Ballistic Target Recognition Research Articles

Quadruplet depth-wise separable fusion convolution neural network for ballistic target recognition with limited samples

Radar high-resolution range profile (HRRP) plays a crucial role in ballistic target recognition due to its simplicity and fast computation. Convolutional neural networks (CNNs), a popular deep learning approach, are widely used in radar automatic target recognition (RATR) based on HRRP, thanks to their excellent feature extraction capabilities. However, current research mainly focuses on integrating branch networks like recurrent neural networks and attention mechanisms into vanilla CNNs to enhance feature extraction. Limited research exists on reducing the computational complexity of vanilla CNNs when applied to HRRP-based RATR, as well as target recognition problems with limited samples. To address these issues, this paper proposes a lightweight depth-wise separable fusion CNN (DSFCNN) for ballistic target HRRP recognition. The DSFCNN reduces the computational complexity of vanilla CNNs while improving recognition accuracy. Furthermore, we introduce the sample-level fitting and class-level distinguishability (SFCD) loss for limited-sample ballistic target HRRP recognition. The SFCD loss ensures better fitting performance of all samples, as well as higher intra-class compactness and inter-class separability, enhancing the distinguishability of embedded features in limited-sample conditions. During the training phase, the SFCD loss is equally applied to four DSFCNNs with shared structures and parameters, resulting in a model known as Quadruplet DSFCNN (QDSFCNN). However, during the testing phase, only one DSFCNN is utilized. Additionally, we propose the parameter quantity shifting-fitting performance (PQS-FP) coordinate system to elucidate the performance disparities between DSFCNN and vanilla CNNs in our experiments. PQS-FP offers inspirational insights into the relations among parameter quantity, fitting performance, and recognition performance. Extensive experimental results demonstrate the robustness and effectiveness of QDSFCNN for ballistic target recognition in various limited-sample scenarios.

Ballistic Target Recognition Based on 4-D Point Cloud Using Randomized Stepped Frequency Radar

Ballistic target recognition (BTR) is critical to the ballistic missile defense system. The challenge of this task is to distinguish warheads from numerous unknown confusing targets within a short observing time. The micromotion feature is proved to be effective for this task. However, traditional methods need a long observing time to acquire enough information for the recognition because of using low-dimensional features. In addition, these model-driven methods cannot handle irregular ballistic targets, such as debris. In this article, we propose a BTR scheme, which characterizes the micromotion features with a higher dimensional representation, i.e., the time–range–velocity–power 4-D point cloud, using the randomized stepped frequency radar. The higher dimensional information contained in the 4-D point cloud can reduce the required observing time. Besides, this scheme combines the model-driven method with a data-driven deep neural network to meet the challenge of model mismatch caused by irregular targets. As a result, the proposed BTR scheme is time efficient and robust, which has been proved on an electromagnetic simulation dataset.

Recognition of Ballistic Targets by Fusing Micro-Motion Features with Networks

Ballistic target recognition is of great significance for space attack and defense. The micro-motion features, which contain spatial and motion information, can be regarded as the foundation of the recognition of ballistic targets. To take full advantage of the micro-motion information of ballistic targets, this paper proposes a method based on feature fusion to recognize ballistic targets. The proposed method takes two types of data as input: the time–range (TR) map and the time–frequency (TF) spectrum. An improved feature extraction module based on 1D convolution and time self-attention is applied first to extract the multi-level features at each time instant and the global temporal information. Then, to efficiently fuse the features extracted from the TR map and TF spectrum, deep generalized canonical correlation analysis with center loss (DGCCA-CL) is proposed to transform the extracted features into a hidden space. The proposed DGCCA-CL possesses better performance in two aspects: small intra-class distance and compact representation, which is crucial to the fusion of multi-modality data. At last, the attention mechanism-based classifier which can adaptively focus on the important features is employed to give the target types. Experiment results show that the proposed method outperforms other network-based recognition methods.

End-to-End Recognition of Similar Space Cone–Cylinder Targets Based on Complex-Valued Coordinate Attention Networks

Except for the slight difference of micromotion parameters, some decoys and warheads have the same geometry and micromotion form. As a result, recognition of similar space cone–cylinder targets is one of the difficult problems in ballistic target recognition. In recent years, due to the good effect of deep neural networks (DNNs) in optical target recognition, space cone–cylinder target recognition methods based on DNN have attracted wide attention. However, these DNN-based methods only recognize the space cone–cylinder targets with different shapes and different micromotion forms. Moreover, these methods require some time-consuming preprocessing operations, which need to observe the target for at least one micromotion period. To recognize similar space cone–cylinder targets, we propose a complex-valued coordinate attention networks (CV-CANets)-based end-to-end recognition method. Firstly, we establish the signal model of space cone–cylinder targets. Secondly, we propose CV-CA blocks by transforming the coordinate attention mechanism into the complex-valued domain. Then, we construct CV-CANet based on the proposed CV-CA blocks. Finally, the proposed CV-CANet is trained and tested by the narrowband radar echo data, which is generated by electromagnetic calculation. Compared with the convolutional neural network (CNN)-based recognition methods, the proposed method can not only recognize the similar space cone–cylinder targets but also is superior in terms of time cost and observation requirement. Extensive experiments validate that the proposed recognition method is effective when the targets only have a slight difference on the precession angular frequency and the observation time is less than half a period.

