High Resolution Radar Target Research Articles

High-Resolution Radar Target Recognition via Inception-Based VGG (IVGG) Networks.

Aiming at high-resolution radar target recognition, new convolutional neural networks, namely, Inception-based VGG (IVGG) networks, are proposed to classify and recognize different targets in high range resolution profile (HRRP) and synthetic aperture radar (SAR) signals. The IVGG networks have been improved in two aspects. One is to adjust the connection mode of the full connection layer. The other is to introduce the Inception module into the visual geometry group (VGG) network to make the network structure more suik / for radar target recognition. After the Inception module, we also add a point convolutional layer to strengthen the nonlinearity of the network. Compared with the VGG network, IVGG networks are simpler and have fewer parameters. The experiments are compared with GoogLeNet, ResNet18, DenseNet121, and VGG on 4 datasets. The experimental results show that the IVGG networks have better accuracies than the existing convolutional neural networks.

A SAR Image Target Recognition Approach via Novel SSF-Net Models.

With the wide application of high-resolution radar, the application of Radar Automatic Target Recognition (RATR) is increasingly focused on how to quickly and accurately distinguish high-resolution radar targets. Therefore, Synthetic Aperture Radar (SAR) image recognition technology has become one of the research hotspots in this field. Based on the characteristics of SAR images, a Sparse Data Feature Extraction module (SDFE) has been designed, and a new convolutional neural network SSF-Net has been further proposed based on the SDFE module. Meanwhile, in order to improve processing efficiency, the network adopts three methods to classify targets: three Fully Connected (FC) layers, one Fully Connected (FC) layer, and Global Average Pooling (GAP). Among them, the latter two methods have less parameters and computational cost, and they have better real-time performance. The methods were tested on public datasets SAR-SOC and SAR-EOC-1. The experimental results show that the SSF-Net has relatively better robustness and achieves the highest recognition accuracy of 99.55% and 99.50% on SAR-SOC and SAR-EOC-1, respectively, which is 1% higher than the comparison methods on SAR-EOC-1.

Supervised kernel discriminant local tangent space alignment for high-resolution range profile-based radar target recognition

We present a modified local tangent space alignment (LTSA) algorithm, called supervised kernel discriminant local tangent space alignment (SKDLTSA), for radar target recognition based on high-resolution range profile (HRRP). SKDLTSA aims to extract intraclass geometric structure embedded in local neighborhoods, as well as to maximize interclass separability characterized by overall distances among different classes. It is formulated with kernel technique to extract nonlinear features, which helps to obtain better performance than its linear counterparts. Extensive experiments on measured HRRP data from three flying airplanes demonstrate that the proposed method can significantly improve the recognition performance. Further results also indicate its robustness to target attitude variations and noise corruption.

A Hybrid Approach to Cognition in Radars

In many engineering domains, cognition is emerging to play vital role. Cognition will play crucial role in radar engineering as well for the development of next generation radars. In this paper, a cognitive architecture for radars is introduced, based on hybrid cognitive architectures. The paper proposes deep learning applications for integrated target classification based on high-resolution radar range profile measurements and target revisit time calculation as case studies. The proposed architecture is based on the artificial cognitive systems concepts and provides a basis for addressing cognition in radars, which is inadequately explored for radar systems. Initial experimental studies on the applicability of deep learning techniques under this approach provided promising results.

Robust statistical recognition and reconstruction scheme based on hierarchical Bayesian learning of HRR radar target signal

A hierarchical Bayesian model is developed to characterize the complex-valued high range resolution (HRR) radar target signal, motivated by the problem of radar automatic target recognition (RATR) robust to low signal-to-noise ratio (SNR) or narrowband interference. Here we assume a sparseness-promoting prior on the complex echoes and a Markov dependency for the location of the dominant scattering center between consecutive HRR signals. The number of the dominant scattering centers can be automatically determined, while the posterior distributions of their complex coefficients and locations can be inferred via such Bayesian model. In the training stage, based on the proposed Bayesian model, the statistical aspect-frame template can be learned for HRR complex training samples from each target-aspect sector under high SNR and without any interference. Considering the low SNR or narrowband interference problem for a test sample, the aspect-frame templates can be updated to make a robust recognition decision for the noised and interfered test sample. Simultaneously, the test sample can be denoised and recovered via the analytically posterior estimation for the reconstruction, which is referred to as the statistical compressive sensing (CS) inversion. In contrast to the traditional CS methods that only utilize the underlying sparse property of a measurement (here a measurement is a test sample), our statistical CS inversion also exploits the statistical information of the training samples, which are under high SNR and without any interference. Therefore, the better recognition and reconstruction performances can be obtained via our method. Efficient inference is performed via variational Bayesian (VB) for the proposed Bayesian model. To validate the formulation, we present our experimental results on the measured HRR dataset, with comparisons to some state-of-the-art methods.

