Systematic analysis of dynamic miRNA-target interactions during C. elegans development

Although microRNA (miRNA)-mediated functions have been implicated in many aspects of animal development, the majority of miRNA::mRNA regulatory interactions remain to be characterized experimentally. We used an AIN/GW182 protein immunoprecipitation approach to systematically analyze miRNA::mRNA interactions during C. elegans development. We characterized the composition of miRNAs in functional miRNA-induced silencing complexes (miRISCs) at each developmental stage and identified three sets of miRNAs with distinct stage-specificity of function. We then identified thousands of miRNA targets in each developmental stage, including a significant portion that is subject to differential miRNA regulation during development. By identifying thousands of miRNA family-mRNA pairs with temporally correlated patterns of AIN-2 association, we gained valuable information on the principles of physiological miRNA::target recognition and predicted 1589 high-confidence miRNA family::mRNA interactions. Our data support the idea that miRNAs preferentially target genes involved in signaling processes and avoid genes with housekeeping functions, and that miRNAs orchestrate temporal developmental programs by coordinately targeting or avoiding genes involved in particular biological functions.

How Leucocyte Cell Membrane Modified Janus Microcapsules are Phagocytosed by Cancer Cells

Modern drug delivery systems rely on either antibody-based single-surface recognition or on surface-hydrophobicity-based approaches. For a tumor showing various surface mutations, both approaches fail. This publication hereby presents Janus capsules based on polyelectrolyte multilayer microcapsules exhibiting human leucocyte (THP-1 cell line) cell membranes for discriminating HUVEC cells from three different cancer cell lines. Despite destroying the cellular integrity of leucocyte cells, the modified Janus capsules are able to adhere to cancer cells. Leucocyte cell-membrane-coated Janus capsules are phagocytosed with the cellular membrane part pointing to the cells.

Macros in microRNA target identification: a comparative analysis of in silico, in vitro, and in vivo approaches to microRNA target identification

MicroRNAs (miRNAs) are short RNA molecules that modulate post-transcriptional gene expression by partial or incomplete base-pairing to the complementary sequences on their target genes. Sequence-based miRNA target gene recognition enables the utilization of computational methods, which are highly informative in identifying a subset of putative miRNA targets from the genome. Subsequently, single miRNA-target gene binding is evaluated experimentally by in vitro assays to validate and quantify the transcriptional or post-transcriptional effects of miRNA-target gene interaction. Although ex vivo approaches are instructive in providing a basis for further analyses, in vivo genetic studies are critical to determine the occurrence and biological relevance of miRNA targets under physiological conditions. In the present review, we summarize the important features of each of the experimental approaches, their technical and biological limitations, and future challenges in light of the complexity of miRNA target gene recognition.

Evolution of the Cytolytic Pore-Forming Proteins (Actinoporins) in Sea Anemones

Sea anemones (Cnidaria, Anthozoa, and Actiniaria) use toxic peptides to incapacitate and immobilize prey and to deter potential predators. Their toxin arsenal is complex, targeting a variety of functionally important protein complexes and macromolecules involved in cellular homeostasis. Among these, actinoporins are one of the better characterized toxins; these venom proteins form a pore in cellular membranes containing sphingomyelin. We used a combined bioinformatic and phylogenetic approach to investigate how actinoporins have evolved across three superfamilies of sea anemones (Actinioidea, Metridioidea, and Actinostoloidea). Our analysis identified 90 candidate actinoporins across 20 species. We also found clusters of six actinoporin-like genes in five species of sea anemone (Nematostella vectensis, Stomphia coccinea, Epiactis japonica, Heteractis crispa, and Diadumene leucolena); these actinoporin-like sequences resembled actinoporins but have a higher sequence similarity with toxins from fungi, cone snails, and Hydra. Comparative analysis of the candidate actinoporins highlighted variable and conserved regions within actinoporins that may pertain to functional variation. Although multiple residues are involved in initiating sphingomyelin recognition and membrane binding, there is a high rate of replacement for a specific tryptophan with leucine (W112L) and other hydrophobic residues. Residues thought to be involved with oligomerization were variable, while those forming the phosphocholine (POC) binding site and the N-terminal region involved with cell membrane penetration were highly conserved.

