been proposed to identify different classes of targets in this paper. The database of CNN can be acquired by using SBR-based fast image scheme. The numerical results show the efficiency and accuracy for the construction of database. Moreover, it can achieve accuracy of 95% for the 3-class task.

International audience; This paper is devoted to the study of an automatic target recognition method to classify Inverse Synthetic Aperture Radar (ISAR) images of moving targets. 2-D ISAR imagery generation allows to obtain a pertinent representation of the moving target reflectivity distribution employing radar imaging system. The proposed target recognition technique is based on a combined the polar representation with shape matrix description applied on ISAR image. These descriptors (features) are used to achieve the recognition task method using Neural Network and Support Vector Machine. Several simulations are provided to validate the performances of the proposed method for the automatic aircraft target recognition.

The capability to perform Automatic Target Recognition (ATR) from SAR images has great importance for both civilian and military applications. However, this task becomes challenging when the quality and quantity of target information is not sufficient to reliably discriminate the targets. This is particularly important when dealing with marine targets, where features such as scattering intensities and shapes are common to many different targets. This paper investigates the possibility to enhance classification capabilities of marine targets in SAR images by exploiting the micro-motion information. This characterizing source of information, is extracted by applying Doppler sub-apertures and pixel tracking on SAR images containing the target of interest. The proposed approach is validated on real COSMO-SkyMed SAR data demonstrating the effectiveness to discriminate ships through their unique Doppler fingerprint.

Surveillance applications demand round the clock monitoring of regions in constrained illumination conditions. Thermal infrared cameras which capture the heat emitted by the objects present in the scene appear as a suitable sensor technology for such applications. However, developing of AI techniques for automatic detection of targets for monitoring applications is challenging due to high variability of targets within a class, variations in pose of targets, widely varying environmental conditions, etc. This paper presents a real-time framework to detect and classify targets in a forest landscape. The system comprises of two main stages: the moving target detection and detected target classification. For the first stage, Mixture of Gaussians (MoG) background subtraction is used for detection of Region of Interest (ROI) from individual frames of the IR video sequence. For the second stage, a pre-trained Deep Convolutional Neural Network with additional custom layers has been used for the feature extraction and classification. A challenging thermal dataset created by using both experimentally generated thermal infrared images and from publically available FLIR Thermal Dataset. This dataset is used for training and validating the proposed deep learning framework. The model demonstrated a preliminary testing accuracy of 95%. The real-time deployment of the framework is done on embedded platform having an 8-core ARM v8.2 64-bit CPU and 512-core Volta GPU with Tensor Cores. The moving target detection and recognition framework achieved a frame rate of approximately 23 fps on this embedded computing platform, making it suitable for deployment in resource constrained environments.

Automatic Target Recognition (ATR) based on Synthetic Aperture Radar (SAR) images plays a key role in military applications. However, there are difficulties with this traditional recognition method...

In the field of automatic target recognition and tracking, long-term tracking for aerial infrared target has been recently seen with great interest. Although deep trackers and correlation filtering trackers offer competitive results on performance, the problems of deformation, abrupt motion, heavy occlusion, and out of view still remain unsolved. In addition, since this paper focus on infrared images, it is also important to consider that infrared images have a significant drawbacks, such as low resolution, low contrast, and lack of textures. In this paper, we adopt correlation filtering trackers and deep learning detection method to achieve accurate tracking results. Our tracking system composed of three parts: the DTB correlation filtering tracker (DTB-CF), a better regression model to discriminate the target from the background with adjustable Gaussian window functions; the UTA correlation filtering tracker (UTA-CF), an optimum regression model to update the target appearance with simultaneously optimal in position, scale, and integration of multi-feature fusion; and the YOLOv3 re-detector, which ensures re-location of the correct position of the target when the tracking fails. In addition, we introduce the ratio between average peak-to-correlation energy (APCE) of the current frame and average APCE of former frames as a criterion to update the UTA-CF tracker to maintain the target model stability. And we combine the nearest neighbor maximum value method with APCE as criterion together to initialize the YOLOv3 re-detector. We evaluate our algorithm on real aerial infrared target thermal image sequences in terms of precision plot, success plot, and speed. The experimental results show that our method has a significant improvement than the state-of-the-art methods for long-term tracking both in accuracy and robustness for aerial infrared object tracking.

