Actual Target Research Articles

The concept and context of citizenship in India

Citizenship is the most privileged kind of nationality since it allows people to live in the state in a pleasant, honourable, and harmonious manner. The term citizenship refers to the relationship between an individual and a state that entitle an individual with rights, duties, privileges and other benefits whereas noncitizens and migrated residents are denied or only partially provided. Thus, Citizenship is the privilege of the marked citizens to get benefits from the government of India through welfare and developmental programmes but crores of unmarked citizens (people from various countries have been settling in India without citizenship) are getting same benefits and privileges on par with citizens as they are living in India. Thus, the government of india have introduced CAA and NRC which are useful to identify the unmarked citizens and to make the needful acts to control unmarked citizens. If the government is failed to control the unmarked citizens, then the actual target group (Below Poverty Line people) won’t get the privileges.In this context the paper examines its central themes—concept, context and present status of citizenship in India. The objectives of this paper are firstly to analyse the concept of citizenship; secondly, to elaborate the neutral laws of citizenship along with misconceptions of citizenship acts in India particularly in the context of minorities and refugees about rendering their citizenship in India. The study has collected secondary data both qualitative and quantitative data and analysed with descriptive analysis method.

The Rheb GTPase promotes pheromone blindness via a TORC1-independent pathway in the phytopathogenic fungus Ustilago maydis.

The target of the rapamycin (TOR) signaling pathway plays a negative role in controlling virulence in phytopathogenic fungi. However, the actual targets involved in virulence are currently unknown. Using the corn smut fungus Ustilago maydis, we tried to address the effects of the ectopic activation of TOR on virulence. We obtained gain-of-function mutations in the Rheb GTPase, one of the conserved TOR kinase regulators. We have found that unscheduled activation of Rheb resulted in the alteration of the proper localization of the pheromone receptor, Pra1, and thereby pheromone insensitivity. Since pheromone signaling triggers virulence in Ustilaginales, we believe that the Rheb-induced pheromone blindness was responsible for the associated lack of virulence. Strikingly, although these effects required the concourse of the Rsp5 ubiquitin ligase and the Art3 α-arrestin, the TOR kinase was not involved. Several eukaryotic organisms have shown that Rheb transmits environmental information through TOR-dependent and -independent pathways. Therefore, our results expand the range of signaling manners at which environmental conditions could impinge on the virulence of phytopathogenic fungi.

Terminal Adaptive Guidance for Autonomous Hypersonic Strike Weapons via Reinforcement Metalearning

An adaptive guidance system suitable for the terminal phase trajectory of a hypersonic strike weapon is optimized using reinforcement meta learning. The guidance system maps observations directly to commanded bank angle, angle of attack, and sideslip angle rates. Importantly, the observations are directly measurable from radar seeker outputs with minimal processing. The optimization framework implements a shaping reward that minimizes the line-of-sight rotation rate, with a terminal reward given if the agent satisfies path constraints and meets terminal accuracy and speed criteria. This paper shows that the guidance system can adapt to off-nominal flight conditions, including perturbation of aerodynamic coefficient parameters, actuator failure scenarios, sensor scale factor errors, and actuator lag, while satisfying heating rate, dynamic pressure, and load path constraints, as well as a minimum impact speed constraint. This paper demonstrates precision strike capability against a maneuvering ground target and the ability to divert to a new target, the latter being important to maximize strike effectiveness for a group of hypersonic strike weapons. Moreover, this paper demonstrates a threat evasion strategy against interceptors with limited midcourse correction capability, where the hypersonic strike weapon implements multiple diverts to alternate targets, with the last divert to the actual target.

Preference for prior probabilities of positions under incongruent visual information.