Generation of neuromuscular specificity in Drosophila: novel mechanisms revealed by new technologies

The Drosophila larval neuromuscular system is one of the best-characterized model systems for axon targeting. In each abdominal hemisegment, only 36 identified motor neurons form synaptic connections with just 30 target muscles in a highly specific and stereotypic manner. Studies in the 1990s identified several cell-surface and secreted proteins that are expressed in specific muscles and contribute to target specificity. Emerging evidence suggests that target selection is determined not only by attraction to the target cells but also by exclusion from non-target cells. Proteins with leucine-rich repeats (LRR proteins) appear to be a major molecular family of proteins responsible for the targeting. While the demonstrated roles of the target-derived cues point to active recognition by presynaptic motor neurons, postsynaptic muscles also reach out and recognize specific motor neurons by sending out cellular protrusions called myopodia. Simultaneous live imaging of myopodia and growth cones has revealed that local and mutual recognition at the tip of myopodia is critical for selective synapse formation. A large number of candidate target cues have been identified on a single muscle, suggesting that target specificity is determined by the partially redundant and combinatorial function of multiple cues. Analyses of the seemingly simple neuromuscular system in Drosophila have revealed an unexpected complexity in the mechanisms of axon targeting.

Conformational frustration in calmodulin-target recognition

Calmodulin (CaM) is a primary calcium (Ca(2+) )-signaling protein that specifically recognizes and activates highly diverse target proteins. We explored the molecular basis of target recognition of CaM with peptides representing the CaM-binding domains from two Ca(2+) -CaM-dependent kinases, CaMKI and CaMKII, by employing experimentally constrained molecular simulations. Detailed binding route analysis revealed that the two CaM target peptides, although similar in length and net charge, follow distinct routes that lead to a higher binding frustration in the CaM-CaMKII complex than in the CaM-CaMKI complex. We discovered that the molecular origin of the binding frustration is caused by intermolecular contacts formed with the C-domain of CaM that need to be broken before the formation of intermolecular contacts with the N-domain of CaM. We argue that the binding frustration is important for determining the kinetics of the recognition process of proteins involving large structural fluctuations.

Extended target recognition in cognitive radar networks

We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS). Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches.

Elimination of vision-guided target attraction in Aedes aegypti using CRISPR

Blood-feeding insects, such as the mosquito, Aedes (Ae.) aegypti, use multiple senses to seek out and bite humans.^1,2 Upon exposure to the odor of CO₂, the attention of female mosquitoes to potential targets is greatly increased. Female mosquitoes are attracted to high-contrast visual cues and use skin olfactory cues to assist them in homing in on targets several meters away.^3-9 Within close range, convective heat from skin and additional skin odors further assist the mosquitoes' evaluation as to whether the object of interest might be a host.^10,11 Here, using CRISPR-Cas9, we mutated the gene encoding Op1, which is the most abundant of the five rhodopsins expressed in the eyes of Ae. aegypti. Using cage and wind-tunnel assays, we found that elimination of op1 did not impair CO₂-induced target seeking. We then mutated op2, which encodes the rhodopsin most similar to Op1, and also found that there was no impact on this behavior. Rather, mutation of both op1 and op2 was required for abolishing vision-guided target attraction. In contrast, the double mutants exhibited normal phototaxis and odor-tracking responses. By measuring the walking optomotor response, we found that the double mutants still perceived optic flow. In further support of the conclusion that the double mutant is not blind, the animals retained an electrophysiological response to light, although it was diminished. This represents the first genetic perturbation of vision in mosquitoes and indicates that vision-guided target attraction by Ae. aegypti depends on two highly related rhodopsins.

An Optimized Transient Dual Luciferase Assay for Quantifying MicroRNA Directed Repression of Targeted Sequences

