Real Target Research Articles

An Improved Sliding Matched Filter Method for Interrupted Sampling Repeater Jamming Suppression Based on Jamming Reconstruction

The development of the digital radio frequency memory (DRFM) has led to the interrupted sampling repeater jamming (ISRJ) becoming increasingly popular in electronic countermeasure (ECM). It is coherent with the emitted signal and extremely limits radar target detection which significantly obstructs radar electronic countercountermeasure (ECCM). In this paper, we study the ISRJ suppression for pseudo random code continuous wave (PRC-CW) radars. First, the relationship between the ISRJ and the radar waveform is obtained by analyzing the ISRJ principle. Second, the intermittent feature of the ISRJ with matched filter sliding is discussed and used to determine the retransmitted sampled slices (RSS). Third, the jamming signal is reconstructed using the minimum residual criterion and excluded from the echo signal. In the proposed method, we make the most of the information of the jamming signal for improving the anti-ISRJ performance in the low SNR regimes. This information pertains to the amplitude of the jamming signal being considerably higher than that of the real target signal. Fourth, utilizing this characteristic of the jamming signal, we propose an improved sliding matched filter method based on the RSS reconstruction. Last, numerical simulations illustrate the effectiveness of the proposed method and validation of the theoretical analysis.

Long Noncoding RNA lncR17454 Regulates Metamorphosis of Silkworm Through let-7 miRNA Cluster.

A number of long noncoding RNAs (lncRNAs) have been identified in silkworm, but little is known about their functions. Recent study showed that the let-7 miRNA cluster (contains let-7, miR-2795, and miR-100) was transcribed from the last exon of lncRNA lncR17454 in silkworm. To investigate the functional role of lncR17454, dsRNAs of lncR17454 were injected into the hemolymph of 1-d-old third-instar larvae of Bombyx mori, repression of lncR17454 led to molting arrestment during the larval–larval and larval–pupal transition of silkworm, which was consistent to the result as let-7 knockdown in other studies. The expression level of mature let-7, miR-100, and miR-2795 decreased 40%, 36%, and 40%, respectively, while the mRNA level of two predicted target genes of let-7, the Broad Complex isoform 2 (BR-C-Z2) and the BTB-Zinc finger transcription repression factor gene Abrupt (Ab), increased significantly after lncR17454 knockdown. In contrast, when adding the 20-Hydroxyecdysone (20E) to silkworm BmN4 cell lines, the expression level of lncR17454 and let-7 cluster all increased significantly, but the expression of Abrupt, the predicted target gene of let-7, was repressed. Dual-luciferase reporter assays confirmed Abrupt was the real target of let-7. Here we found that the lncRNA lncR17454 can play regulator roles in the metamorphosis of silkworm through let-7 miRNA cluster and the ecdysone signaling pathway, which will provide new clues for lepidopteran pest control.

Beliefs, Commitments, and Ad Baculum Arguments

Typically, an ad baculum argument is one where an arguer threatens a respondent in order to induce them to adopt a standpoint. It is a fallacy, a common account goes, because the power to impose a standpoint is irrelevant to its truth or acceptability. However, fallacies, if they are to be anything, ought at a minimum to be persuasive, and it is hard to see how an ad baculum might persuade. Employing an ad baculum just underscores how terrible someone’s reasons are. Despite this, cases of fallacious ad baculum arguments seem to exist, and this is a fact that requires some explanation. This paper offers an account where the real target of an ad baculum is an audience downstream from the initial ad baculum exchange. This means that the ad baculum consists of misrepresenting the quality of evidence by means of the forced adoption of a particular standpoint.

