SAR Ship Target Recognition Via Multi-Scale Feature Attention and Adaptive-Weighed Classifier

Maritime surveillance is indispensable for civilian fields, including national maritime safeguarding, channel monitoring, and so on, in which synthetic aperture radar (SAR) ship target recognition is a crucial research field. The core problem to realizing accurate SAR ship target recognition is the large inner-class variance and inter-class overlap of SAR ship features, which limits the recognition performance. Most existing methods plainly extract multi-scale features of the network and utilize equally each feature scale in the classification stage. However, the shallow multi-scale features are not discriminative enough, and each scale feature is not equally effective for recognition. These factors lead to the limitation of recognition performance. Therefore, we proposed a SAR ship recognition method via multi-scale feature attention and adaptive-weighted classifier to enhance features in each scale, and adaptively choose the effective feature scale for accurate recognition. We first construct an in-network feature pyramid to extract multi-scale features from SAR ship images. Then, the multi-scale feature attention can extract and enhance the principal components from the multi-scale features with more inner-class compactness and inter-class separability. Finally, the adaptive weighted classifier chooses the effective feature scales in the feature pyramid to achieve the final precise recognition. Through experiments and comparisons under OpenSARship data set, the proposed method is validated to achieve state-of-the-art performance for SAR ship recognition

A New MCMC Sampling Based Segment Model for Radar Target Recognition

One of the main tools in radar target recognition is high resolution range profile (HRRP)‎. ‎However‎, ‎it is very sensitive to the aspect angle‎. ‎One solution to this problem is to assume the consecutive samples of HRRP identically independently distributed (IID) in small frames of aspect angles‎, ‎an assumption which is not true in reality‎. ‎However, b‎‎ased on this assumption‎, ‎some models have been developed to characterize the sequential information contained in the multi-aspect radar echoes‎. ‎Therefore‎, ‎they only consider the short dependency between consecutive samples‎. ‎Here‎, ‎we propose an alternative model‎, ‎the segment model‎, ‎to address the shortcomings of these assumptions‎. ‎In addition‎, ‎using a Markov chain Monte-Carlo (MCMC) based Gibbs sampler as an iterative approach to estimate the parameters of the segment model‎, ‎we will show that the proposed method is able to estimate the parameters with quite satisfying accuracy and computational load‎

System Design of Advanced Multi-Beam and Multi-Range Automotive Radar

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 김성철.As the number of vehicles on the road is increased, the incidence of traffic accident is gradually increased and the number of death on roads is also increased. Most accidents are due to carelessness of the driver. If the vehicle can actively recognize the dangerous situation and alert the driver to avoid accident, it will be a great help to the driver. As concern for safety and driver assistance increases, needs for driver assistance system (DAS) are consistently increasing. Moreover, with the grooming demand for autonomous driving, there has been paid a great attention to the incorporation of multiple sensors. Various sensors for safety and convenience are being introduced for automobiles. The detection performance of the automotive radar looks outstanding compared to other sensors such as Lidar, camera, and ultrasonic sensors, in poor weather conditions or environmental conditions of the roads. Among many applications using automotive radars, the adaptive cruise control (ACC) and the autonomous emergency braking (AEB) using forward looking radars are the most basic functions for safety and convenience. Using ACC and AEB functions, drivers can be guaranteed safety as well as convenience when visibility is poor under bad weather conditions. Generally, the radar system for ACC and AEB had been composed of singe longrange radar (LRR) and two of short-range radar (SRR) and the system cost was very expensive. However, the cost can be lowered by the concept of multi-beam, multirange (MBMR) radar which consist of integrated narrow long range beam and wide short range beam in a single radar sensor. In this dissertation, we propose an advanced MBMR radar for ACC and AEB using 77 GHz band and highly integrated RF ICs. The detection specifications are investii gated base on theoretical radar principles and effective design concepts are suggested to satisfy the specifications. We implemented an actually working forward looking MBMR radar and performed experiments to verify the detection performance. To overcome the limitation of radar hardware resources for cost-effective design, we propose novel signal processing schemes to recognize environment on roads which are regarded as impossible with automotive radar. Characteristics of an iron tunnel which deteriorate the detection performance of the radar are analyzed and a measure for the recognition is proposed. Moreover, the recognition method is expanded to harmonic clutters which are caused by man-made structures on roads containing periodic structures such as iron tunnels, guardrails, and sound-proof wall. The harmonic clutter suppression method is also proposed to enhance the quality of the received signal and improve the detection performance of the radar. All experiments are performed using the proposed MBMR radar to verify the detection performance and the usefulness of proposed signal processing methods for recognition and suppression of clutters on roads.1 Introduction 1 2 A Multi-Beam and Multi-Range FMCW Radar using 77 GHz Frequency Band for ACC and AEB 6 2.1 Introduction 6 2.2 System Design of Advanced MBMR Radar 7 2.3 Waveform and Signal Processing Structure Design 14 2.4 Advanced Singal Processing Technique for AEB 19 2.5 Design Results 20 2.6 Experimental Results 22 2.6.1 Anechoic Chamber 22 2.6.2 Field Test 27 2.7 Summary 29 3 Iron-tunnel Recognition 30 3.1 Introduction 30 3.2 Iron-Tunnel Recognition 32 3.2.1 Radar Model 32 3.2.2 Spectral Characteristics of an Iron-Tunnel 34 3.2.3 Measuring Spectrum Spreading 40 3.3 Experimental Result 45 3.3.1 Iron-Tunnel Recognition 45 3.3.2 Early Target Detection and Prevention of Target Drop 49 3.4 Summary 53 4 Clutter Suppression 55 4.1 Introduction 55 4.2 Clutter Recognition 57 4.2.1 Radar Model 57 4.2.2 Spectral Analysis of Road Environment 62 4.2.3 Proposed Clutter-recognition Method (Measuring Harmonics of Clutter) 64 4.3 Clutter Suppression 69 4.3.1 Proposed clutter suppression method 69 4.3.2 Verification using real data 71 4.4 Experimental results 74 4.5 Summary 81 5 Conclusion and Future Works 82 Bilbliography 85 Abstract (In Korean) 89Docto