People's expectations help them make judgments about the world. In the area of spatial memory, the interaction of existing knowledge with incoming information is best illustrated in the category effect, a bias in positioning a target toward the prototypical location of its region (Huttenlocher et al., 1991). According to Bayesian principles, these biased judgments are weighted averages of a target's metric code (incoming data) and spatial categorical code (prior expectation). While previous research in the category effect generally focused on presence and reliability of different sources of information, we examined a scenario in which prior expectation is violated. Specifically, we investigated the role of prior expectations in location memory when these conflict with visual perception. We found that people favored their previous knowledge about where a target ought to be over the visual data on the actual target location. Hence, our work contributes to the literature by demonstrating the dominance of prior expectations over incongruent visual cues, and the data extend the context effect to spatial memory. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Law Enforcement Through Social Media Pressure

Problems related to efforts to apply the law from the past have seized the attention of the Indonesian people because they are considered a very important phenomenon because it has the potential to have significant implications for Indonesian law which is basically still widely misused by legal practitioners. If viewed from the perspective of legal policy formulation that is correlated with various legal institutions at various levels, the performance of the work shown is categorized as not yet ideal and has not been able to realize the actual target of implementing the law. This encourages people to often express their disappointment through social media. From information and dissemination of social media news, it often develops into public opinion which in the language of social media communication is often called “viral news” which sometimes leads to the encouragement of law enforcement to act no longer because of law enforcement efforts but has become the fulfillment of public satisfaction and tends to ignore legal norms. Social media has made people sometimes change their image in the hope of getting sympathy and favorable public opinion for them so that it often becomes hyperreality, not reality anymore. Hyperreality is a form and state where you cannot distinguish between reality and fantasy. Through this paper, the researcher tries to do mini research through the socio-legal method where through this method the author seeks to explore further and at the same time explore law enforcement by not only based on the study of legal norms or doctrines, but also trying to see the full context of norms and their application. This study is solely aimed at providing an overview to the justice-seeking community that in the midst of uncertainty and rampant politicization of law enforcement, social media can be used as a force of coercion (pressure) as a means of law enforcement with justice.

Five-year LDL-cholesterol trend and its predictors among type 2 diabetes patients in an upper-middle-income country: a retrospective open cohort study.

BackgroundPatients with diabetes have increased risks of cardiovascular diseases (CVD), and their LDL-cholesterol (LDL-C) has to be treated to target to prevent complications. We aim to determine the LDL-C trend and its predictors among patients with type 2 diabetes (T2D) in Malaysia.MethodsThis was a retrospective open cohort study from 2013 to 2017 among T2D patients in public primary health care clinics in Negeri Sembilan state, Malaysia. Linear mixed-effects modelling was conducted to determine the LDL-C trend and its predictors. The LDL-C target for patients without CVD was <2.6 mmol/L, whereas <1.8 mmol/L was targeted for those with CVD.ResultsAmong 18,312 patients, there were more females (55.9%), adults ≥60 years (49.4%), Malays (64.7%), non-smokers (93.6%), and 45.3% had diabetes for <5 years. The overall LDL-C trend reduced by 6.8% from 2.96 to 2.76 mmol/L. In 2017, 16.8% (95% CI: 13.2–21.0) of patients without CVD and 45.8% (95% CI: 44.8–46.8) of patients with CVD achieved their respective LDL-C targets. The predictors for a higher LDL-C trend were younger adults, Malay and Indian ethnicities, females, dyslipidemia, and diabetes treatment with lifestyle modification and insulin. Longer diabetes duration, obesity, hypertension, retinopathy, statin therapy, achievement of HbA1c target and achievement of BP target were independent predictors for a lower LDL-C trend.ConclusionsThe LDL-C trend has improved, but there are still gaps between actual results and clinical targets. Interventions should be planned and targeted at the high-risk populations to control their LDL-C.

Targeting Emerging Pathogenic Mechanisms by Natural Molecules as Potential Therapeutics for Neurodegenerative Diseases.