The Application Research of Visual Touch Based on Computer Vision in Artistic Visual Image Design

As a branch of artistic design, artistic visual image design focuses on the visual image, because the scope of artistic visual image design is relatively wide. This research takes ice flower as the research object. After a brief introduction to computer vision and visual touch, it focuses on the current situation of the application of ice flower graphics, the creative graphic design of dew ice flower, the artificial production method of ice flower painting, the method of subject research, the repetition and arrangement of elements, and the type of gradients for ice drawing graphic elements. In addition, the principle of the visual tracking algorithm is introduced through the formula, and the tracking accuracy and success rate of the algorithm in this paper and other algorithms are compared during the experiment, and the experiment shows the formation conditions of the ice flower. Experiments show that tracking results combined with deep features perform the best, with an overall average of 66.95%. The closer the tracking sample is to the real target, the better the tracking performance can be guaranteed. This also proves the effectiveness of using the characteristics of deep features to select candidate samples related to the target and also proves that deep features are more robust in dealing with various challenging factors.

Mitigation Measures for Windfarm Effects on Radar Systems

Windfarms can have a significant impact on radar systems, especially air surveillance radar. This is because it is usually designed to show only moving targets and cancel out stationary objects. However, the rotation of wind turbine blades can be detected by radar as a false flight or target. Clutter or interference caused by windfarms can reduce radar sensitivity in critical regions, making real targets disappear. This could in turn affect the deployment of windfarms and lead to the cancellation of future renewable energy projects and plans. This paper presents the different strategies and techniques used to mitigate the windfarm impact on radar systems. Precisely, we aim throughout this article to classify the main currently adopted approaches, view the recent trends and research directions in this field, and try to trace the advantages and drawbacks along with the challenges of deploying such schemes.

Low-SNR Infrared Point Target Detection and Tracking via Saliency-Guided Double-Stage Particle Filter.

Low signal-to-noise ratio (SNR) infrared point target detection and tracking is crucial to study regarding infrared remote sensing. In the low-SNR images, the intensive noise will submerge targets. In this letter, a saliency-guided double-stage particle filter (SGDS-PF) formed by the searching particle filter (PF) and tracking PF is proposed to detect and track targets. Before the searching PF, to suppress noise and enhance targets, the single-frame and multi-frame target accumulation methods are introduced. Besides, the likelihood estimation filter and image block segmentation are proposed to extract the likelihood saliency and obtain proper proposal density. Guided by this proposal density, the searching PF detects potential targets efficiently. Then, with the result of the searching PF, the tracking PF is adopted to track and confirm the potential targets. Finally, the path of the real targets will be output. Compared with the existing methods, the SGDS-PF optimizes the proposal density for low-SNR images. Using a few accurate particles, the searching PF detects potential targets quickly and accurately. In addition, initialized by the searching PF, the tracking PF can keep tracking targets using very few particles even under intensive noise. Furthermore, the parameters have been selected appropriately through experiments. Extensive experimental results show that the SGDS-PF has an outstanding performance in tracking precision, tracking reliability, and time consumption. The SGDS-PF outperforms the other advanced methods.

Device-Free Multitarget Localization With Weighted Intersection Multidimensional Feature for Passive UHF RFID

The device-free localization (DFL) can locate the target without requiring the target to carry any radio equipment, which has a broad application prospect in the field of indoor positioning. Radio frequency identification (RFID), as a key technology for IoT implementation, has been widely used in objects identification and tracking. RFID-based radio tomographic imaging (RTI) method has received extensive attention due to its low cost, excellent real-time performance and easy to deployment. However, the indoor multipath effect, which leads to unpredictable variations of the received signal strength (RSS), especially in multi-target positioning. It is still a challenging problem to recognize the locations of multiple targets effectively in the complex indoor environment. In this paper, a novel method of intersection multidimensional feature analysis is proposed to solve this problem. The target impact region is introduced to extract the effective intersections, and then the multidimensional feature information is analyzed to determine the region where the real target may exist. The location estimation of the target is obtained by assigning appropriate weights to each intersection in the region. Extensive experiments are conducted with results are demonstrating that the method achieves high localization accuracy and localization stability in different indoor scenarios, while maintaining low computational complexity.

