Characterization and Application of S-Band Polarimetric Radar and X-Band Phased Array Radar for a Tornadic Storm Event on June 16,2022

The X-band phased array radar offers faster scanning speed and higher spatial resolution compared to the S-band radar,making it capable of enhancing tornado monitoring and early warning capabilities.This study analyzed the characteristics and nowcasting signals of a tornado case that occurred on June 16,2022 in the Guangzhou region.Our findings indicate that the violent contraction of rotation radius and the dramatic increase in rotation speed were important signal characteristics associated with tornado formation.The X-band phased array radar,with its high temporal and spatial resolution,provided an opportunity to capture early warning signals from polarimetric characteristics.The X-band phased array radar demonstrated noteworthy ability to identify apparent tornado vortex signature(TVS)features in a 10-minute lead time,surpassing the capabilities of the CINRAD/SA radar.Additionally,due to its higher scanning frequency,the Xband phased-array radar was capable of consistently identifying TVS with shorter intervals,enabling a more precise tracking of the tornado's path.The application of professional radars,in this case,provides valuable insights for the monitoring of evolutions of severe local storms and even tornadoes and the issuance of early warning signals.

Quantitative Precipitation Estimation Using X-band Phased Array Weather Radar

This study utilized data from an X-band phased array weather radar and ground-based rain gauge observations to conduct a quantitative precipitation estimation(QPE)analysis of a heavy rainfall event in Xiong an New Area from 20:00 on August 21 to 07:00 on August 22,2022.The analysis applied the Z-R relationship method for radar-based precipitation estimation and evaluated the QPE algorithm s performance using scatter density plots and binary classification scores.The results indicated that the QPE algorithm accurately estimates light to moderate rainfall but significantly underestimates heavy rainfall.The study identified disparities in the predictive accuracy of the QPE algorithm across various precipitation intensity ranges,offering essential insights for the further refinement of QPE techniques.

Dwell scheduling algorithm for multifunction phased array radars based on the scheduling gain

A real-time dwell scheduling model, which takes the time and energy constraints into account is founded from the viewpoint of scheduling gain. Scheduling design is turned into a nonlinear programming procedure. The real-time dwell scheduling algorithm based on the scheduling gain is presented with the help of two heuristic rules. The simulation results demonstrate that compared with the conventional adaptive scheduling method, the algorithm proposed not only increases the scheduling gain and the time utility but also decreases the task drop rate.

Novel cued search strategy based on information gain for phased array radar

A search strategy based on the maximal information gain principle is presented for the cued search of phased array radars. First, the method for the determination of the cued search region, arrangement of beam positions, and the calculation of the prior probability distribution of each beam position is discussed. And then, two search algorithms based on information gain are proposed using Shannon entropy and Kullback-Leibler entropy, respectively. With the proposed strategy, the information gain of each beam position is predicted before the radar detection, and the observation is made in the beam position with the maximal information gain. Compared with the conventional method of sequential search and confirm search, simulation results show that the proposed search strategy can distinctly improve the search performance and save radar time resources with the same given detection probability.

Variable scheduling interval task scheduling for phased array radar

A scheduling algorithm is presented aiming at the task scheduling problem in the phased array radar. Rather than assuming the scheduling interval(SI) time, which is the update interval of the radar invoking the scheduling algorithm, to be a fixed value,it is modeled as a fuzzy set to improve the scheduling flexibility.The scheduling algorithm exploits the fuzzy set model in order to intelligently adjust the SI time. The idle time in other SIs is provided for SIs which will be overload. Thereby more request tasks can be accommodated. The simulation results show that the proposed algorithm improves the successful scheduling ratio by 16%,the threat ratio of execution by 16% and the time utilization ratio by 15% compared with the highest task mode priority first(HPF)algorithm.

Design and Realization of Phased Array Radar Optical Fiber Transmission System

One optical fiber transmission system is designed. The modularization optical fiber transmission adapters were utilized in the system, so the system structure could be flexibly sealable. The sub-array adapter and signal processor adapter were designed and realized utilizing the new field programmable gate array （FP- GA） which could drive the optical transceiver. The transmission agreement was designed based on the data stream. In order to solve the signal synchronization problem of the optical fiber transmitted phased array radar, a method named synchronous clock was designed. The fiber transmission error code rate of the system was zero with an experimental transmission velocity of 800 Mbit/s. The phased array radar system has detected the airplane target, thus validated the feasibility of the design method.

