New structure of Kalman filter for radar networking

Due to the different data rates of the sensors and communication delays in the radar netting, the research of the asynchronous multisensor data fusion problem is more practical than that of the synchronous one. Through discussing the sequential approach, which is the classical asynchronous multisensor data fusion algorithm, a new algorithm based on distributed computation structure is proposed. The new algorithm can meet the requirement of real-time computation of netting fusion system, and is more practical for engineering compared with the classical sequential approach. Simulation results show the validity of the presented algorithm.

Layer-Constrained Triangulated Irregular Network Algorithm Based on Ground Penetrating Radar Data and Its Application

In this paper,a layer-constrained triangulated irregular network（ LC-TIN） algorithm is proposed for three-dimensional（ 3 D） modelling,and applied to construct a 3 D model for geological disease information based on ground penetrating radar（ GPR） data. Compared with the traditional TIN algorithm,the LCTIN algorithm introduced a layer constraint to the discrete data points during the 3 D modelling process,and it can dynamically construct networks from layer to layer and implement 3 D modelling for arbitrary shapes with high precision. The experimental results validated this method,the proposed algorithm not only can maintain the rationality of triangulation network,but also can obtain a good generation speed. In addition,the algorithm is also introduced to our self-developed 3 D visualization platform,which utilized GPR data to model geological diseases. Therefore the feasibility of the algorithm is verified in the practical application.

UAV penetration mission path planning based on improved holonic particle swarm optimization

To meet the requirements of safety, concealment, and timeliness of trajectory planning during the unmanned aerial vehicle(UAV) penetration process, a three-dimensional path planning algorithm is proposed based on improved holonic particle swarm optimization(IHPSO). Firstly, the requirements of terrain threat, radar detection, and penetration time in the process of UAV penetration are quantified. Regarding radar threats, a radar echo analysis method based on radar cross section(RCS)and the spatial situation is proposed to quantify the concealment of UAV penetration. Then the structure-particle swarm optimization(PSO) algorithm is improved from three aspects.First, the conversion ability of the search strategy is enhanced by using the system clustering method and the information entropy grouping strategy instead of random grouping and constructing the state switching conditions based on the fitness function.Second, the unclear setting of iteration numbers is addressed by using particle spacing to create the termination condition of the algorithm. Finally, the trajectory is optimized to meet the intended requirements by building a predictive control model and using the IHPSO for simulation verification. Numerical examples show the superiority of the proposed method over the existing PSO methods.

New deghosting method based on generalized triangulation

A new deghosting method based on the generalized triangulation is presented. First, two intersection points corresponding to the emitter position are obtained by utilizing two azimuth angles and two elevation angles from two jammed 3-D radars （or 2-D passive sensors）. Then, hypothesis testing based deghosting method in the multiple target scenarios is proposed using the two intersection points. In order to analyze the performance of the proposed method, the correct association probability of the true targets and the incorrect association probability of the ghost targets are defined. Finally, the Monte Carlo simulations are given for the proposed method compared with the hinge angle method in the cases of both two and three radars. The simulation results show that the proposed method has better performance than the hinge angle method in three radars case.

Distributed Control Technology for Air and Missile Defence Operations

This paper relates to accepted presentation at international conference Air and Missile Defence Technology,November 16-17,2022,London UK(day two),reflecting contents of the presentation slides.It describes applications of the patented and internationally tested Spatial Grasp Technology(SGT)and its Spatial Grasp Language(SGL)for Integrated Air and Missile Defense(IAMD).Based on holistic space navigation and processing by recursive mobile code self-spreading in distributed words,SGT differs radically from traditional management of large systems as consisting of parts exchanging messages.The dynamic network of SGL interpreters can be arbitrarily large and cover terrestrial and celestial environments as powerful spatial engines.The paper contains an example of tracking and destruction of multiple cruise missiles by self-evolving spatial intelligence in SGL using networks of radar stations.It also briefs the growing multiple satellite constellation in Low Earth Orbits(LEO)for potential IAMD applications.Starting from Strategic Defense Initiative(SDI)of the past and then briefing the latest project of Space Development Agency,the paper shows SGL solutions for discovery,tracking,and destroying ballistic missiles and hypersonic gliders with the use of collectively behaving constellations of LEO satellites.It also shows how to organize higher levels of supervision of groups of mobile chasers fighting multiple targets(both potentially as missiles or drones),by providing their global awareness even consciousness in SGL which can drastically improve their performance.The latest version of SGT can be implemented on any platforms and put into operation in a short time,similarly to its previous versions in different countries.

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.

Mobile Multimedia Computing in Cyber-Physical Surveillance Services Through UAV-Borne Video-SAR:A Taxonomy of Intelligent Data Processing for IoMT-Enabled Radar Sensor Networks

This study investigates the different aspects of multimedia computing in Video Synthetic Aperture Radar(Video-SAR)as a new mode of radar imaging for real-time remote sensing and surveillance.This research also considers new suggestions in the systematic design,research taxonomy,and future trends of radar data processing.Despite the conventional modes of SAR imaging,Video-SAR can generate video sequences to obtain online monitoring and green surveillance throughout the day and night(regardless of light sources)in all weathers.First,an introduction to Video-SAR is presented.Then,some specific properties of this imaging mode are reviewed.Particularly,this research covers one of the most important aspects of the Video-SAR systems,namely,the systematic design requirements,and also some new types of visual distortions which are different from the distortions,artifacts and noises observed in the conventional imaging radar.In addition,some topics on the general features and high-performance computing of Video-SAR towards radar communications through Unmanned Aerial Vehicle(UAV)platforms,Internet of Multimedia Things(IoMT),Video-SAR data processing issues,and real-world applications are investigated.

A Class of Distributed Variable Structure Multiple Model Algorithm Based on Posterior Information of Information Matrix

The tracking of maneuvering targets in radar networking scenarios is studied in this paper.For the interacting multiple model algorithm and the expected-mode augmentation algorithm,the fixed base model set leads to a mismatch between the model set and the target motion mode,which causes the reduction on tracking accuracy.An adaptive grid-expected-mode augmentation variable structure multiple model algorithm is proposed.The adaptive grid algorithm based on the turning model is extended to the two-dimensional pattern space to realize the self-adaptation of the model set.Furthermore,combining with the unscented information filtering,and by interacting the measurement information of neighboring radars and iterating information matrix with consistency strategy,a distributed target tracking algorithm based on the posterior information of the information matrix is proposed.For the problem of filtering divergence while target is leaving radar surveillance area,a k-coverage algorithm based on particle swarm optimization is applied to plan the radar motion trajectory for achieving filtering convergence.