Dynamic sector processing using 2D assignment for rotating radars

Mechanically steered scanning radars receive measurements continuously while sweeping the surveillance region. However, most target tracking algorithms, like the multiple hypothesis tracker (MHT) and the joint probabilistic data association (JPDA) techniques, wait for the end of a scan in order to process the measurements and to estimate targets states. This is due to the fundamental assumption of one-to-one association between tracks and measurements and the 360° physical limit of a scan. Associating measurements to initialized tracks and filtering at the end of a complete scan may cause significant delays in target state update. In addition, association may become imperfect due to longer intervals between updates. This issue becomes significant when tracking high-speed targets with low scan rate sensors as in the airborne early warning (AEW) system. In this paper, we present a new dynamic sector processing algorithm using two-dimensional (2-D) assignment for scanning radars that report measurements within the duration of a scan. The full scan is dynamically and adaptively divided into sectors, which could be as small as a single detection, depending on the scanning rate, sparsity of targets, and required target state update speed. Measurement-to-track association, filtering, and target state update are done dynamically while sweeping from one region to another, i.e., continuous track update, limited only by the intermeasurement interval, becomes possible. The proposed algorithm offers low latency while maintaining estimation accuracy in track updates as well as efficient utilization of computational resources compared with standard frame-based tracking algorithms. Experimental results based on rotating radars demonstrate the advantages of the proposed technique.

Regularization technology is an efficient and robust method to implement forward-looking imaging of scanning radar, and the selection of regularization parameter is quite important for the imaging performance. When the imaging scene contains sparse strong targets as well as a weak background, different regularization parameters should be adopted. A dual-channel fast iterative shrinkage-thresholding (D-FIST) regularization algorithm is proposed. First, by setting a threshold with mean segmentation method, the strong targets are extracted from the echo to formulate the data of channel I, and the entire data are included in channel II. The L-curve method is adopted to determine the regularization parameter for two channels data, respectively. Then, the data are processed by the FIST algorithm with corresponding parameters. At last, the imaging result is obtained by combining the results of two channels. Simulation experiments show that the imaging results of D-FIST are better than that of direct method, especially in a low SNR situation.

A weather-adaptive three-dimensional variational data assimilation (3DVAR) system was included in the NOAA Hazardous Weather Testbed as a first step toward introducing warn-on-forecast initiatives into operations. NWS forecasters were asked to incorporate the data in conjunction with single-radar and multisensor products in the Advanced Weather Interactive Processing System (AWIPS) as part of their warning-decision process for real-time events across the United States. During the 2011 and 2012 experiments, forecasters examined more than 36 events, including tornadic supercells, severe squall lines, and multicell storms. Products from the 3DVAR analyses were available to forecasters at 1-km horizontal resolution every 5 min, with a 4–6-min latency, incorporating data from the national Weather Surveillance Radar-1988 Doppler (WSR-88D) network and the North American Mesoscale model. Forecasters found the updraft, vertical vorticity, and storm-top divergence products the most useful for storm interrogation and quickly visualizing storm trends, often using these tools to increase the confidence in a warning decision and/or issue the warning slightly earlier. The 3DVAR analyses were most consistent and reliable when the storm of interest was in close proximity to one of the assimilated WSR-88D, or data from multiple radars were incorporated into the analysis. The latter was extremely useful to forecasters in blending data rather than having to analyze multiple radars separately, especially when range folding obscured the data from one or more radars. The largest hurdle for the real-time use of 3DVAR or similar data assimilation products by forecasters is the data latency, as even 4–6 min reduces the utility of the products when new radar scans are available.