An adaptive dwell time scheduling model for phased array radar based on three-way decision

Real-time resource allocation is crucial for phased array radar to undertake multi-task with limited resources,such as the situation of multi-target tracking,in which targets need to be prioritized so that resources can be allocated accordingly and effectively.A three-way decision-based model is proposed for adaptive scheduling of phased radar dwell time.Using the model,the threat posed by a target is measured by an evaluation function,and therefore,a target is assigned to one of the three possible decision regions,i.e.,positive region,negative region,and boundary region.A different region has a various priority in terms of resource demand,and as such,a different radar resource allocation decision is applied to each region to satisfy different tracking accuracies of multi-target.In addition,the dwell time scheduling model can be further optimized by implementing a strategy for determining a proper threshold of three-way decision making to optimize the thresholds adaptively in real-time.The advantages and the performance of the proposed model have been verified by experimental simulations with comparison to the traditional twoway decision model and the three-way decision model without threshold optimization.The experiential results demonstrate that the performance of the proposed model has a certain advantage in detecting high threat targets.

Cued search algorithm with uncertain detection performance for phased array radars

A cued search algorithm with uncertain detection performance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, and the statistical characteristic of the detection probability is calculated by using the fluctuant model of the target radar cross section （RCS）. Secondly, when the detection probability is completely unknown, its probability density function is modeled with a beta distribution, and its posterior probability distribution with the radar observation is derived based on the Bayesian theory. Finally simulation results show that the cued search algorithm with a known RCS fluctuant model can achieve the best performance, and the algorithm with the detection probability modeled as a beta distribution is better than that with a random selected detection probability because the model parameters can be updated by the radar observation to approach to the real value of the detection probability.

Reliability of X-band Dual-polarization Phased Array Radars Through Comparison with an S-band Dual-polarization Doppler Radar

Based on the observations of a squall line on 11 May 2020 and stratiform precipitation on 6 June 2020 from two X-band dual-polarization phased array weather radars(DP-PAWRs)and an S-band dual-polarization Doppler weather radar(CINRAD/SA-D),the data reliability of DP-PAWR and its ability to detect the fine structures of mesoscale weather systems were assessed.After location matching,the observations of DP-PAWR and CINRAD/SA-D were compared in terms of reflectivity(Z_(H)),radial velocity(V),differential reflectivity(Z_(DR)),and specific differential phase(K_(DP)).The results showed that:(1)DP-PAWR has better ability to detect mesoscale weather systems than CINRAD/SAD;the multi-elevation-angles scanning of the RHI mode enables DP-PAWR to obtain a wider detection range in the vertical direction.(2)DP-PAWR’s Z_(H)and V structures are acceptable,while its sensitivity is worse than that of CINRAD/SA-D.The Z H suffers from attenuation and the Z_(H)area distribution is distorted around strong rainfall regions.(3)DP-PAWR’s Z_(DR)is close to a normal distribution but slightly smaller than that of CINRAD/SA-D.The K_(DP)products of DP-PAWR have much higher sensitivity,showing a better indication of precipitation.(4)DP-PAWR is capable of revealing a detailed and complete structure of the evolution of the whole storm and the characteristics of particle phase variations during the process of triggering and enhancement of a small cell in the front of a squall line,as well as the merging of the cell with the squall line,which cannot be observed by CINRAD/SA-D.With its fast volume scan feature and dual-polarization detection capability,DP-PAWR shows great potential in further understanding the development and evolution mechanisms of meso-γ-scale and microscale weather systems.

Computer Control Techniques of Phased Array Radars

The computer control techniques applicable to electronically scanned multifunction radars are presented. The software and hardware architecture for the real time control and the data processing within a phased array radar are described. The software system comprising a number of tasks is written in C language and implemented. The results show that the algorithm for the multitask adaptive scheduling and the multitarget data processing is suitable for multifunction phased array radars.