Studies investigating the action of small RNAs on computationally predicted target genes require some form of experimental validation. Classical molecular methods of validating microRNA action on target genes are laborious, while approaches that tag predicted target sequences to qualitative reporter genes encounter technical limitations. The aim of this study was to address the challenge of experimentally validating large numbers of computationally predicted microRNA-target transcript interactions using an optimized, quantitative, cost-effective, and scalable approach. The presented method combines transient expression via agroinfiltration of Nicotiana benthamiana leaves with a quantitative dual luciferase reporter system, where firefly luciferase is used to report the microRNA-target sequence interaction and Renilla luciferase is used as an internal standard to normalize expression between replicates. We report the appropriate concentration of N. benthamiana leaf extracts and dilution factor to apply in order to avoid inhibition of firefly LUC activity. Furthermore, the optimal ratio of microRNA precursor expression construct to reporter construct and duration of the incubation period post-agroinfiltration were determined. The optimized dual luciferase assay provides an efficient, repeatable and scalable method to validate and quantify microRNA action on predicted target sequences. The optimized assay was used to validate five predicted targets of rice microRNA miR529b, with as few as six technical replicates. The assay can be extended to assess other small RNA-target sequence interactions, including assessing the functionality of an artificial miRNA or an RNAi construct on a targeted sequence.

"You Have Reached Your Destination": A Single Trial EEG Classification Study

Studies have established that it is possible to differentiate between the brain's responses to observing correct and incorrect movements in navigation tasks. Furthermore, these classifications can be used as feedback for a learning-based BCI, to allow real or virtual robots to find quasi-optimal routes to a target. However, when navigating it is important not only to know we are moving in the right direction toward a target, but also to know when we have reached it. We asked participants to observe a virtual robot performing a 1-dimensional navigation task. We recorded EEG and then performed neurophysiological analysis on the responses to two classes of correct movements: those that moved closer to the target but did not reach it, and those that did reach the target. Further, we used a stepwise linear classifier on time-domain features to differentiate the classes on a single-trial basis. A second data set was also used to further test this single-trial classification. We found that the amplitude of the P300 was significantly greater in cases where the movement reached the target. Interestingly, we were able to classify the EEG signals evoked when observing the two classes of correct movements against each other with mean overall accuracy of 66.5 and 68.0% for the two data sets, with greater than chance levels of accuracy achieved for all participants. As a proof of concept, we have shown that it is possible to classify the EEG responses in observing these different correct movements against each other using single-trial EEG. This could be used as part of a learning-based BCI and opens a new door toward a more autonomous BCI navigation system.

RGB-D Image Processing Algorithm for Target Recognition and Pose Estimation of Visual Servo System

This paper studies the control performance of visual servoing system under the planar camera and RGB-D cameras, the contribution of this paper is through rapid identification of target RGB-D images and precise measurement of depth direction to strengthen the performance indicators of visual servoing system such as real time and accuracy, etc. Firstly, color images acquired by the RGB-D camera are segmented based on optimized normalized cuts. Next, the gray scale is restored according to the histogram feature of the target image. Then, the obtained 2D graphics depth information and the enhanced gray image information are distort merged to complete the target pose estimation based on the Hausdorff distance, and the current image pose is matched with the target image pose. The end angle and the speed of the robot are calculated to complete a control cycle and the process is iterated until the servo task is completed. Finally, the performance index of this control system based on proposed algorithm is tested about accuracy, real-time under position-based visual servoing system. The results demonstrate and validate that the RGB-D image processing algorithm proposed in this paper has the performance in the above aspects of the visual servoing system.

Active Multimodal Sensor System for Target Recognition and Tracking

High accuracy target recognition and tracking systems using a single sensor or a passive multisensor set are susceptible to external interferences and exhibit environmental dependencies. These difficulties stem mainly from limitations to the available imaging frequency bands, and a general lack of coherent diversity of the available target-related data. This paper proposes an active multimodal sensor system for target recognition and tracking, consisting of a visible, an infrared, and a hyperspectral sensor. The system makes full use of its multisensor information collection abilities; furthermore, it can actively control different sensors to collect additional data, according to the needs of the real-time target recognition and tracking processes. This level of integration between hardware collection control and data processing is experimentally shown to effectively improve the accuracy and robustness of the target recognition and tracking system.