Natural molecules with favorable safety profile and broad pharmacological activities have shown great promise in the treatment of various neurodegenerative diseases (NDDs). Current studies applying natural molecules against NDDs mainly focus on well-recognized conventional pathogenesis, such as toxic protein aggregation, oxidative stress, and neuroinflammation. However, accumulating evidence reveals that some underlying pathogenic mechanisms are involved earlier and more deeply in the occurrence and development of NDDs, such as ferroptosis, energy metabolism disorders, autophagy-lysosomal dysfunction, endoplasmic reticulum stress, and gut dysbiosis. Therefore, determining whether natural molecules can play therapeutic roles in these emerging pathogenic mechanisms will help clarify the actual targets of natural molecules and their future clinical translation. Furthermore, how to overcome the inability of most poorly water-soluble natural molecules to cross the blood-brain barrier is also critical for effective NDD treatment. This review summarizes emerging pathogenic mechanisms targeted by natural molecules for NDD treatment, proposes nanocarrier-based drug delivery and intranasal administration to enhance the intracerebral bioavailability of natural molecules, and summarizes the current state of clinical research on natural product-based therapeutics.

Evaluation of Margin for Intra-Fractional Patient Motion during Single-Isocenter Multi Targets Volumetric Modulated Arc Therapy Stereotactic Radiation Therapy for Brain Metastases Using Actual Target Coordinates

<h3>Purpose/Objective(s)</h3> Single-isocenter multi targets volumetric modulated arc therapy (SIMT-VMAT) has been used extensively in stereotactic radiation therapy (SRT) for brain metastases. However, to the best of our knowledge, there is no report on the position displacement of each target by the intra-fractional patient motion (IFPM) during SIMT-VMAT SRT. In particular, the effect of rotational IFPM is assumed to be greater depending on the distance between the center of gravity of the target and the isocenter (DTI). We hypothesized that the margin for IFPM increased depending on the DTI during SIMT-VMAT SRT. Thus, this study aimed to evaluate the margin for the IFPM during SIMT-VMAT SRT by estimating actual target displacement (ATD), which consisted of translational and rotational displacement (RD) for each target by the IFPM. <h3>Materials/Methods</h3> The study included 25 patients (aged 49–83 years; 13 men and 12 women) with 2-13 brain metastases who had previously received SIMT-VMAT SRT, with four fractions in our institution. The total number of targets for all patients was 101 and the DTI range was 14–85 mm. The clamshell-style immobilization device immobilized all patients. Bite-block was used in 20 patients. Cone beam computed tomography (CBCT) images were acquired in pre- and post-treatment within the same imaging center. The IFPM was calculated from both CBCT images. Both ATD and RD were calculated by the difference between the planned target coordinates and the actual target coordinates, which were considered with the IFPM. The Spearman rank correlation coefficient (r_s) was used for the estimation of the correlation of RD with DTI and ATD with DTI. The Wilcoxon-signed rank test was used for comparing ATD, with and without bite-block. Statistical significance was established at <i>P</i> <0.05. <h3>Results</h3> In SIMT-VMAT SRT with bite-block, the median of ATD and RD was 0.37 mm (95% confidence interval (CI): 0.019–0.80 mm) and 0.15 mm (95% CI: 0.016–0.41 mm), respectively. We observed a weak positive correlation in RD with DTI (r_s=0.39, <i>P</i> <0.05) and no correlation in ATD with DTI (r_s=0.035, <i>P</i> =0.53). On the other hand, in SIMT-VMAT SRT without bite-block, the median of ATD and RD was 0.66 mm (95% CI 0.078–1.5 mm) and 0.43 mm (95% CI 0.065–1.0 mm), respectively. We observed a positive correlation in RD with DTI (r_s=0.52, <i>P</i> <0.05) and a weak positive correlation in ATD with DTI (r_s=0.28, <i>P</i> <0.05). There was a significant difference in ATD with and without bite-block (<i>P</i> <0.05). <h3>Conclusion</h3> These results indicate that the margin for the IFPM in SIMT-VMAT SRT with the bite-block would be equal regardless of the DTI. Our results indicated the bite-block increased the accuracy of patient immobilization. Moreover, it was suggested that the margin for the IFPM would be uniformly below 1 mm from the edge of the target.