ALGORITHMS FOR TRACKING MANEUVERING TARGET WITH PHASED ARRAY RADAR

Several typical algorithms for tracking maneuvering target with phased array radar are studied in this paper. The constant gain filter with multiple models is analyzed. A typical method for adaptively controlling the sampling interval is modified. The performance of the single model and multiple model estimator with uniform and variable sampling interval are evaluated and compared. It is shown by the simulation results that it is necessary to apply the adaptive sampling policy based on the multiple model method when the maneuvering targets are tracked by the phased array radar since saving radar resources is more important. The adaptive algorithms of variable sampling interval are better than the algorithms of variable model. The adaptive policy to determine the sampling interval based on multiple model are superior than those based on the single model filter, because IMM estimator can adapt to the maneuver more quickly and the prediction covariance of IMM is the more sensitive and more reliable index than residual to determine the sampling interval. With IMM based method, lower sampling interval is required for a certain accuracy.

ADAPTIVE UPDATE RATE FOR PHASED ARRAY RADAR BASED ON IMMK-PF

Interacting Multiple Model Kalman-Particle Filter (IMMK-PF) has the advantages of particle filter and Kalman filter and good computation efficiency compared with Interacting Multiple Model Particle Filter (IMMPF). Based on IMMK-PF, an adaptive sampling target tracking algorithm for Phased Array Radar (PAR) is proposed. This algorithm first predicts Posterior Cramer-Rao Bound Matrix (PCRBM) of the target state, then updates the sample interval in accordance with change of the target dynamics by comparing the trace of the predicted PCRBM with a certain threshold. Simulation results demonstrate that this algorithm could solve the nonlinear motion and the nonlinear relationship between radar measurement and target motion state and decrease computation load.

Real-Time Dwell Scheduling Based on a Unified Pulse Interleaving Framework for Phased Array Radar

Dwell scheduling is a key for phased array radar to realize multi-function and it becomes especially challenging in complex tactical situations.In this manuscript,a real-time radar dwell scheduling algorithm based on a unified pulse interleaving framework is proposed.A unified pulse interleaving framework that can realize pulse interleaving analysis for phased array radars with different receiving modes is put forward,which greatly improves the time utilization of the system.Based on above framework,a real-time two-stage approach is proposed to solve the optimization problem of dwell scheduling.The importance and urgency criteria are guaranteed by the first pre-schedule stage,and the desired execution time criterion is improved at the second stage with the modified particle swarm optimization(PSO).Simulation results demonstrate that the proposed algorithm has better comprehensive scheduling performance than up-to-date algorithms that consider the pulse interleaving technique for both single beam and multiple beams receiving modes.Besides,the proposed algorithm can realize dwell scheduling in realtime.

Intelligent decision-making algorithm for airborne phased array radar search tasks based on a hierarchical strategy framework

To address the guided search task of airborne phased array radar in the scenarios of large airspace with widespread distribution of cluster targets in Beyond Visual Range(BVR)air combat,a hierarchical strategy framework based on deep reinforcement learning is proposed to guide different stages of search tasks.Firstly,an airspace set-covering model and a radar parameter optimization model for the guided search task of cluster targets are established.Secondly,the hierarchical strategy framework including upper-level and lower-level strategies is constructed based on the above models.Finally,the happo-rgs algorithm is proposed for feature extraction from Markov continuous observation sequences,to enhance the training effectiveness and improve the algorithm convergence speed.Simulation results show that the trained agent can make precise autonomous decisions rapidly based on airspace-target covering situation and target guidance information which significantly improves the radar search performance in the forementioned scenarios compared to traditional algorithms.

Resource saving based dwell time allocation and detection threshold optimization in an asynchronous distributed phased array radar network

The resource optimization plays an important role in an asynchronous Phased Array Radar Network(PARN)tracking multiple targets with Measurement Origin Uncertainty(MOU),i.e.,considering the false alarms and missed detections.A Joint Dwell Time Allocation and Detection Threshold Optimization(JDTADTO)strategy is proposed for resource saving in this case.The Predicted Conditional Cramér-Rao Lower Bound(PC-CRLB)with Bayesian Detector and Amplitude Information(BD-AI)is derived and adopted as the tracking performance metric.The optimization model is formulated as minimizing the difference between the PC-CRLBs and the tracking precision thresholds under the constraints of upper and lower bounds of dwell time and false alarm ratio.It is shown that the objective function is nonconvex due to the Information Reduction Factor(IRF)brought by the MOU.A cyclic minimizer-based solution is proposed for problem solving.Simulation results confirm the flexibility and robustness of the JDTADTO strategy in both sufficient and insufficient resource scenarios.The results also reveal the effectiveness of the proposed strategy compared with the strategies adopting the BD without detection threshold optimization and amplitude information.

