Real Target Research Articles

Research on ADS-B spoof detection technology based on radio spectrum features

Abstract This paper discusses and analyzes the 1090ES mode ADS-B and examines the airborne ADS-B transponder and ground monitoring sensing nodes. The ADS-B signal receiver structure converts the received radio signal to a zero IF signal for digital filtering to obtain phase information. The cognitive radio spectrum monitoring technology mainly uses periodic characteristic monitoring to maximize the network's security in the era of artificial intelligence. For a set of ADS-B messages from the same source, the correlation coefficients of the two variation amounts are carried out to discriminate between real targets and spoofed interference, and the source consistency detection method is used to reduce the probability of false alarms. The results show that when the number of signal bars is greater than 11, the correlation value of the real signal is greater than 0.61, while the correlation value of the spoofed signal is around 0.22, which can be better distinguished.

An Infrared Small Target Detection Method Based on Attention Mechanism.

The human visual attention system plays an important role in infrared target recognition because it can quickly and accurately recognize infrared small targets and has good scene adaptability. This paper proposes an infrared small target detection method based on an attention mechanism, which consists of three modules: a bottom-up passive attention module, a top-down active attention module, and decision feedback equalization. In the top-down active attention module, given the Gaussian characteristics of infrared small targets, the idea of combining knowledge-experience Gaussian shape features is applied to implement feature extraction, and quaternion cosine transform is performed to achieve multi-dimensional fusion of Gaussian shape features, thereby achieving complementary fusion of multi-dimensional feature information. In the bottom-up passive attention module, considering that the difference in contrast and motion between the target and the background can attract attention easily, an optimal fast local contrast algorithm and improved circular pipeline filtering are adopted to find candidate target regions. Meanwhile, the multi-scale Laplacian of the Gaussian filter is adopted to estimate the optimal size of the infrared small target. The fast local contrast algorithm based on box filter acceleration and structure optimization is employed to extract local contrast features, and candidate target regions can be obtained by using an adaptive threshold. Besides, the mean gray, target size, Gaussian consistency, and circular region constraint are used in pipeline filtering to extract motion regions, and the false-alarm rate is reduced effectively. Finally, decision feedback equalization is adopted to obtain real targets. Experiments are conducted on some real infrared images involving complex backgrounds with sea, sky, and ground clutters, and the experimental results indicate that the proposed method can achieve better detection performance than conventional baseline methods, such as RLCM, ILCM, PQFT, MPCM, and ADMD. Also, mathematical proofs are provided to validate the proposed method.

A Multiple-False-Target ISAR Shape Deception Jamming Method Based on Improved Template Multiplication Modulated Time-Delay Algorithm and Sub-Nyquist Sampling

In recent decades, the deception jamming approach based on digital radio frequency memory (DRFM) for inverse synthetic aperture radar (ISAR) has been a well-researched topic. Various types of jamming signals based on interrupted-sampling repeater jamming (ISRJ) can induce one-dimensional multiple high-resolution range profile (HRRP) false targets or two-dimensional realistic multiple ISAR false targets for deception. However, these existing methods generate false targets that are identical to the real target. The ISRJ false target also generates a main false target whose energy is much higher than other sub-false targets. Thus, it is easy to discover that the radar has suffered jamming. In order to generate better, more confusing jamming signals, this paper proposes a jamming method based on sub-Nyquist sampling jamming to induce realistic, multiple false targets on ISAR images. It improves a template multiplication modulated time-delay method to eliminate and add scatterers on the selected false target to change its ISAR shape. The frequency-shift parameters of the template jamming signal are analyzed and derived in detail for eliminating and adding scatterers at the specified location. Thus, multiple false targets with different ISAR shapes and similar energy are generated, which can create better deception effects. Meanwhile, this method can adjust the number of false targets and the location of changed-shape false targets. Furthermore, the resolution of the false target can adaptively change with the radar pulse width and the accumulated pulse number. The simulation results show that the proposed deception jamming strategy works.