High resolution range-reflectivity estimation of radar targets via compressive sampling and Memetic Algorithm

Recent results of Compressive Sampling (CS) have demonstrated its feasibility in high-resolution radar targets estimation and imaging [2,10,14,15,17,19,23,29,30,32–34]. However, the signal recovery is reduced to seeking a sparse solution to an underdetermined linear system of equations. It is potentially very difficult because even finding a solution that approximates the true minimum is NP-hard. In this paper, we introduce Memetic Algorithm (MA) to solve this non-convex l0-norm minimization problem, and design a compressive receiver for high-resolution range-reflectivity estimation of multiple radar targets. A double-population MA is proposed, where the position population is used to evaluate the ranges, and the coefficient population is used to realize a local search of target reflectivities. By combining the global search with a local searching operation to exploit the available knowledge in the recovery, the proposed MA outperforms the general purpose optimization algorithms in terms of the quality of solution. Some experiments are taken to investigate the performance of this compressive receiver at different sampling rates, and the results show the superiority to its counterparts in both noiseless environment and noisy, cluttered environment.

Computational Burden Resulting from Image Recognition of High Resolution Radar Sensors

This paper presents a methodology for high resolution radar image generation and automatic target recognition emphasizing the computational cost involved in the process. In order to obtain focused inverse synthetic aperture radar (ISAR) images certain signal processing algorithms must be applied to the information sensed by the radar. From actual data collected by radar the stages and algorithms needed to obtain ISAR images are revised, including high resolution range profile generation, motion compensation and ISAR formation. Target recognition is achieved by comparing the generated set of actual ISAR images with a database of ISAR images generated by electromagnetic software. High resolution radar image generation and target recognition processes are burdensome and time consuming, so to determine the most suitable implementation platform the analysis of the computational complexity is of great interest. To this end and since target identification must be completed in real time, computational burden of both processes the generation and comparison with a database is explained separately. Conclusions are drawn about implementation platforms and calculation efficiency in order to reduce time consumption in a possible future implementation.

Computational Burden Resulting from Image Recognition of High Resolution Radar Sensors

This paper presents a methodology for high resolution radar image generation and automatic target recognition emphasizing the computational cost involved in the process. In order to obtain focused inverse synthetic aperture radar (ISAR) images certain signal processing algorithms must be applied to the information sensed by the radar. From actual data collected by radar the stages and algorithms needed to obtain ISAR images are revised, including high resolution range profile generation, motion compensation and ISAR formation. Target recognition is achieved by comparing the generated set of actual ISAR images with a database of ISAR images generated by electromagnetic software. High resolution radar image generation and target recognition processes are burdensome and time consuming, so to determine the most suitable implementation platform the analysis of the computational complexity is of great interest. To this end and since target identification must be completed in real time, computational burden of both processes the generation and comparison with a database is explained separately. Conclusions are drawn about implementation platforms and calculation efficiency in order to reduce time consumption in a possible future implementation.

High resolution radar target adaptive detector and performance assessment

The high resolution radar target detection is addressed in the non-Gaussian clutter. An adaptive detector is derived for range-spread target based on a novel covariance matrix estimator. It is proved that the new detector is constant false alarm rate (CFAR) to both of the clutter covariance matrix structure and power level theoretically for match cases. The simulation results show that the new detector is almost CFAR for mismatch cases, and it outperforms the existing adaptive detector based on the sample covariance matrix. It also shows that the detection performance improves, as the number of pulses, the number of secondary data or the clutter spike increases. In addition, the derived detector is robust to different subsets, estimated clutter group sizes and correlations of clutter. Importantly, the number of iterations for practical application is just one.

Multiple Target Detection for High Resolution Doppler Radar

A new approach of signal design for multiple target detection in high resolution radar in presence of high additive noise and Doppler shift. The Hamming backtrack algorithm is designed and used for optimization by taking figure of merit and discrimination as the measure of goodness. The detection capability is further improved through coincidence detection. The simulation results shows the improved robustness of noise and Doppler shift for high resolution radar target detection.