A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar

A hybrid optimization approach combining a particle swarm algorithm, a genetic algorithm, and a heuristic interleaving algorithm is proposed for scheduling tasks in the multifunction phased array radar. By optimizing parameters using chaos theory, designing the dynamic inertia weight for the particle swarm algorithm as well as introducing crossover operation and mutation operation of the genetic algorithm, both the efficiency and exploration ability of the hybrid algorithm are improved. Under the frame of the intelligence algorithm, the heuristic interleaving scheduling algorithm is presented to further use the time resource of the task waiting duration. A large-scale simulation demonstrates that the proposed algorithm is more robust and efficient than existing algorithms.

Beam-pointing error compensation method of phased array radar seeker with phantom-bit technology

A phased array radar seeker（PARS） must be able to effectively decouple body motion and accurately extract the line-of-sight（LOS） rate for target missile tracking.In this study,the realtime two-channel beam pointing error（BPE） compensation method of PARS for LOS rate extraction is designed.The PARS discrete beam motion principium is analyzed,and the mathematical model of beam scanning control is finished.According to the principle of the antenna element shift phase,both the antenna element shift phase law and the causes of beam-pointing error under phantom-bit conditions are analyzed,and the effect of BPE caused by phantom-bit technology（PBT） on the extraction accuracy of the LOS rate is examined.A compensation method is given,which includes coordinate transforms,beam angle margin compensation,and detector dislocation angle calculation.When the method is used,the beam angle margin in the pitch and yaw directions is calculated to reduce the effect of the missile body disturbance and to improve LOS rate extraction precision by compensating for the detector dislocation angle.The simulation results validate the proposed method.

Improving the Nowcasting of Strong Convection by Assimilating Both Wind and Reflectivity Observations of Phased Array Radar:A Case Study

With the advent of the phased array radar(PAR)technology,it is possible to capture the development and evolution of convective systems in a much shorter time interval and with higher spatial resolution than via traditional Doppler radar.Research on the assimilation of PAR observations in numerical weather prediction models is still in its infancy in China.In this paper,the impact of assimilating PAR data on model forecasts was investigated by a case study of a local heavy rainfall event that occurred over Foshan city of Guangdong Province on 26 August 2020,via a series of sensitivity experiments.Both the retrieved three-dimensional wind and hydrometeor fields were assimilated through the nudging method with the Tropical Regional Assimilation Model for South China Sea_Rapid Update Cycle_1km(TRAMS_RUC_1km).The temperature and moisture fields were also adjusted accordingly.The results show that significant improvements are made in the experiments with latent heat nudging and adjustment of the water vapor field,which implies the importance of thermodynamic balance in the initialization of the convective system and highlights the need to assimilate PAR radar observations in a continuous manner to maximize the impact of the data.Sensitivity tests also indicate that the relaxation time should be less than 5 min.In general,for this case,the assimilation of PAR data can significantly improve the nowcasting skill of the regional heavy precipitation.This study is the first step towards operational PAR data assimilation in numerical weather prediction in southern China.

Cognate pulse sorting method based on beam missions characteristics

In most multi-function phased array radar applications, multiple missions, including airspace searching and target tracking, are usually performed simultaneously by the digital beam-forming technique and the time dividing method. This paper presents a novel method to classify pulses of different missions from an interleaved pulse sequence emitted by the same radar, which is significant in radar electronic reconnaissance and electronic support measure. Firstly, two hypotheses, i.e., pulse relativity within the same mission and pulse independence among different missions, are proposed by analyzing the antenna pattern and the beam scheduling method of the phased array radar. Based on the above two hypotheses, an optimal model for pulse classification is exploited with pulse amplitude series, where the absolute-value sum of second order difference is taken as the optimal kernel to measure sequence smooth continuity. Finally, several pieces of sequences under different numbers of missions and tracking data rates are simulated for algorithm verification. The simulation results show that the long data length and the high data rate will increase classification efficiency due to the validity of the two hypotheses in sufficient pulse amplitude sequence.