Feasibility of Using a Single Planning MRI for Two-Insertion Intracavitary Brachytherapy for Cervical Cancer

MRI is an essential tool in treatment planning for cervical cancer patients undergoing definitive chemoradiation involving brachytherapy. However, due access to MRI for many patients may be limited. We sought to examine the feasibility and quality of treatment planning for organs at risk (OAR) and target volumes of a single treatment planning MRI for a planned two-insertion HDR intracavitary cervical brachytherapy. Eighteen patients with cervical cancer who underwent MRI-based treatment planning for intracavitary brachytherapy were selected for analysis. Patients underwent two intracavitary applicator insertions using a tandem and ring applicator (TnR_1 and TnR_2) with both CT (for digitization only) and MRI following each insertion and two fractions delivered with each insertion (total 7 Gy x 4fx). MRI-based TnR_2 plans were retrospectively replanned with the following procedure: 1) register MRI from TnR_1 to TnR_2 CT, 2) transfer cervix contour (Cx_simul) and plan from TnR_1 to TnR_2 CT, simulating blinding from TnR_2 MRI, 3) crop Cx_simul from TnR_2 OARs, 4) adjust plan only to meet OAR constraints or clinical doses if clinical plan (TnR_2 original MRI plan summed with TnR_1 and EBRT doses) violates constraints. Coverage changes (∆_D90%) in actual MRI defined TnR_2 cervix (Cx_real) was compared to clinical plans and evaluated against our clinical target goal: >85 Gy EQD2 (EBRT+BT). Differences between Cx_simul and Cx_real contours were evaluated with Dice Similarity Coefficient (DSC). Changes in bladder, rectum, rectosigmoid, and bowel D2cc was evaluated. Procedure time was recorded to determine time added by MRI (time between CT and final MRI timestamps; n = 29). Plan modification to meet OAR constraints was required for 5/18 plans. Cx_real target coverage from clinical to simulated plans decreased in 7/18 plans (∆_D90% = 1.7±6%). Composite EQD2 was <85 Gy in 2/18 cases, with ∆_D90% of -1.3% (clinical EQD2<85 Gy for this case also) and -7.3%, which was due to inconsistent cervix shape between insertions 1 and 2. Cervix DSC values between Cx_simul and Cx_real ranged from 0.68-0.88 (mean = 0.78 ± 0.06). Bladder, rectum, rectosigmoid, and bowel D2cc increased on average 4 ± 12%, 3 ± 9%, 5 ± 10%, and 5 ± 17%, respectively. Including planning MRI increased procedure time by 41.8 ± 10.4 min. The proposed method of utilizing TnR_1 MRI contours for TnR_2 CT planning reduces procedure time and provides reasonable target coverage for most cases. OAR assessment varies between imaging modality which likely represents variability in OAR position rather than treatment planning modality. Next steps are to increase patients studied, and implement this technique prospectively while waiting for TnR_2 MRI completion and compare the clinical TnR_2 MRI plan with the TnR_2 CT plan created with Cx_simul. This will provide further confidence in validity of the approach and ensure plan quality is maintained prior to full implementation.

TiO2-guanine as a new amalgamation compoundfor fabrication of a disposable biosensor with high sensitivity and rapid detection of H1N1 swine flu virus.

A TiO2-guanine nanocomposite (TG NC)-based electrochemical biosensor was immobilized with hemagglutinin (HA) gene specific probe with 5' NH2 group on screen-printed gold electrode (probe(ss)DNA-TG-SPGE). The modified biosensor was examined for H1N1 swine flu virus. TG NCs along with precursors were characterized spectroscopically and morphologically by employing several approaches. Electrochemical investigations were performed with the help of cyclic voltammetric (CV) and electrochemical impedance spectroscopy (EIS) in 0.1 M phosphate buffer saline (PBS; pH 7.4) with 1 µM methylene blue (MB) redox indicator. For better detection of single-stranded virus DNA, the modified electrode was optimized at various concentrations, pH, and scan rates. The modified biosensor showed high sensitivity (40.32 μA/ng.cm2), low LOD (0.00024 ng/6 µL), and broad linear range 0.0002-20 ng/6µL with coefficient of determination ofR2=0.9981 for H1N1 virus detection. The HA gene-modified biosensor presented decent stability and specificity against different infectious pathogens including H3N2 virus and human DNA with negative response. Furthermore, the modified biosensor also responded well for real sample target DNA detection with a recovery of >96%. The simply designed HA gene-modified biosensor transduces decreased current response towards target-specific (ss)DNA binding and could be used as a rapid detection tool for H1N1 swine flu virus diagnosis.