Gaze estimation in videoconferencing settings

Screen-based communication increasingly replaces face-to-face interactions. Gaze information is important for nonverbal communication. Therefore, we investigate how humans (“receivers”) estimate gaze direction of others (“senders”) in videoconferencing-like settings, a major example of screen-based communication. In two online experiments, receivers estimated gaze targets – which were known to the experimenters – from images of senders. As in real videoconferencing settings, receivers had no information about the geometry of the senders' setup (camera position, etc.), but had to rely on the information available on screen. In Experiment 1, we found that gaze-target estimates were more closely related to the actual target positions in the horizontal than in the vertical direction, a bias toward the sender's head position, and some advantage of presenting the same sender in succession. For Experiment 2, we created a new database of sender images, in which the senders' head position in the image and gaze-target position were systematically varied. Additionally, images of natural scenes were presented whose content served as potential gaze targets. At large, we replicated the findings of Experiment 1, and found only little effect of image content on the estimates. As gaze is an important part of intuitive human-machine interaction, our findings bear relevance beyond videoconferencing.

Research on Camouflaged Human Target Detection Based on Deep Learning.

In the task of camouflaged human target detection, the target is highly integrated with the complex environment background, which is difficult to identify and leads to false detection and missed detection. A detection algorithm MC-YOLOv5s is proposed for the characteristics of camouflaged targets. The algorithm takes YOLOv5s as the basic framework. First, a multispectral channel attention module is embedded in the backbone feature extraction network, which enhances the network's ability to extract camouflaged target features, weakens the attention to the surrounding background, and effectively improves the algorithm's antibackground interference. Second, the original upsampling operation is replaced by a lightweight general upsampling operator to achieve effective fusion of high-resolution low-level feature maps and low-resolution high-level feature maps. Finally, the K-means++ clustering method is used to optimize the anchor boxes of the dataset target, and the sizes of the generated priori boxes are allocated to each detection layer, which increases the matching degree between the priori boxes and the actual target boxes, and further improves the detection accuracy of the algorithm. The training and verification were carried out on the military camouflaged personnel dataset (MCPD), the precision (P), recall (R), and mean average precision (mAP) of the MC-YOLOv5s algorithm reached 97.4%, 86.1%, and 94%, respectively. Compared to the original YOLOv5s model, mean average precision (mAP) is increased by 3.7 percentage points. The improved algorithm has better detection effect, is more sensitive to camouflaged targets, and achieves accurate positioning and identification of camouflaged human targets. If the proposed MC-YOLOv5s is applied to personnel search and rescue in complex battlefields and natural disaster environments, it can greatly improve personnel search and rescue efficiency and life survival rate, and reduce the consumption of human and material resources in rescue.

Measurement-Based Wideband Space-Time Channel Models for 77GHz Automotive Radar in Underground Parking Lots

In recent years, with the rapid development of Advanced Driver Assistance Systems (ADAS), vehicular millimeter-wave (mmWave) radar plays an important role in urban intelligent transportation system. Self-interference caused by multipath in radar channel is a widely concerned problem. However, the research on radar channel propagation characteristics is very limited and there are few models for radar channel available. In this paper, we propose a pioneering research for radar channel model. Based on measurement data, channel characteristics are analysed using the space-alternating generalized expectation-maximization (SAGE) algorithm. An empirical statistical model is established for the 77GHz mmWave radar channel in the underground parking lot scenario, which is one of the most challenging environments because of time-variation and abundant multipaths, filling the gap in the field. A path-association algorithm is used to obtain multipath component (MPC) trajectories in delay, Doppler frequency and power domains. Additionally, some trajectory (in range domain) features are analysed. Moreover, based on geometric analysis of trajectories in range domain, the deterministic component of trajectories is modeled, and then a novel method for discrimination between actual targets and ghost images is proposed and verified by simulation and measurement data.