Human frontoparietal cortex represents behaviorally relevant target status based on abstract object features

Searching for items that are useful given current goals, or "target" recognition, requires observers to flexibly attend to certain object properties at the expense of others. This could involve focusing on the identity of an object while ignoring identity-preserving transformations such as changes in viewpoint or focusing on its current viewpoint while ignoring its identity. To effectively filter out variation due to the irrelevant dimension, performing either type of task is likely to require high-level, abstract search templates. Past work has found target recognition signals in areas of ventral visual cortex and in subregions of parietal and frontal cortex. However, target status in these tasks is typically associated with the identity of an object, rather than identity-orthogonal properties such as object viewpoint. In this study, we used a task that required subjects to identify novel object stimuli as targets according to either identity or viewpoint, each of which was not predictable from low-level properties such as shape. We performed functional MRI in human subjects of both sexes and measured the strength of target-match signals in areas of visual, parietal, and frontal cortex. Our multivariate analyses suggest that the multiple-demand (MD) network, including subregions of parietal and frontal cortex, encodes information about an object's status as a target in the relevant dimension only, across changes in the irrelevant dimension. Furthermore, there was more target-related information in MD regions on correct compared with incorrect trials, suggesting a strong link between MD target signals and behavior. NEW & NOTEWORTHY Real-world target detection tasks, such as searching for a car in a crowded parking lot, require both flexibility and abstraction. We investigated the neural basis of these abilities using a task that required invariant representations of either object identity or viewpoint. Multivariate decoding analyses of our whole brain functional MRI data reveal that invariant target representations are most pronounced in frontal and parietal regions, and the strength of these representations is associated with behavioral performance.

Underwater Acoustic Target Recognition Based on Supervised Feature-Separation Algorithm

For the purpose of improving the accuracy of underwater acoustic target recognition with only a small number of labeled data, we proposed a novel recognition method, including 4 steps: pre-processing, pre-training, fine-tuning and recognition. The 4 steps can be explained as follows: (1) Pre-processing with Resonance-based Sparsity Signal Decomposition (RSSD): RSSD was firstly utilized to extract high-resonance components from ship-radiated noise. The high-resonance components contain the major information for target recognition. (2) Pre-training with unsupervised feature-extraction: we proposed a one-dimensional convolution autoencoder-decoder model and then we pre-trained the model to extract features from the high-resonance components. (3) Fine-tuning with supervised feature-separation: a supervised feature-separation algorithm was proposed to fine-tune the model and separate the extracted features. (4) Recognition: classifiers were trained to recognize the separated features and complete the recognition mission. The unsupervised pre-training autoencoder-decoder can make good use of a large number of unlabeled data, so that only a small number of labeled data are required in the following supervised fine-tuning and recognition, which is quite effective when it is difficult to collect enough labeled data. The recognition experiments were all conducted on ship-radiated noise data recorded using a sensory hydrophone. By combining the 4 steps above, the proposed recognition method can achieve recognition accuracy of 93.28%, which sufficiently surpasses other traditional state-of-art feature-extraction methods.

Application of a Vision-Based Single Target on Robot Positioning System

In this paper, we propose a Circular-ring visual location marker based on a global image-matching model to improve the positioning ability in the fiducial marker system of a single-target mobile robot. The unique coding information is designed according to the cross-ratio invariance of the projective theorem. To verify the accuracy of full 6D pose estimation using the Circular-ring marker, a 6 degree of freedom (DoF) robotic arm platform is used to design a visual location experiment. The experimental result shows in terms of small resolution images, different size markers, and long-distance tests that our proposed robot positioning method significantly outperforms AprilTag, ArUco, and Checkerboard. Furthermore, through a repeatable robot positioning experiment, the results indicated that the proposed Circular-ring marker is twice as accurate as the fiducial marker at 2–4 m. In terms of recognition speed, the Circular-ring marker processes a frame within 0.077 s. When the Circular-ring marker is used for robot positioning at 2–4 m, the maximum average translation error of the Circular-ring marker is 2.19, 3.04, and 9.44 mm. The maximum average rotation error is also 1.703°, 1.468°, and 0.782°.

Time Series Data Fusion Based on Evidence Theory and OWA Operator

Time series data fusion is important in real applications such as target recognition based on sensors' information. The existing credibility decay model (CDM) is not efficient in the situation when the time interval between data from sensors is too long. To address this issue, a new method based on the ordered weighted aggregation operator (OWA) is presented in this paper. With the improvement to use the Q function in the OWA, the effect of time interval on the final fusion result is decreased. The application in target recognition based on time series data fusion illustrates the efficiency of the new method. The proposed method has promising aspects in time series data fusion.