Research on Target Imaging Scene Simulation for Photoelectronic Tracking

Abstract In this paper, the 3D visual display and generation of target in optoelectronic tracking are researched by simulation. 3ds max software is used to model the target, the target model is converted into code files by Deep Exploration and added into the system model. The key point is to associate the target model track and size ratio with the actual target to approach the real battlefield environment. The image data provided by the scene building is tracked by the internal image processing algorithm of photoelectric tracking such as the Canny-based edge tracking. The experiment will compare the tracking of the same target in same algorithm but different backgrounds, and the same backgrounds but different algorithm. Result can see that through the experimental data, the application of the simulated sky background, and the proportion relationship between real target and target in simulation scene, provides a high authenticity data support for the image processing algorithm of self-propelled gun photoelectric tracking system.

A Dual Stream Model for Anti-infrared Decoys: Exploiting Interaction and Contextual Relationship

The infrared decoy jamming release process has spatio-temporal correlation. In order to mine the spatio-temporal correlation between target and decoys, we propose a dual stream model for anti-decoys based on graph convolutional network and long short-term network. The method proposed models the interaction relationship of the target and decoys, and combines contextual information to infer the real target. Experimental results on our prepared dataset with ground truth labels demonstrate that the algorithm proposed outperforms existed anti-decoy jamming methods in various metrics.

Capabilities of an Acoustic Camera to Inform Fish Collision Risk with Current Energy Converter Turbines

A diversified energy portfolio may include marine energy in the form of current energy converters (CECs) such as tidal or in-river turbines. New technology development in the research stage typically requires monitoring for environmental effects. A significant environmental effect of concern for CECs is the risk of moving parts (e.g., turbine blades) colliding with animals such as fishes. CECs are installed in energetic locations in which it is difficult to operate sensors to fulfill monitoring requirements for informing collision risk. Collecting data (i.e., about blade strikes or near-misses) that inform interactions of fishes with CECs is usually attempted using active acoustic sensors or video cameras (VCs). Limitations of low-light conditions or water turbidity that preclude effective use of VCs are overcome by using high-resolution multibeam echosounders (or acoustic cameras (ACs)). We used an AC at two sites to test its ability to detect artificial and real fish targets and determine if strike, near-miss, and near-field behavior could be observed. Interactions with fish and artificial targets with turbines have been documented but strike confirmation with an AC is novel. The first site was in a tidal estuary with a 25 kW turbine and water clarity sufficient to allow VC data to be collected concurrently with AC data showing turbine blade strike on tethered artificial fish targets. The second site was a turbid, debris-laden river with a 5 kW turbine where only AC data were collected due to high water turbidity. Data collection at the second site coincided with downstream Pacific salmon (Oncorhynchus spp.) smolt migration. Physical fish capture downstream of the turbine was performed with an incline plane trap (IPT) to provide context for the AC observations, by comparing fish catches. Discrimination between debris and fishes in the AC data was not possible, because active movement of fishes was not discernable. Nineteen fishes were released upstream of the turbine to provide known times of possible fish/turbine interactions, but detection was difficult to confirm in the AC data. ACs have been used extensively in past studies to count large migratory fish such as Pacific salmon, but their application for small fish targets has been limited. The results from these two field campaigns demonstrate the ability of ACs to detect targets in turbid water and observe blade strikes, as well as their limitations such as the difficulty of distinguishing small fishes from debris in a high-energy turbid river. Recommendations are presented for future applications associated with CEC device testing.