Express Visuomotor Responses Reflect Knowledge of Both Target Locations and Contextual Rules during Reaches of Different Amplitudes.

When humans reach to visual targets, extremely rapid (∼90 ms) target-directed responses can be observed in task-relevant proximal muscles. Such express visuomotor responses are inflexibly locked in time and space to the target and have been proposed to reflect rapid visuomotor transformations conveyed subcortically via the tecto-reticulo-spinal pathway. Previously, we showed that express visuomotor responses are sensitive to explicit cue-driven information about the target, suggesting that the express pathway can be modulated by cortical signals affording contextual prestimulus expectations. Here, we show that the express visuomotor system incorporates information about the physical hand-to-target distance and contextual rules during visuospatial tasks requiring different movement amplitudes. In one experiment, we recorded the activity from two shoulder muscles as 14 participants (6 females) reached toward targets that appeared at different distances from the reaching hand. Increasing the reaching distance facilitated the generation of frequent and large express visuomotor responses. This suggests that both the direction and amplitude of veridical hand-to-target reaches are encoded along the putative subcortical express pathway. In a second experiment, we modulated the movement amplitude by asking 12 participants (4 females) to deliberately undershoot, overshoot, or stop (control) at the target. The overshoot and undershoot tasks impaired the generation of large and frequent express visuomotor responses, consistent with the inability of the express pathway to generate responses directed toward nonveridical targets as in the anti-reach task. Our findings appear to reflect strategic, cortically driven modulation of the express visuomotor circuit to facilitate rapid and effective response initiation during target-directed actions.SIGNIFICANCE STATEMENT Express (∼90 ms) arm muscle responses that are consistently tuned toward the location of visual stimuli suggest a subcortical contribution to target-directed visuomotor behavior in humans, potentially via the tecto-reticulo-spinal pathway. Here, we show that express muscle responses are modulated appropriately to reach targets at different distances, but generally suppressed when the task required nonveridical responses to overshoot/undershoot the real target. This suggests that the tecto-reticulo-spinal pathway can be exploited strategically by the cerebral cortex to facilitate rapid initiation of effective responses during a visuospatial task.

Fractional-Order Total Variation Geiger-Mode Avalanche Photodiode Lidar Range-Image Denoising Algorithm Based on Spatial Kernel Function and Range Kernel Function

A Geiger-mode avalanche photodiode (GM-APD) laser radar range image has much noise when the signal-to-background ratios (SBRs) are low, making it difficult to recover the real target scene. In this paper, based on the GM-APD lidar denoising model of fractional-order total variation (FOTV), the spatial relationship and similarity relationship between pixels are obtained by using a spatial kernel function and range kernel function to optimize the fractional differential operator, and a new FOTV GM-APD lidar range-image denoising algorithm is designed. The lost information and range anomalous noise are suppressed while the target details and contour information are preserved. The Monte Carlo simulation and experimental results show that, under the same SBRs and statistical frame number, the proposed algorithm improves the target restoration degree by at least 5.11% and the peak signal-to-noise ratio (PSNR) by at least 24.6%. The proposed approach can accomplish the denoising of GM-APD lidar range images when SBRs are low.