Personnel recognition and gait classification based on multistatic micro-doppler signatures using deep convolutional neural networks

In this letter, we propose two methods for personnel recognition and gait classification using deep convolutional neural networks (DCNNs) based on multistatic radar micro-Doppler signatures. Previous DCNN-based schemes have mainly focused on monostatic scenarios, whereas directional diversity offered by multistatic radar is exploited in this letter to improve classification accuracy. We first propose the voted monostatic DCNN (VMo-DCNN) method, which trains DCNNs on each receiver node separately and fuses the results by binary voting. By merging the fusion step into the network architecture, we further propose the multistatic DCNN (Mul-DCNN) method, which performs slightly better than VMo-DCNN. These methods are validated on real data measured with a 2.4-GHz multistatic radar system. Experimental results show that the Mul-DCNN achieves over 99% accuracy in armed/unarmed gait classification using only 20% training data and similar performance in two-class personnel recognition using 50% training data, which are higher than the accuracy obtained by performing DCNN on a single radar node

A Multiple Radar Approach for Automatic Target Recognition of Aircraft using Inverse Synthetic Aperture Radar

Along with the improvement of radar technologies, Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR) has come to be an active research area. SAR/ISAR are radar techniques to generate a two-dimensional high-resolution image of a target. Unlike other similar experiments using Convolutional Neural Networks (CNN) to solve this problem, we utilize an unusual approach that leads to better performance and faster training times. Our CNN uses complex values generated by a simulation to train the network; additionally, we utilize a multi-radar approach to increase the accuracy of the training and testing processes, thus resulting in higher accuracies than the other papers working on SAR/ISAR ATR. We generated our dataset with 7 different aircraft models with a radar simulator we developed called RadarPixel; it is a Windows GUI program implemented using Matlab and Java programming, the simulator is capable of accurately replicating a real SAR/ISAR configurations. Our objective is to utilize our multi-radar technique and determine the optimal number of radars needed to detect and classify targets.Comment: 8 pages, 9 figures, International Conference for Data Intelligence and Security (ICDIS

Distant Vehicle Detection Using Radar and Vision

For autonomous vehicles to be able to operate successfully they need to be aware of other vehicles with sufficient time to make safe, stable plans. Given the possible closing speeds between two vehicles, this necessitates the ability to accurately detect distant vehicles. Many current image-based object detectors using convolutional neural networks exhibit excellent performance on existing datasets such as KITTI. However, the performance of these networks falls when detecting small (distant) objects. We demonstrate that incorporating radar data can boost performance in these difficult situations. We also introduce an efficient automated method for training data generation using cameras of different focal lengths