Analysis of infrared polarization properties of targets with rough surfaces

To improve infrared polarization detection and identification probability, accurate infrared polarization properties of the targets are required. Traditional analytical models of infrared polarization ignore the shadowing effect caused by rough surfaces. Based on the surface microelement bidirectional reflectance distribution function (BRDF), the Stokes analytical model of the infrared degree of polarization (DOP) of a rough surface with a shadowing function is constructed. The effects of the incident angle and surface roughness on the polarization of metallic and non-metallic targets are quantitatively analyzed, revealing that the DOP of infrared spontaneous radiation decreases with increasing roughness of both metallic and non-metallic surfaces, with a greater decrease for the non-metallic surface. Under the same roughness and temperature conditions, the DOP of the infrared radiation of the metallic surface is greater than that of the non-metallic one. The DOP of infrared radiation reaches a peak value within a specific incident-angle range. Finally, experiments are carried out with both far-infrared (wavelength from 8um to 12um) and near-infrared (wavelength from 0.9 um to 1.7 um) polarization imaging systems. The experimental results show that infrared polarization properties of the targets are basically consistent with the theoretical analysis results. This research is of great significance for analyzing the polarization properties of real targets and processing DOP images.

Smoothing Linear Multi-Target Tracking Using Integrated Track Splitting Filter

Multi-target tracking (MTT) is a challenging issue due to an unknown number of real targets, motion uncertainties, and coalescence behavior of sensor (such as radar) measurements. The conventional MTT systems deal with intractable computational complexities because they enumerate all feasible joint measurement-to-track association hypotheses and recursively calculate the a posteriori probabilities of each of these joint hypotheses. Therefore, the state-of-art MTT system demands bypassing the entire joint data association procedure. This research work utilizes linear multi-target (LM) tracking to treat feasible target detections followed by neighbored tracks as clutters. The LM integrated track splitting (LMITS) algorithm was developed without a smoothing application that produces substantial estimation errors. Smoothing refines the state estimation in order to reduce estimation errors for an efficient MTT. Therefore, we propose a novel Fixed Interval Smoothing LMITS (FIsLMITS) algorithm in the existing LMITS algorithm framework to improve MTT performance. This algorithm initializes forward and backward tracks employing LMITS separately using measurements collected from the sensor in each scan. The forward track recursion starts after the smoothing. Therefore, each forward track acquires backward multi-tracks that arrived from upcoming scans (future scans) while simultaneously associating them in a forward track for fusion and smoothing. Thus, forward tracks become more reliable for multi-target state estimation in difficult cluttered environments. Monte Carlo simulations are carried out to demonstrate FIsLMITS with improved state estimation accuracy and false track discrimination (FTD) in comparison to the existing MTT algorithms.

Frameless Stereotactic Biopsy with Intraoperative Computed Tomography "Assessment of Efficacy and Real Target Registration Error".

Frameless stereotactic brain biopsy (FSB) with navigation system has been widely used. We reported preliminary experience of FSB with intraoperative computed tomography (iCT) and examined the usefulness of this novel adjuvant technique and real target registration error (rTRE) of FSB. The FSB with 5-aminolevulinic acid (5-ALA) and iCT was performed on 10 patients. The gadolinium-enhanced lesions on magnetic resonance image were defined as the biopsy target. In the procedure, iCTs were scanned twice, for autoregistration of the navigation system and for confirmation of the position of the actual inserted biopsy needle. The red fluorescence of the samples was observed under excitation with violet-blue light through a low-cut filter of neurosurgical microscope. The distance between the planned target and the tip of the biopsy needle in the image of iCT was calculated in a workstation for the assessment of rTRE. The median volume of the target was 12.13 mL (0.06-39.15 mL). We performed the surgical procedure in a prone position in four patients. None to faint 5-ALA-induced fluorescence was observed in six samples. There existed no sampling errors. The mean target distance between the planned and real targets of the mean rTRE of FSB was 2.7 ± 0.56 mm. The real TRE of FSB was first reported and was larger than the reported rTRE exactly calculated from the fiducial registration error. iCT guarantees accurate tumor sampling with autoregistration regardless of the surgical position and prevents inaccurate biopsy to occur even with ALA fluorescence assistance.

Local Spatial-Temporal Matching Method for Space-Based Infrared Aerial Target Detection.