Strain prediction and benchmark with measurement in the Spallation Neutron Source mercury target without gas injection

The 1.4-MW Spallation Neutron Source (SNS) currently operates as the world's highest time-average power spallation source and uses mercury as the spallation target material. The mercury circulates through a stainless-steel target vessel and heat exchanger loop that removes the heat deposited from the incident proton beam in the target. The mercury vessel is part of the fixed but replaceable target module. The target module is not safety credited in the SNS design, but reliable long life is vital for meeting operational goals and minimizing cost and waste. SNS is a short-pulse ($\ensuremath{\sim}0.7\text{ }\text{ }\ensuremath{\mu}\mathrm{s}$) neutron source with a repetition rate of 60 Hz with 1.0 GeV protons onto the target. The short-pulse operation introduces two mechanisms that threaten the longevity of the mercury vessel. The pulsed target heating is essentially isochoric, which results in a distributed pressure field in the mercury with each pulse. After each pulse, the pressure propagates, interacts with, and strains the stainless-steel vessel. For intermittent times, this pressure field leads to mercury cavitation. The first mechanism of concern for the target lifetime is high-cycle fatigue to the stainless-steel vessel and the second is cavitation erosion. At the onset of the SNS project, the R program sought to address the need for an accurate and practical method to estimate the fatigue life of the mercury vessel. Around 2000, the R team conducted a series of in-beam mercury target experiments at the Weapons Neutron Research Facility at Los Alamos Neutron Science Center. These experiments successfully produced a small database of pulse response strain data. The data were used to develop and benchmark an explicit dynamic finite element analysis technique for estimating target fatigue life. SNS is now a mature facility with about 15 years of operation and 29 SNS target modules consumed. Since 2015, strain measurements have been collected from actual targets, and abundant target vessel strain data are now available in greater detail than was possible in 2000. Variances in pulse response data have been characterized using tolerance interval analysis to benchmark the simulation technique more thoroughly. The simulated results were found to match measurements generally well, but the method is always conservative. A potential adjustment to the cavitation threshold parameter value was suggested. Variances in the pulse response data are moderate, which is largely explained by the fact that data were acquired in 60-Hz burst mode and by the lasting vibration of the vessel between pulses.

Design of laser scanning binocular stereo vision imaging system and target measurement

Binocular stereo vision is an important scheme of road environment perception for intelligent driving, because it can provide image information and spatial three-dimensional position information. However, binocular stereo matching is often disturbed by the environment, and noise, shadow, light transformation, and so on, will all cause mismatching, therefore it is impossible to carry out accurate 3D reconstruction. In this paper, an active imaging perception and target measurement method combining laser scanning and binocular vision is proposed. The binocular vision structure is used to simulate the human eye's observation of the environment in front of the vehicle. At the same time, through the laser lines scanned to the target, the strong characteristics of binocular matching are provided to enhance the anti-interference of binocular matching, and calculate the three-dimensional point cloud data of the front object according to the binocular stereo vision theory. Firstly, this paper studies the influence of the parameters changing of the system on the accuracy of object depth value in indoor environment, and then selects the most suitable system parameters to carry out three-dimensional reconstruction experiment on the actual target object in indoor environment. Finally, the scene experiment in outdoor environment is carried out. In the outdoor environment, the red spectrum laser equipped in the system can still effectively reconstruct the target surface radian, but there are some limitations due to the limitation of outdoor light intensity. This paper preliminarily summarizes the current structural design and experiment, and further discusses the follow-up research and improvement ideas. Research and experiments show that the laser scanning binocular stereo vision imaging system proposed in this paper has the function of forward target ranging, especially under some bad imaging environment, this method can also effectively match the binocular images, and present the three-dimensional information of the internal details of the target. Under the indoor experimental environment, the minimum relative error is 1.42 % for the target with a distance of about 1.5 m, and in the evening outdoor environment, the minimum the minimum relative error is 0.87 % for the target with a distance of about 3 m. Due to the advantages of laser line intensity feature matching, it can accelerate the speed of binocular matching, and has potential application prospects in autonomous system.