Infrared moving small target detection based on spatial-temporal local contrast under slow-moving cloud background

It is intractable to detect infrared moving small targets under slow-moving clouds, because small targets are weak so that they may be submerged in clouds, in addition to cloud edges are difficult to distinguish from them. To overcome these obstacles, a robust spatial–temporal local contrast method for infrared moving small targets is proposed. Firstly, the spatial–temporal local energy change saliency (STLECS) map is designed to discriminate whether interframe local energy changes are caused by cloud slow-motion or target motion. In addition, a multi-directional interval minimum gradient (MDIMG) map is also constructed to measure the difference between the target and the background edge on the gradient distribution. To further enhance the saliency of moving targets in space–time domain, a fused spatial -temporal saliency (FSTS) map is obtained by combining the STLECS map and the MDIMG map, and an adaptive threshold is applied to this fused map to segment real targets. The performance experiment results based on real images show that the proposed method has the better performance compared to other state-of-the-art contrast-based methods.

What impacts the acceptability of wearable devices that detect opioid overdose in people who use opioids? A qualitative study.

Drug-related deaths involving an opioid are at all-time highs across the United Kingdom. Current overdose antidotes (naloxone) require events to be witnessed and recognised for reversal. Wearable technologies have potential for remote overdose detection or response but their acceptability among people who use opioids (PWUO) is not well understood. This study explored facilitators and barriers to wearable technology acceptability to PWUO. Twenty-four participants (79% male, average age 46 years) with current (n = 15) and past (n = 9) illicit heroin use and 54% (n = 13) who were engaged in opioid substitution therapy participated in semi-structured interviews (n = 7) and three focus groups (n = 17) in London and Nottingham from March to June 2022. Participants evaluated real devices, discussing characteristics, engagement factors, target populations, implementation strategies and preferences. Conversations were recorded, transcribed and thematically analysed. Three themes emerged: device-, person- and environment-specific factors impacting acceptability. Facilitators included inconspicuousness under the device theme and targeting subpopulations of PWUO at the individual theme. Barriers included affordability of devices and limited technology access within the environment theme. Trust in device accuracy for high and overdose differentiation was a crucial facilitator, while trust between technology and PWUO was a significant environmental barrier. Determinants of acceptability can be categorised into device, person and environmental factors. PWUO, on the whole, require devices that are inconspicuous, comfortable, accessible, easy to use, controlled by trustworthy organisations and highly accurate. Device developers must consider how the type of end-user and their environment moderate acceptability of the device.

The Development of the Real Time Target Simulator for the RF Signal of Electronic Warfare using VST and FPGA

In this paper, the target simulator for RF signals was developed by using VST(Vector Signal Transceiver) and set by real-time signal processing SW programs. A function to process RF signals using FPGA(Field Programmable Gate Array) board was designed. The system functions capable of data processing, raw signals monitoring, target signals(simulated range, velocity) generating and RF environments data analyzing were implemented. And the characteristics of modulated signal were analyzed in RF environment. All function of programs for processing RF signal have options to store signal data and to manage the data. The validity of the signal simulation was confirmed through verification of simulated signal results.

Research on 3D Particle Field Reconstruction Method Based on Deep Learning

Reconstructing 3D particle fields from single holograms is an important topic in the computer vision field. To increase network accuracy, we incorporate a channel attention mechanism based on the U-Net architecture in this research; meanwhile, we use the LeakyReLU activation function to accelerate the network convergence. Deep learning is used to extract information from a single hologram that can recreate the 3D particle field. The network receives the digital hologram as input, and the radius and 3D locations of the particles are converted into 2D grayscale images as real labels. Simulation and experimental results show that 2D grayscale images with clear edge textures can be rapidly encoded using the neural network, and the average SSIM and PSNR of the network output results with the real target on the test data set can reach 0.989 and 32.56.