Application Research on High Resolution Radar Target Aggregation

In high resolution radar system, the same target always has original data; so we need to merge multiple data from the same target as one target. Because of the system’s real-time requirement, we usually have to carry out target aggregation as quickly as possible. In this paper, we propose a quick target aggregation method based on clustering algorithm. The proposed method divides original data into subsets by single dimensional distance, and then merges subsets according to single dimensional distance and setdensity. At last we apply the proposed method to carry out target aggregation for airport scene surveillance radar system. Experimental result shows the proposed method has high execution efficiency and is not sensitive to noise data; it is useful for high resolution radar target aggregation.

An evolutionary multiclass algorithm for automatic classification of high range resolution radar targets

In this paper a novel scheme to solve multiclass classification problems using pure evolutionary techniques is presented. Our proposal consists of the evolution of several geometric structures such as hypercubes, hyperspheres or hyperoctahedrons, to

Maximum Position Alignment Method for Noisy High-Resolution Radar Target Classification

In this paper, the alignment of noisy high-resolution radar signals using the maximum position method is studied. The relationship between the shift estimation and the signal-to-noise ratio is considered. As a result, two analytical expressions are obtained that approximate the root-mean-square error of the difference in the shift estimation with and without noise. These two expressions allow us to improve the understanding of the sensitivity to noise of the Maximum Position alignment method.

A NOVEL FORWARD–BACKWARD SMOOTHING-BASED LEARNING SUBSPACE METHOD FOR RECOGNITION OF RADAR TARGETS

This paper proposes a novel Forward–Backward Smoothing-Based Learning Subspace Method (FBSLSM), which can satisfy the equirements of being insensitive to the order of presentation of the training samples, and is of faster convergence speed. This method is applied to the recognition of simulating High Resolution Radar (HRR) targets (two for ships, one for chaff). Moreover, for recognition of HRR targets, a new selection method of subspace dimensionality is given. The computer simulating experiments show that the corresponding performance of proposed FBSLSM such as rate of correct recognition and convergence speed is better than that of the ALSM presented by Oja.

Circularly integrated bispectra: Novel shift invariantfeaturesfor high-resolution radar target recognition

Novel shift invariant features based on the integration of the bispectra of range profiles along circles on the bifrequency plane are proposed for high-resolution radar target recognition. The proposed features are evaluated using a dataset of three scaled aircraft models and are shown to be highly discriminative.

A detection method of high resolution radar targets based on position correlation

This paper studies a detection method of targets of high resolution radar operating at the band of millimeter-wave(32∼38GHz) under the background of the clutters, and proposes a new nonparametric detection method, which not only does less computation, but also is able to detect multiple extended targets radially distributed along distance “corridor”, based on the position (range) correlation information of one-dimensional range images(or called range profiles) of high resolution radar targets. The experimental results, on the real echo data of tank illuminated by the millimeter-wave stepped frequency high resolution radar, have certified that such a method presented in this paper is a very effective detection method for multiple extended targets.

Matching score properties between range profiles of high-resolution radar targets

In this paper, how the statistical properties of the matching scores are affected by the carrier frequency, the aspect variation, the range resolution, and the target complexity are studied. The statistical parameters (mean and standard deviation) of the matching scores among range profiles of independent objects are derived, and their values are used as references to determine the threshold values for target identification. It is found that the range profile obtained at a certain carrier frequency can also be used as the feature vector for radars operated at shifted frequencies if the range resolution is fine enough or the target is simple. It is also found that a radar with higher range resolution can tolerate more aspect variation, yielding a significant advantage in saving memory space for establishing the data base. The results obtained can have several applications such as target identification, data association in multiple target tracking, and target direction determination when widely-spaced high-resolution radars are employed.

A neural clustering approach for high resolution radar target classification

Neural learning techniques, the Self-Organizing Feature Map and Learning Vector Quantization, have been applied to the automatic target recognition (ATR) problem in the presence of high range resolution radar target signatures. The database is collected by placing the targets on a rotary turntable and slowly turning them over a complete 360° azimuth while the radar signatures are collected. Our pattern recognition system is composed of a feature identifier and a classifier. A simple Euclidean distance classifier using those identified features provides a baseline of 97% mean probability of correct classification.

Target detection-sensitivity enhancement using high-resolution radar and 2-D and 3-D stereo target displays

A 1989 experiment in detection-sensitivity enhancement of an airplane target in a background of ground clutter is reported. The experiment showed an 11 dB improvement when a high-resolution radar target was displayed as a 2-D image rather than a blip or blob (0-dimensions). A further 7 dB was observed when the target was displayed in stereo (3-D). These extraordinary observations warrant independent verification and theoretical examination.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>