The feature of a space-based infrared signal is that the intensity of clutter is much stronger than that of an aerial target. Such a feature poses a great challenge to aerial target detection since the existing infrared target detection methods are prone to enhance clutter but ignore the real target, which results in missed detection and false alarms. To tackle the challenge, we propose a concise method based on local spatial–temporal matching (LSM). Specifically, LSM mainly consists of local normalization, local direction matching, spatial–temporal joint model, and inverse matching. Local normalization aims to enhance the target to the same strength as the clutter, so that the weak target will not be ignored. After normalization, a direction-matching step is applied to estimate the moving direction of the background between the basic frame and referenced frame. Then the spatial–temporal joint model is constructed to enhance the target and suppress strong clutter. Similarly, inverse matching is conducted to further enhance the target. Finally, a salience map is obtained, on which the aerial target is extracted by the adaptive threshold segmentation. Experiments conducted on four space-based infrared datasets indicate that LSM handles the above challenge and outperforms seven state-of-the-art methods in space-based infrared aerial target detection.

Cover Feature: Post‐Synthetic Macrocyclization of Rotaxane Building Blocks (ChemPlusChem 3/2022)

The Cover Feature shows a night sky photo from dusk until dawn illustrating relative orbital motions of planets and stars in universe due to gravitational interactions. Far in the whirl of light patterns are [2]rotaxane scaffolds that may undergo cyclization. Coming to the foreground, the resulting appealing cyclic interlocked molecular architectures are the real star molecular targets of the review, furthermore acting as molecular shuttles for some of them. More information can be found in the Review by Frédéric Coutrot, and co-workers.

A field experiment characterizing variable detection rates during plant surveys.

Surveys aimed at finding threatened and invasive species can be challenging due to individual rarity and low and variable individual detection rates. Detection rate in plant surveys typically varies due to differences among observers, among the individual plants being surveyed (targets), and across background environments. Interactions among these 3 components may occur but are rarely estimated due to limited replication and control during data collection. We conducted an experiment to investigate sources of variation in detection of 2 Pilosella species that are invasive and sparsely distributed in the Alpine National Park, Australia. These species are superficially similar in appearance to other yellow‐flowered plants occurring in this landscape. We controlled the presence and color of flowers on target Pilosella plants and controlled their placement in plots, which were selected for their variation in cover of non‐target yellow flowers and dominant vegetation type. Observers mimicked Pilosella surveys in the plots and reported 1 categorical and 4 quantitative indicators of their survey experience level. We applied survival analysis to detection data to model the influence of both controlled and uncontrolled variables on detection rate. Orange‐ and yellow‐flowering Pilosella in grass‐ and heath‐dominated vegetation were detected at a higher rate than nonflowering Pilosella. However, this detection gain diminished as the cover of other co‐occurring yellow‐flowering species increased. Recent experience with Pilosella surveys improved detection rate. Detection experiments are a direct and accessible means of understanding detection processes and interpreting survey data for threatened and invasive species. Our detection findings have been used for survey planning and can inform progress toward eradication. Interaction of target and background characteristics determined detection rate, which enhanced predictions in the Pilosella eradication program and demonstrated the difficulty of transferring detection findings into untested environments.

Image denoising and enhancement strategy based on polarization detection of space targets

Polarization detection of space targets is one of the most important research directions in the field of space target recognition. In view of the fact that there are problems such as strong background noise and inconspicuous details of contour features in the polarization image of space targets, an image denoising and enhancement strategy is proposed. To solve the problem of high intensity of Gaussian noise in degree of polarization (DoP) images, a denoising method named adaptive noise template prediction (ANTP) is proposed to eliminate the noise. Compared to the existing methods, the ANTP algorithm performs better at reducing noise and improving image quality. Aiming at the difficulty of separating the background noise from angle of polarization (AoP) images, a denoising method named gray analysis of local area (GALA) is proposed. Compared to traditional methods, the GALA algorithm can effectively extract the contour features of targets and improve the contrast of AoP images. An image fusion method based on discrete cosine transform and local spatial frequency (LSF) is used to fuse the denoised DoP image and AoP image. The experimental results of the simulated and real space target polarization detection confirm the effectiveness of our proposed strategy.