Research on monostation passive location technology based on infrared tracking system

The ability to track and locate aerial targets is of great significance to target monitoring and recognition, as well as other applications. The traditional infrared tracking system needs the assistance of active ranging methods, such as laser and radar, or binocular vision and multistation detection methods for positioning. Based on high-resolution infrared tracking systems, which measure the target temperature and angle, and the law that target temperature changes with height, a monostation passive target location method based on infrared quantitative detection is proposed. The theoretical model of target location, which comprehensively considers the factors of temperature retrieval, aerodynamic heating, and atmosphere, is established. The location experiment is conducted with the developed high-resolution infrared tracking system. Compared with the automatic dependent surveillance-broadcast data of the actual target, the relative error of the ranging is <5 % . The error sources of this method are simulated and analyzed, and the simulation of the location distance and the error analysis of the long-distance target are conducted. Results show that the proposed method effectively locates the target. The method improves the monostation location ability of the infrared system and has a certain application value.

Inclusion of technology affinity in self scale–Development and evaluation of a single item measurement instrument for technology affinity

The success of the increasing use of technology in education is highly dependent on learner acceptance. Although the Technology Acceptance Model (TAM) is dominant in research for surveying acceptance of technology, it does not allow the prediction of a successful first time use of technology. The successful first time use can be determined with the survey of technology affinity, as it corresponds to the expression of certain personality traits of users and is thus detached from the specific technology. Since there are no measurement instruments for the educational sector so far and existing instruments for measuring technology affinity do not meet the specific requirements for use in the educational context (e.g., limited time for questioning), we present the single item Inclusion of Technology Affinity in Self-Scale (ITAS). In study 1 we provide evidence of convergent and discriminant validity within the general population so that a generalization of its applicability is possible. In study 2 we subsequently tested ITAS in the actual target group, the educational sector. The high correlations of the ITAS with the ATI and the control instrument TA-EG (ranging from rs = 0.679 to rs = 0.440) show that ITAS is suitable for use in research. Furthermore, the newly developed instrument convinces with its low complexity, the graphical component, which requires little text understanding and the high time saving. This research thus can contribute to the investigation of technology affinity in the educational sector helping educators to conduct technical activities with their learning group, to predict possible difficulties and adjust their planning accordingly.

Personalized brain stimulation of memory networks

BackgroundThe finding that transcranial magnetic stimulation (TMS) can enhance memory performance via stimulation of parietal sites within the Cortical-Hippocampal Network counts as one of the most exciting findings in this field in the past decade. However, the first independent effort aiming to fully replicate this finding found no discernible influence of TMS on memory performance. ObjectiveWe examined whether this might relate to interindividual spatial variation in brain connectivity architecture, and the capacity of personalisation methodologies to overcome the noise inherent across independent scanners and cohorts. MethodsWe implemented recently detailed personalisation methodology to retrospectively compute individual-specific parietal targets and then examined relation to TMS outcomes. ResultsCloser proximity between actual and novel fMRI-personalized targets associated with greater improvement in memory performance. ConclusionThese findings demonstrate the potential importance of aligning brain stimulation targets according to individual-specific differences in brain connectivity, and extend upon recent findings in prefrontal cortex.

Advanced direction of arrival estimation using step‐learnt iterative soft‐thresholding for frequency‐modulated continuous wave multiple‐input multiple‐output radar

The number of antennas in automotive frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar systems is increasing. Existing greedy or subspace-based methods cannot quickly and accurately estimate the direction of arrival (DoA) of the target. Therefore, we propose a fast and accurate DoA estimation algorithm for the automotive FMCW MIMO radar. To achieve both fastness and accuracy, we exploit the group sparsity in DoA estimation by defining the problem as a multiple measurement vector (MMV) compressive sensing and extend the step-learnt iterative soft-thresholding algorithm (SLISTA) to the MMV problem. To apply the extended SLISTA, we train the network in an unsupervised manner and normalise the input. We conduct experiments to evaluate the performance of the proposed method. Compared to the algorithms such as ISTA/FISTA/MFOCUSS that solve the same optimisation problem, the extended SLISTA exhibits the most accurate DoA estimation results for actual targets, with less execution time than a subspace-based method. Moreover, the results show that the extended SLISTA prevents false detections, whereas greedy and subspace-based methods do not.