Adaptive Refining-Aggregation-Separation Framework for Unsupervised Domain Adaptation Semantic Segmentation

Unsupervised domain adaptation has attracted widespread attention as a promising method to solve the labeling difficulties of semantic segmentation tasks. It trains a segmentation network for unlabeled real target images using easily available labeled virtual source images. To improve performance, clustering is used to obtain domain-invariant feature representations. However, most clustering-based methods indiscriminately cluster all features mapped by category from both domains, causing the centroid shift and affecting the generation of discriminative features. We propose a novel clustering-based method that uses an adaptive refining-aggregation-separation framework, which learns the discriminative features by designing different adaptive schemes for different domains and features. The clustering does not require any tunable thresholds. To estimate more accurate domain-invariant centroids, we design different ways to guide the adaptive refinement of different domain features. A critic is proposed to directly evaluate the confidence of target features to solve the absence of target labels. We introduce a domain-balanced aggregation loss and two adaptive separation losses for distance and similarity respectively, which can discriminate clustering features by combining the refinement strategy to improve segmentation performance. Experimental results on GTA5→Cityscapes and SYNTHIA→Cityscapes benchmarks show that our method outperforms existing state-of-the-art methods.

Random-Frequency-Coded Waveform Optimization and Signal Coherent Accumulation Against Compound Deception Jamming

Among the various types of jamming, active deception jamming has similar characteristics to the transmitted signal. The jamming seriously affects the detection and tracking of real targets by radar. In recent years, waveform design has become an effective anti-jamming method with the maturity of waveform generator technology. However, most anti-jamming waveforms are only designed to combat a single kind of jamming. To compensate for the shortage of existing waveforms that cannot simultaneously combat compound deception jamming, we construct an interpulse-intrapulse joint frequency coded(I-IJFC) anti-jamming waveform and analyze the anti-jamming process flow. Moreover, to reduce the influence of residual jamming, we propose an intrapulse frequency-coded method based on jamming parameters estimation. The waveform can be used against cross-pulse repeater deception jamming(CRDJ) and interrupted sampling jamming(ISJ). Then, to solve the problem that the existing algorithm cannot realize the coherent accumulation of the proposed waveform, we propose a coherent accumulation algorithm of subpulse rearrangement compensation and nonuniform discrete Fourier transform (SRCNDFT). The simulation examples show that the waveform proposed in this paper performs better against compound deception jamming than existing waveforms, and the proposed algorithm can realize the coherent accumulation of I-IJFC echo signals.

Multi-Dimensional Bisection Method in HIL Environment: Stability and Chatter Prediction in Turning

In turning operations, the harmful small-amplitude but high-frequency chatter vibrations are identified in hardware-in-the-loop (HIL) experimental environment by means of the application of a multi-dimensional bisection method. The dummy workpiece clamped to the real main spindle is excited by contactless electromagnetic actuators and the response of the workpiece is detected by laser-based sensors. According to the present and the stored previous positions of the rotating workpiece, the desired cutting force characteristic along with the surface regeneration effect can be emulated by means of a high-performance real target computer. While the conventional experimental results in the HIL environment identify the stability limits of the cutting operation accurately only in a high-resolution grid of the technological parameters, the embedded bisection method reduces significantly both the size of the required grid and the time duration of the measurement by path following the boundaries of the linear loss of stability. Based on this technique, the experimental stability boundary of the emulated turning process is presented in a wide range of spindle speeds.

Anti-occlusion tracker for infrared ground targets based on improved trajectory prediction network

Aiming at the problem of infrared ground target tracking drift or even failure caused by background occlusion, we propose an anti-occlusion tracker for infrared ground targets based on improved trajectory prediction network. First, the occlusion judgment criterion is proposed to accurately determine the occlusion of the infrared ground target. Then, based on the BiTraP trajectory prediction network, on the one hand, by introducing velocity information through the Siamese network structure and using one-way prediction, the SiamUniTraP trajectory prediction network is proposed, which improves the precision of trajectory prediction. On the other hand, by improving the training method and application method, it can predict the trajectory of ground target more accurately. For short-term occlusions, the SiamUniTraP network is employed to predict the bounding box for target occlusion based on temporal context information. For long-term occlusions, the search expansion strategy is proposed to handle the accumulation of prediction errors caused by the lack of real target information. Finally, the “second determination” criterion is proposed to achieve accurate recapture of the target and return to normal tracking. Infrared image sequence tests are conducted under occlusion conditions. Compared with the SuperDiMP tracker, the proposed tracker improves the success rate by 5.2% and the accuracy by 5.9% under the OPE evaluation index. This indicates that the proposed tracker has good robustness for infrared ground target tracking in occlusion situations, especially in full occlusion situations.