Analysis of Infrared Radiation Characteristics and Penetration Measures of Warhead

Ballistic target recognition occupies a unique and important position in many application fields of target recognition because of its challenge and important position of ballistic missile defense in national security; recognition time of defense system becomes very limited because of ballistic missile high-speed flight; recognition distance of defense system is also due to stealth technology. The integrated application of active jamming and passive decoy greatly increases the difficulty of identification of defense system. Because of its special status and challenge, ballistic target recognition has attracted wide attention of researchers at home and abroad, making it one of the most important issues in infrared target recognition research at home and abroad. In this paper, the infrared characteristics of a ballistic missile warhead target/decoy are analyzed, and the corresponding penetration measures are put forward according to the analysis results.

Ballistic targets micro-motion and geometrical shape parameters estimation from sparse decomposition representation of infrared signatures.

Micro-motion dynamics and geometrical shape are considered to be essential evidence for infrared (IR) ballistic target recognition. However, it is usually hard or even impossible to describe the geometrical shape of an unknown target with a finite number of parameters, which results in a very difficult task to estimate target micro-motion parameters from the IR signals. Considering the shapes of ballistic targets are relatively simple, this paper explores a joint optimization technique to estimate micro-motion and dominant geometrical shape parameters from sparse decomposition representation of IR irradiance intensity signatures. By dividing an observed target surface into a number of segmented patches, an IR signature of the target can be approximately modeled as a linear combination of the observation IR signatures from the dominant segmented patches. Given this, a sparse decomposition representation of the IR signature is established with the dictionary elements defined as each segmented patch's IR signature. Then, an iterative optimization method, based on the batch second-order gradient descent algorithm, is proposed to jointly estimate target micro-motion and geometrical shape parameters. Experimental results demonstrate that the micro-motion and geometrical shape parameters can be effectively estimated using the proposed method, when the noise of the IR signature is in an acceptable level, for example, SNR>0 dB.

On the possibility of employing a surveillance radar station to solve the problems of locating points of aim (points of impact)

We consider the possibility of employing a surveillance radar station to recognise ballistic targets (projectiles) and locate points of aim. We present the results of full-scale live-fire trial data analysis and a method for implementing ballistic target (projectile) recognition by radar stations of the surveillance type. We developed a mathematical model, an algorithm for ballistic target recognition, as well as algorithms and software to locate the point of aim

Precession Characteristics Analysis of Ballistic Target Based on Non-Ideal Scattering Centers

Precession characteristics are very important for ballistic target recognition. It is theoretically approved that the radar echo is sinusoidal frequency modulation signal when the micro-motion of scattering centers is the same as warhead itself. But this assumption disagrees with the fact. To solve this problem, model of precession target is set and the expression of the micro-Doppler of the non-ideal scattering centers is obtained in the paper. Then, precession characteristics of warhead are presented in the end of the paper. Finally, the correctness of theoretical analysis is validated through the simulation results.

Pseudo-Maximum Likelihood Estimation of ballistic missile precession frequency

In order to estimate a ballistic missile precession frequency, which is an important feature parameter in ballistic target recognition, we firstly establish the dynamic Radar Cross Section (RCS) signal model for a conical ballistic missile warhead with precession motion. Scintillation which is an important factor that cannot be ignored in radar measurement is taken into account in the model establishment, and modeled either as a log-normal or chi-square multiplicative noise. The distribution of obtained RCS signal is non-Gaussian and cannot be obtained in closed-form. Hence, the exact Maximum Likelihood Estimation (MLE) of the pertinent parameter, the missile precession frequency, is untractable. In the paper, we propose three pseudo-MLE approaches to estimate the parameter of missile precession frequency. The first approach ignores the multiplicative noise in the measured RCS. The second approach ignores the additive noise (i.e., assuming the infinite signal-to-noise ratio (SNR)). The third approach enforces a Gaussian distribution on both additive and multiplicative noise components. The Cramér–Rao lower bound (CRLB) corresponding to the maximum SNR scenario is derived. Simulations indicate that accounting for the multiplicative noise in the estimation significantly improves estimation performance, and also show the validation and robustness of the proposed methods.