<p>Calving of tidewater glaciers is a key driver of glacier mass loss as well as a significant contribution towards sea level rise. However, this dynamic process is still challenging to quantify. In addition, there are very few direct measurements of calving activity in Svalbard at daily to sub-daily resolution due to the requirement of continuous human labour at the calving front for field studies. Seismic instruments in the vicinity of glaciers offer the potential to circumvent this issue since they record ground motion signals, including those generated by calving events, with an unprecedented sub-second resolution. Such data sets are not affected by site conditions like poor visibility or darkness and, in the case of permanent regional seismological stations, already offer long-term datasets. Despite this, a knowledge gap remains which prevents making a direct link between precise calving volumes and seismic records. This study presents our effort made towards obtaining an estimate of volumetric ice loss from integrating seismic records with 3D millimetre-wave radar measurements of a tidewater glacier calving front. In the summer of 2021, an 8-day long time series of integrated measurements was acquired at the calving front of Hansbreen, South Spitsbergen. It included remote sensing observations from a millimetre-wave radar (AVTIS2), Terrestrial Laser Scanner and time-lapse cameras correlated with a seismic dataset from two local arrays deployed at direct vicinity of calving front and a closeby regional permanent seismological station in Hornsund. Integrating these datasets brings an opportunity to correlate visual observations of calving including volumetric ice loss derived from radar scans with seismic signatures registered at nearby seismic arrays. We explore various parameters that characterize observed calving events and develop a model linking chosen parameters with ice loss using machine learning techniques. Local arrays were installed for a limited time and the calibrated parameters are expected to change spatially. Therefore, we further transfer our approach and integrate decade long records from nearby permanent seismological station. Limiting data to a single station record reduces both the accuracy of estimated ice volume and spatial resolution. However, it enables us to apply detection algorithm trained using observed calvings to decade long records and, consequently, to revisit a decade long history of Hansbreen's calving.</p>

<span lang="IN">Monitoring system in the smart grid is a key aspect that determines the reliability of transmission and communication systems in the power grid. The aim of this research is to conduct monitoring in smart grid easier and obiquitous remotely. Measured electrical parameters in smart gird are monitored through Telegram social media and webservice internet-based wirelessly. The role of web service and telepot in the smart grid monitoring system are installed in Raspberry Pi 3 which responsible to receive and process data from sensor module on each nodes and saved in database. The MySQL database accommodates amounts of data containing information on the value of current, voltage, power and time stamp which deliver by sensor node. Web programming is designed using PHP to generate connection to the database and node sensor status that runs continuously. Processed data are conducted in telepot and it is converted in to *.csv file type before sent to the telegram user. The information were also shown in web site as well as in telegram over graph display form that utilize the Highcharts library. Graphs that shows information on voltage, current, and power values can be accessed through a browser using IP address from the Raspberry Pi 3 server. When the users request information to the server, they only need certain command through Telegram social media or open the web broser to access the information from the smart grid system.</span>

Radial acceleration estimation of multiple high maneuvering targets

This article delves into the communication quality monitoring system driven by big data under the C/S (client/server) architecture, aiming to provide a comprehensive, real-time, and efficient security monitoring solution for factory production and manufacturing environments. The system integrates LoRa wireless communication technology, big data processing and analysis technology, and innovative dynamic expansion algorithms to achieve real-time monitoring and rapid response to various safety hazards in the production environment. The core of the system lies in the construction of LoRa wireless communication network, which adopts an innovative umbrella shaped network structure to ensure stable and fast transmission of sensor monitoring data to the server. By using the STM32 development board with LoRa module, dynamic expansion of hardware integration modules has been achieved, enhancing the flexibility and scalability of the system. At the level of data transmission and processing, introducing Kafka distributed message queue as a data cache effectively alleviates the pressure of processing massive real-time data. Combining the Spark Streaming streaming data processing framework, a distributed data processing model was constructed to achieve real-time consumption and efficient parallel processing of messages in Kafka queues. At the same time, the designed dynamic extension algorithm model can automatically persist the data of new monitoring points, ensuring the system's rapid adaptation to data changes. In terms of data storage and visualization, a data persistence layer architecture for a big data platform has been constructed. Real time data, historical data, and log data are stored separately in Redis, MySQL, and HDFS systems through a data streaming model, improving data storage efficiency and system performance. Based on Tomcat network server and SSM architecture, a B/S structured web server visualization platform has been developed, which supports users to remotely query the security status of the production environment through computer or mobile browser, and receive alarm notifications in case of abnormalities. The system integration test results show that the communication quality monitoring system performs excellently in wireless communication quality, data processing speed, data storage efficiency, and data visualization, and can meet the complex monitoring needs of factory production and manufacturing environments, providing solid technical support for enterprise safety production. However, there is still room for improvement in real-time data collection loss rate, alarm function completeness, and mobile access experience. Future research will focus on optimizing data transmission software models, adding multi-channel alarm functions, and developing mobile apps to enhance user experience.