The DISCS method for perforation simulation of thin metallic plates by a rigid projectile

The DISCS method had been developed for penetration analysis of an axisymmetric projectile with a slender nose into a semi-infinite medium. Recently, the authors developed an approximate approach to assess perforation of concrete slabs of finite thickness, using the DISCS method. The present paper extends the approximate model to the perforation analysis of ductile metallic thin plates and determines the key parameters of the projectile residual velocity, the target perforation limit, and the ballistic limit. The present investigation focuses on thin metallic plates, the thickness of which is considerably smaller than that of the concrete slabs and smaller in comparison to the projectile nose length. The method is rather simple, easy to implement and provides a fast solution. An idealized target is characterized by a set of discs of small thickness responding in the plate plane, disregarding the local damage around the penetration ductile hole. The in-plane response of the discs during penetration and perforation of an idealized target are calculated. The Residual Velocity upon perforation of the Idealized Target (RVIT) is lower than the residual velocity of the real plate since it disregards the local damage, hence, the RVIT is the lower bound of the true residual velocity of the real perforated thin plate. Therefore, a positive RVIT for the thin plate with a given thickness indicates right away that the real target will be perforated. Using the RVIT parameter, the proposed DISCS method turns into a series of RVIT-perforation problems of plates with thicknesses that are smaller than the real thickness, to determine the decreasing RVIT with increasing thickness, until the full thickness is considered. The analysis is carried out using the modified (hybrid) DISCS method, that aims at analyzing penetration depth into a thick target. The analysis is based on the theoretical field equations and the constitutive equations of the plate material. The proposed method is based on a theoretical model and on engineering insight. Neither empirical constants nor any calibrations are required. Validation of the proposed method is carried out thorough comparisons with numerous test data on ductile aluminum and steel plates of different thicknesses that are struck by rigid projectiles at different impact velocities. All the comparisons demonstrate very good correspondence between the proposed method predictions and the test data.

Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation.

Breast cancer is commonly diagnosed with mammography. Using image segmentation algorithms to separate lesion areas in mammography can facilitate diagnosis by doctors and reduce their workload, which has important clinical significance. Because large, accurately labeled medical image datasets are difficult to obtain, traditional clustering algorithms are widely used in medical image segmentation as an unsupervised model. Traditional unsupervised clustering algorithms have limited learning knowledge. Moreover, some semi-supervised fuzzy clustering algorithms cannot fully mine the information of labeled samples, which results in insufficient supervision. When faced with complex mammography images, the above algorithms cannot accurately segment lesion areas. To address this, a semi-supervised fuzzy clustering based on knowledge weighting and cluster center learning (WSFCM_V) is presented. According to prior knowledge, three learning modes are proposed: a knowledge weighting method for cluster centers, Euclidean distance weights for unlabeled samples, and learning from the cluster centers of labeled sample sets. These strategies improve the clustering performance. On real breast molybdenum target images, the WSFCM_V algorithm is compared with currently popular semi-supervised and unsupervised clustering algorithms. WSFCM_V has the best evaluation index values. Experimental results demonstrate that compared with the existing clustering algorithms, WSFCM_V has a higher segmentation accuracy than other clustering algorithms, both for larger lesion regions like tumor areas and for smaller lesion areas like calcification point areas.