A sidelobe suppression algorithm for 77 GHz MIMO radars

77 GHz radar has become a promising approach to enhance automotive safety by quickly detecting and identifying targets around the vehicle, especially in harsh weather conditions. This requires 77 GHz radars to provide environmental imaging with high resolution and reliability. However, radar images are easily blurred by sidelobes and background noises, which makes it difficult to extract real target information. In this paper, a sidelobe suppression algorithm based on the point spread function (PSF) and complex-valued neural network has been proposed to discriminate and suppress unwanted sidelobes while maintaining mainlobes referring to targets. To overcome the scarcity of real-world 77 GHz multiple-input multiple-output (MIMO) radar datasets, this paper derives the formula of PSF for 77 GHz MIMO radars in detail and exploits the PSF to generate simulated datasets for training. In addition, to be compatible with the complex-valued radar datasets, a customized neural network model has also been established in this paper. The well-trained neural network is further adopted to suppress sidelobes on real-world radar images. Comprehensive simulations and measurements have proved the superior performance of the proposed method, especially in cases with low signal-to-noise ratio (SNR), large channel mismatches, small target Radar Cross-Section (RCS) and large observation angle.

A sensitive carbon monoxide sensor for industrial process control based on laser absorption spectroscopy with a 2.3 μm distributed feedback laser

Carbon monoxide (CO) is a ubiquitous atmospheric trace gas produced by natural and anthropogenic sources. It can be used as a quantitative marker to help us understand the different production processes. A near-infrared laser absorption spectroscopy of 2.3 μm based in-situ, real-time and continuous CO sensor equipment is developed for the exhaust pipeline of furnace. High sensitivity is accomplished utilizing the ratio between the second and first harmonic signals detection (WMS-2f/1f). The analyzer is housed in an explosion-proof enclosure and installed at both sides of the pipeline with a diameter of 2.1 m. The measured gas temperature inside the pipeline reaches around 400 K. The results primarily show a more linear response and less error, the measurement precision and the minimum detection limit are 0.42 ppm and 1.29 ppm (1σ), simultaneously. Evaluation of the data suggested a dynamic range from 3.87 ppm to 1.3% based on the 3σ rule and maximum absorbance of one. An operator can analyze the industrial production process clearly, that is, the measured CO concentration is negatively correlated with excess oxygen (O2) by observing the measurement results within 10 h. Meanwhile, the measurements of the real targets show that the sensor has great applicability and potential for exhaust pipeline and combustion control systems.

Infrared Small Target Detection Based on Gradient-Intensity Joint Saliency Measure

Small target detection is an arduous mission in infrared search and tracking (IRST) system, especially when the target signal is disturbed by high-intensity background clutters. In view of this situation, this paper presents a robust target detection algorithm based on gradient-intensity joint saliency measure (GISM) to gradually eliminate complex background clutter. Because of thermal remote sensing imaging, the infrared target usually occupies a small area that accords with the optics point spread function (PSF), so it can be distinguished from the background clutter in both gradient and intensity properties. According to this, firstly, the original image is transformed into a gradient map, and the gradient saliency measure (GSM) is calculated to highlight the target signal and suppress the sharp edge clutter, so the candidate targets can be reliably extracted by using the maximum entropy principle. Secondly, the local intensity saliency measure (LISM) is obtained by calculating the gray difference between each candidate region and its local surroundings, so as to preserve the real target and remove intense structural clutter such as black holes or corners. Finally, by fully integrating the gradient and intensity properties, the GISM defined by LISM weighted GSM map can efficiently identify the real target signal and eliminate false alarms. Experimental results prove that the proposed method not only has advantages in background clutter suppression and small target enhancement, but also has reasonable time consumption.