The problem of distributed tracking of multiple targets is tackled by exploiting sensor mobility and the presence of sparsity in the sensor data covariance matrix. Sparse matrix decomposition relying on norm-one/two regularization is integrated with a kinematic framework to identify informative sensors, associate them with the targets, and enable them to follow closely the moving targets. Coordinate descent techniques are employed to determine in a distributed way the target-informative sensors, while the modified barrier method is employed to minimize proper error covariance matrices acquired by extended Kalman filtering. Different from existing approaches which force all sensors to move, here, local updating recursive rules are obtained only for the target-informative sensors that can update their location and follow closely the corresponding targets while staying connected. Simulations advocate that the proposed scheme outperforms alternative tracking schemes while accurately tracks multiple targets by imposing mobility only on the target-informative sensors.

Fast-scan cyclic voltammetry at scan rates between 5 and 1000 V s(-1) was performed at the tip of a scanning electrochemical microscope immersed in a solution of redox mediator. The effect of conducting and insulating substrates on the voltammetric signal was investigated as a function of scan rate and tip-substrate distance. It was found that diffusional interactions between the tip and the substrate are greatest at lower scan rates and on the reverse sweep of the voltammogram. At the fastest scan rates used, the tip could be brought to with 1 microm of the substrate without appreciable perturbation of the voltammogram. By selecting scan rates and tip-substrate distances such that feedback effects were negligible, it was possible to image the diffusion layer of a 10 microm Pt substrate electrode. With the tip placed 1 microm above a biological cell, tip-substrate diffusional interactions were greatly diminished at a scan rate of 100 V s(-1) and absent at a scan rate of 1000 V s(-1). These results suggest conditions can be selected that allow chemical imaging of substrates without the feedback interactions typically encountered in scanning electrochemical microscopy.

This paper presents a method to track multiple moving humans using Ultra-Wideband (UWB) radar. UWB radar can complement other human tracking technologies, as it works well in poor visibility conditions. Our tracking approach is based on a point process interpretation of the multi-path UWB radar scattering model for moving humans. Based on this model, we present a multiple hypothesis tracking (MHT) framework for tracking the ranges and velocities of a variable number of moving human targets. The multi-target tracking (MTT) problem for UWB radar differs from traditional applications because of the complex multipath scattering observations per target. We develop an MHT framework for UWB radar-based multiple human target tracking, which can simultaneously solve the complex observation clustering and data association problems using Bayesian inference. We present experimental results in which a monostatic UWB radar tracks both individual and multiple human targets to estimate target ranges and velocities, even with changing numbers of targets across radar scans.

Towards a data processing plane: An automata-based distributed dynamic data processing model

Knowledge Tracing (KT) is an important part of intelligent online education and is the key to personalized guidance for students' learning process. It aims at dynamically estimate students' knowledge status based on history answer records and predict whether they will answer the next question correctly. Predicting students' knowledge is a difficult task because student learning is a dynamic process and students' knowledge status is constantly changing. However, existing approaches often ignore the fact of students' stage development of learning ability and do not take into account the impact of short-term knowledge states on the next moment. Existing models do not emphasize the importance of students' long-term knowledge states and short-term knowledge states. In this paper, we propose a new Long- and Short-term Attention network Knowledge Tracing model (LSAKT). Specifically, we divided the sequences into subsequences based on time stamps, with the first attention layer learning the student's long-term knowledge state based on the interactional performance with the problem, while the other attention layer learns the student's short-term knowledge state based on the last sequence. Finally, we integrate the long-term knowledge state and the short-term knowledge state to form the student's final knowledge state. We evaluated the proposed model on four publicly available datasets, and the experimental results showed that the LSAKT approach achieved a fantastic result.

For safety applications, automotive radar networks show an interesting potential. Apart from long range radars, networked short range radar systems are under development. Additional applications can be accomplished, such includes precrash detection, parking aid and blind spot surveillance. We research the automotive radar networks using short range radars. The network is built with multiple radars. In this paper, we consider data processing using measured ranges provided by the radars in order to estimate multiple target positions with high accuracy. Firstly, we derive a method based on MAP estimation. Secondly, we propose a method which reduces the calculation complexity compared to the above MAP method. Finally, we introduce a method which can detect targets with low undetected targets. The case often happens when the measured ranges are lost because of miss-detections. We evaluate the performance with computer simulations.

High-Speed AFM Reveals the Dynamics of Single Biomolecules at the Nanometer Scale

Adaptive detection method of infrared small target based on target-background separation via robust principal component analysis