Research on an Intra-Pulse Orthogonal Waveform and Methods Resisting Interrupted-Sampling Repeater Jamming within the Same Frequency Band

Interrupted-sampling repeater jamming (ISRJ) is a kind of intra-pulse coherent deception jamming that can generate false target peaks in the range profile and interfere with the detection and tracking of real targets. In this paper, an anti-ISRJ method based on the intra-pulse orthogonal waveform is proposed, which can recognize common interference signals by comparing sub-signal matched filtering results. For some special scenes where real targets cannot be directly differentiated from false targets, a new recognition method based on the energy discontinuity of the interference signal in the time domain is proposed in this paper. The method proposed in this paper can recognize real and false targets in all ISRJ modes without any prior information, such as jammer parameters, with a small amount of calculation, which is suitable for actual radar systems. Simulation experiments using different interference parameters show that although this method has a 3 dB loss of pulse compression gain, it can completely suppress different kinds of ISRJ interference when the SNR before pulse compression is higher than −20 dB, with 100% target detection probability.

Cyberknife treatment for different types of tumor

CyberKnife radiosurgery is a frameless, image guided robotic stereotactic radiosurgical system (IGR-SRS) which uses potent, ablative radiation dose with limited treatment volume. The main aim of this study is to provide knowledge of this treatment for different types of tumor. To deliver radiation treatment from multiple angles, with real time target tracking through a combined orthogonal radiograph imaging and optic motion tracking system cyberKnife is a new and advanced technique. Radiation is delivered to the tumor within a predetermined range, for this an object recognition computer algorithm and a fudicial marker placed within a tumor. CyberKnife treatment cost USD 6,800 (Indian Rupees approx. 4,89,000). In this review you will get idea about different types of tumor get treated with cyberKnife radiosurgery (SRS). CyberKnife have submillimeter accuracy in metastatic brain tumor. By giving appropriate tracking method for stage I lung cancer and lung oligometastases stereotactic radiotherapy is an efficient treatment. So, at well-defined tumor sites cyberKnife is a new stereotactic body radiation therapy which delivers high dose of radiation in short period of time. For head and neck tumor treatment cyberKnife is most widely used. This treatment is helpful for adrenal gland tumor and prostate tumor, spinal metastases.

A Target Damage Effectiveness Assessment Mathematical Calculation Method with Uncertain Information Based on an Adaptive Fuzzy Neural Network

Aiming to address the issue of assessing the effectiveness of target damage in air defense interception under projectile and target intersection, this paper proposes a novel target damage effectiveness assessment mathematical calculation method using an adaptive fuzzy neural network. The design and calculation methods introduce the target cabin’s damage weight factor, the dispersion error and coverage density and fire density of warhead fragment, and the ratio of the number of warhead fragments to the cross-sectional area of the target as the primary damage factors, and we establish a new model for calculating the target damage efficiency using Takagi-Sugeno adaptive fuzzy neural network with the multiple damage factors. In the designed model, we take the characteristic parameters of warhead fragments formed by the projectile explosion as the sample data and learn the mapping relationship between damage factors and damage effect expressed by the system of fuzzy rule. Combined with the vulnerability weight and damage factors of the target, the fuzzy rule system is applied to predict and calculate the damage probability and damage effectiveness. In addition, we use shot-line technology to set up the intersection criterion between warhead fragments and target, analyze the coordinate relationship of the projectile explosion relative to the target based on the ground coordinate system, and derive the distribution density function of warhead fragment and research a calculation model for target damage probability with multiple vulnerable cabins. Finally, we use the established model to train, test, and calculate the data from the actual projectile and target intersection damage test and present the comparison results to other damage calculation methods. By calculation and comparative analysis, the results show the smaller the distance between projectile and target, as well as the smaller the average intersection angle, and the larger the weight coefficient of the warhead fragment covering the target cabin, the total target damage probability increases obviously. In the two experimental calculations, the target cabin with the largest weight coefficient was considered, and compared with the existing literature, the damage probability of the proposed calculation method was increased by 9.13% and 10.93%, respectively, and it is clear that the proposed target damage assessment method can effectively reflect the real target damage effectiveness in the state of projectile and target intersection.