Toward Understanding Molecular Recognition between PRMTs and their Substrates

Protein arginine methylation is a widespread eukaryotic posttranslational modification that occurs with as much frequency as ubiquitinylation. Yet, how the nine different human protein arginine methyltransferases (PRMTs) recognize their respective protein targets is not well understood. This review summarizes the progress that has been made over the last decade or more to resolve this significant biochemical question. A multipronged approach involving structural biology, substrate profiling, bioorthogonal chemistry and proteomics is discussed.

Bayesian Update with Information Quality under the Framework of Evidence Theory

Bayesian update is widely used in data fusion. However, the information quality is not taken into consideration in classical Bayesian update method. In this paper, a new Bayesian update with information quality under the framework of evidence theory is proposed. First, the discounting coefficient is determined by information quality. Second, the prior probability distribution is discounted as basic probability assignment. Third, the basic probability assignments from different sources can be combined with Dempster's combination rule to obtain the fusion result. Finally, with the aid of pignistic probability transformation, the combination result is converted to posterior probability distribution. A numerical example and a real application in target recognition show the efficiency of the proposed method. The proposed method can be seen as the generalized Bayesian update. If the information quality is not considered, the proposed method degenerates to the classical Bayesian update.

CNN-LRP: Understanding Convolutional Neural Networks Performance for Target Recognition in SAR Images

Target recognition is one of the most challenging tasks in synthetic aperture radar (SAR) image processing since it is highly affected by a series of pre-processing techniques which usually require sophisticated manipulation for different data and consume huge calculation resources. To alleviate this limitation, numerous deep-learning based target recognition methods are proposed, particularly combined with convolutional neural network (CNN) due to its strong capability of data abstraction and end-to-end structure. In this case, although complex pre-processing can be avoided, the inner mechanism of CNN is still unclear. Such a "black box" only tells a result but not what CNN learned from the input data, thus it is difficult for researchers to further analyze the causes of errors. Layer-wise relevance propagation (LRP) is a prevalent pixel-level rearrangement algorithm to visualize neural networks' inner mechanism. LRP is usually applied in sparse auto-encoder with only fully-connected layers rather than CNN, but such network structure usually obtains much lower recognition accuracy than CNN. In this paper, we propose a novel LRP algorithm particularly designed for understanding CNN's performance on SAR image target recognition. We provide a concise form of the correlation between output of a layer and weights of the next layer in CNNs. The proposed method can provide positive and negative contributions in input SAR images for CNN's classification, viewed as a clear visual understanding of CNN's recognition mechanism. Numerous experimental results demonstrate the proposed method outperforms common LRP.

A Novel Deep-Learning Method with Channel Attention Mechanism for Underwater Target Recognition

The core of underwater acoustic recognition is to extract the spectral features of targets. The running speed and track of the targets usually result in a Doppler shift, which poses significant challenges for recognizing targets with different Doppler frequencies. This paper proposes deep learning with a channel attention mechanism approach for underwater acoustic recognition. It is based on three crucial designs. Feature structures can obtain high-dimensional underwater acoustic data. The feature extraction model is the most important. First, we develop a ResNet to extract the deep abstraction spectral features of the targets. Then, the channel attention mechanism is introduced in the camResNet to enhance the energy of stable spectral features of residual convolution. This is conducive to subtly represent the inherent characteristics of the targets. Moreover, a feature classification approach based on one-dimensional convolution is applied to recognize targets. We evaluate our approach on challenging data containing four kinds of underwater acoustic targets with different working conditions. Our experiments show that the proposed approach achieves the best recognition accuracy (98.2%) compared with the other approaches. Moreover, the proposed approach is better than the ResNet with a widely used channel attention mechanism for data with different working conditions.

Target Recognition in Infrared Circumferential Scanning System via Deep Convolutional Neural Networks

With an infrared circumferential scanning system (IRCSS), we can realize long-time surveillance over a large field of view. Recognizing targets in the field of view automatically is a crucial component of improving environmental awareness under the trend of informatization, especially in the defense system. Target recognition consists of two subtasks: detection and identification, corresponding to the position and category of the target, respectively. In this study, we propose a deep convolutional neural network (DCNN)-based method to realize the end-to-end target recognition in the IRCSS. Existing DCNN-based methods require a large annotated dataset for training, while public infrared datasets are mostly used for target tracking. Therefore, we build an infrared target recognition dataset to both overcome the shortage of data and enhance the adaptability of the algorithm in various scenes. We then use data augmentation and exploit the optimal cross-domain transfer learning strategy for network training. In this process, we design the smoother L1 as the loss function in bounding box regression for better localization performance. In the experiments, the proposed method achieved 82.7 mAP, accomplishing the end-to-end infrared target recognition with high effectiveness on accuracy.