Deep learning classifiers, particularly, Convolutional Neural Networks (CNNs), have been demonstrated to be very effective in the area of SAR automatic target recognition (ATR). Despite of this achievement, there is still a problem with proper classification of target objects from their speckled SAR imagery. In this paper, we address this technical challenge by implementing a two-step Hybrid Stacked Denoising Auto-Encoder (HSDAE) as an effective holistic denoiser and classifier model. Since there is no publically available comprehensive real or synthetic SAR dataset of aerial vehicles, we primarily employed the IRIS Electromagnetic modeling and simulation system to generate the required synthetic noisy SAR images from an array of test physics-based CAD models placed in different operating environments. Our generated test dataset contains synthetically generated SAR images of more than 300 aerial and ground vehicles. These images are systematically scanned from various azimuth and elevation angles as well as from different ranges and in different operating environments. They are regarded as the ground truth object radiation backscattering reflectivity map of test objects. Furthermore, these images are modulated with appropriate additive multiplicative noise to form speckled SAR images. Using a partial collection of ground-truth test vehicles images along with their corresponding speckled SAR images, we train a two-step concurrent denoising auto encoder followed by a CNN model to classify vehicles. Through the initial step, a denoising operation in performed and the test objects’ features like shape, size, and orientation attributes are recovered from any given input speckled SAR images. The output image from this denoising process is next passed as input to a CNN classifier for performing object recognition and classification. In this paper, we presented the architecture of HSDAE and its variants and compare their performances. Our results indicate the proposed HSDAE meets higher accuracy and repeatability for recognizing and classifying the target objects under different operating conditions.

Synthetic Aperture Radar (SAR) technology offers innovative remote sensing opportunity for the area of surveillance applications. However, for the Automatic Target Recognition (ATR) of aerial and ground vehicles from SAR data, there is a need for large-scale imagery of the target objects of interest (TOI’s) from different perspective viewing angles – that is rarely available publically. Such large datasets can be very instrumental for the initial training of deep learning classifiers as well as for the achievement of improved transfer learning. In this paper, we address this shortcoming by introducing IRIS Electromagnetic (EM) modeling and simulation system for virtual staging and automatic generation of realistic synthetic (i.e. simulated) multi0perspective SAR imagery of the test vehicles for the purpose of training of ATR classifiers. Primarily, we prepared a collection of 250 physics-based CAD models containing different aerial and ground vehicles objects. A fourstep process was implemented. In the first step, an optimized multi-path ray-tracing technique was developed for obtaining the synthetic EM radiation backscattering reflectivity patterns of the test objects. In the second step, we furnish the synthetically generated SAR images with different backgrounds (e.g. ground, grass, and asphalt) by employing appropriate noise modulation transfer functions. In the third step, we introduced a method for projecting directional test objects’ shadows from eight different perspective viewings. In the final step, the surface regions producing high-strength radiation backscatterings were highlighted to further enhance realism of the synthetically generated SAR images. To test and verify the validity and dependability of this proposed approach, we compared our simulated SAR imagery results against a number of comparable military and commercial vehicles from MSTAR dataset.

Interest in bistatic radar has fluctuated since its first demonstration. Modern multistatic and MIMO radar systems have prompted a resurgence in the field, particularly where imaging radar and automatic target recognition are concerned. The lack of openly-available bistatic imagery and corresponding analysis of the unique artefacts which occur within it is a significant barrier to developing automatic target recognition methods for such systems. This paper looks to address these issues by presenting an appropriate simulation methodology for obtaining bistatic synthetic aperture radar imagery of ground vehicle targets and investigating the features that occur within this imagery. In this paper, a number of effects unique to the bistatic case are presented, and the performance degradation of a classifier at several bistatic angles is empirically demonstrated. A version of the final database will be publicly released to promote wider research into this challenge.