Brain computer interface system based on monocular vision and motor imagery for UAV indoor space target searching

This paper puts forward a brain computer interface (BCI) system that integrates the monocular vision navigation and decision subsystems to realize the indoor three-dimensional (3D) space target searching with a low speed Unmanned Aerial Vehicle (UAV). The monocular vision navigation subsystem provides 3D space feasible flight directions for the decision subsystem. It utilizes the Scale Invariant Feature Transform (SIFT) algorithm and Brute-Force (BF) algorithm to extract the key points and match the extracted feature points. Obstacles and their positions are estimated by comparing the changes in the size of key points and the ratio of the size of the “convex points” area of the interest targets. The decision subsystem collects four kinds of motor imaginary (MI) tasks (left/right-hand, feet and tongue) electroencephalogram (EEG) signals from 15 electrodes firstly. Then, it applies two fifth-order Butterworth Band-Pass Filter (BPF) to preprocess the raw MI tasks EEG signals. Next, it adopts the Common Spatial Pattern (CSP) to spatially filter the preprocess EEG signals. To implement the quaternary classification,the decision subsystem utilizes the single convolutional layer Convolutional Neural Network (CNN) to realize the MI tasks EEG signals feature extraction, classification and final feasible flight direction decision finally. During this process, the spectrograms of the spatially filtered signals are given as the input to CNN. The actual indoor 3D space target searching experiment verifies that this BCI system has good adaptability and control stability.

An Efficient IAKF Approach for Indoor Positioning Drift Correction.

In this study, an indoor positioning shift correction architecture was developed with an improved adaptive Kalman filter (IAKF) algorithm for the people interference condition. Indoor positioning systems (IPSs) use ultra-wideband (UWB) communication technology. Triangulation positioning algorithms are generally employed for determining the position of a target. However, environmental communication factors and different network topologies produce localization drift errors in IPSs. Therefore, the drift error of real-time positioning points under various environmental factors and the correction of the localization drift error are discussed. For localization drift error, four algorithms were simulated and analyzed: movement average (MA), least square (LS), Kalman filter (KF), and IAKF. Finally, the IAKF algorithm was implemented and verified on the UWB indoor positioning system. The measurement results showed that the drift errors improved by 60% and 74.15% in environments with and without surrounding crowds, respectively. Thus, the coordinates of real-time positioning points are closer to those of actual targets.

Autoencoder-Based Target Detection in Automotive MIMO FMCW Radar System.

In general, a constant false alarm rate algorithm (CFAR) is widely used to automatically detect targets in an automotive frequency-modulated continuous wave (FMCW) radar system. However, if the number of guard cells, the number of training cells, and the probability of false alarm are set improperly in the conventional CFAR algorithm, the target detection performance is severely degraded. Therefore, we propose a method using a convolutional neural network-based autoencoder (AE) to replace the CFAR algorithm in the multiple-input and multiple-output FMCW radar system. In the AE, the entire detection result is compressed at the encoder side, and only significant signal components are recovered on the decoder side. In this work, by changing the number of hidden layers and the number of filters in each layer, the structure of the AE showing a high signal-to-noise ratio in the target detection result is determined. To evaluate the performance of the proposed method, the AE-based target detection result is compared with the target detection results of conventional CFAR algorithms. As a result of calculating the correlation coefficient with the data marked with the actual target position, the proposed AE-based target detection shows the highest similarity with a correlation of 0.73 or higher.