Post-crossing segment of dI1 commissural axons forms collateral branches to motor neurons in the developing spinal cord

The dI1 commissural axons in the developing spinal cord, upon crossing the midline through the floor plate, make a sharp turn to grow rostrally. These post-crossing axons initially just extend adjacent to the floor plate without entering nearby motor columns. However, it remains poorly characterized how these post-crossing dI1 axons behave subsequently to this process. In the present study, to address this issue, we examined in detail the behavior of post-crossing dI1 axons in mice, using the Atoh1 enhancer-based conditional expression system that enables selective and sparse labeling of individual dI1 axons, together with Hb9 and ChAT immunohistochemistry for precise identification of spinal motor neurons (MNs). We found unexpectedly that the post-crossing segment of dI1 axons later gave off collateral branches that extended laterally to invade motor columns. Interestingly, these collateral branches emerged at around the time when their primary growth cones initiated invasion into motor columns. In addition, although the length of the laterally growing collateral branches increased with age, the majority of them remained within motor columns. Strikingly, these collateral branches further gave rise to multiple secondary branches in the region of MNs that innervate muscles close to the body axis. Moreover, these axonal branches formed presynaptic terminals on MNs. These observations demonstrate that dI1 commissural neurons develop axonal projection to spinal MNs via collateral branches arising later from the post-crossing segment of these axons. Our findings thus reveal a previously unrecognized projection of dI1 commissural axons that may contribute directly to generating proper motor output.

Selective Therapeutic Intervention: A Challenge against Off-Target Effects

Despite the massive global spend on biology-driven drug discovery, tackling the issue of side effects and adverse events resulting from drug promiscuity represents a persistent challenge. Although delivering authentic medical innovations today is more complex than ever, minimization of off-target effects should be a priority.

A Neural Network with Convolutional Module and Residual Structure for Radar Target Recognition Based on High-Resolution Range Profile

In the conventional neural network, deep depth is required to achieve high accuracy of recognition. Additionally, the problem of saturation may be caused, wherein the recognition accuracy is down-regulated with the increase in the number of network layers. To tackle the mentioned problem, a neural network model is proposed incorporating a micro convolutional module and residual structure. Such a model exhibits few hyper-parameters, and can extended flexibly. In the meantime, to further enhance the separability of features, a novel loss function is proposed, integrating boundary constraints and center clustering. According to the experimental results with a simulated dataset of HRRP signals obtained from thirteen 3D CAD object models, the presented model is capable of achieving higher recognition accuracy and robustness than other common network structures.

Target Recognition of SAR Images via Matching Attributed Scattering Centers with Binary Target Region

A target recognition method of synthetic aperture radar (SAR) images is proposed via matching attributed scattering centers (ASCs) to binary target regions. The ASCs extracted from the test image are predicted as binary regions. In detail, each ASC is first transformed to the image domain based on the ASC model. Afterwards, the resulting image is converted to a binary region segmented by a global threshold. All the predicted binary regions of individual ASCs from the test sample are mapped to the binary target regions of the corresponding templates. Then, the matched regions are evaluated by three scores which are combined as a similarity measure via the score-level fusion. In the classification stage, the target label of the test sample is determined according to the fused similarities. The proposed region matching method avoids the conventional ASC matching problem, which involves the assignment of ASC sets. In addition, the predicted regions are more robust than the point features. The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is used for performance evaluation in the experiments. According to the experimental results, the method in this study outperforms some traditional methods reported in the literature under several different operating conditions. Under the standard operating condition (SOC), the proposed method achieves very good performance, with an average recognition rate of 98.34%, which is higher than the traditional methods. Moreover, the robustness of the proposed method is also superior to the traditional methods under different extended operating conditions (EOCs), including configuration variants, large depression angle variation, noise contamination, and partial occlusion.