Moving target localization for multistatic passive radar using delay, Doppler and Doppler rate measurements

Time delay and Doppler shift between the echo signal and the reference signal are two most commonly used measurements in target localization for the passive radar. Doppler rate, which can be obtained from the extended cross ambiguity function, offers an opportunity to further enhance the localization accuracy. This paper considers using the measurement Doppler rate in addition to measurements of time delay and Doppler shift to locate a moving target. A closed-form solution is developed to accurately and efficiently estimate the target position and velocity.The proposed solution establishes a pseudolinear set of equations by introducing some additional variables, imposes weighted least squares formulation to yield a rough estimate, and utilizes the function relation among the target location parameters and additional variables to improve the estimation accuracy. Theoretical covariance and Cramer-Rao lower bound(CRLB) are derived and compared, analytically indicating that the proposed solution attains the CRLB. Numerical simulations corroborate this analysis and demonstrate that the proposed solution outperforms existing methods.

Waveform diversity based sonar system for target localization

A new monostatic array system taking advantage of diverse waveforms to improve the performance of underwater tar- get localization is proposed. Unlike the coherent signals between different elements in common active array, the transmitted signals from different elements here are spatially orthogonal waveforms which allow for array processing in the transit mode and result in an extension of array aperture. The mathematical derivation of Capon estimator for this sonar system is described in detail. And the performance of this orthogonal-waveform based sonar is an- alyzed and compared with that of its phased-array counterpart by water tank experiments. Experimental results show that this sonar system could achieve 12 dB-15 dB additional array gain over its phased-array counterpart, which means a doubling of maximum detection range. Moreover, the angular resolution is significantly improved at lower SNR.

Target localization based on cross-view matching between UAV and satellite

Matching remote sensing images taken by an unmanned aerial vehicle(UAV) with satellite remote sensing images with geolocation information. Thus, the specific geographic location of the target object captured by the UAV is determined. Its main challenge is the considerable differences in the visual content of remote sensing images acquired by satellites and UAVs, such as dramatic changes in viewpoint, unknown orientations, etc. Much of the previous work has focused on image matching of homologous data. To overcome the difficulties caused by the difference between these two data modes and maintain robustness in visual positioning, a quality-aware template matching method based on scale-adaptive deep convolutional features is proposed by deeply mining their common features. The template size feature map and the reference image feature map are first obtained. The two feature maps obtained are used to measure the similarity. Finally, a heat map representing the probability of matching is generated to determine the best match in the reference image. The method is applied to the latest UAV-based geolocation dataset(University-1652 dataset) and the real-scene campus data we collected with UAVs. The experimental results demonstrate the effectiveness and superiority of the method.

Coordinated target localization base on pseudo measurement for clustered space robot

This paper presents a coordinated target localization method for clustered space robot.According to the different measuring capabilities of cluster members,the master-slave coordinated relative navigation strategy for target localization with respect to slavery space robots is proposed;then the basic mathematical models,including coordinated relative measurement model and cluster centralized dynamics,are established respectively.By employing the linear Kalman flter theorem,the centralized estimator based on truth measurements is developed and analyzed frstly,and with an intention to inhabit the initial uncertainties related to target localization,the globally stabilized estimator is designed through introduction of pseudo measurements.Furthermore,the observability and controllability of stochastic system are also analyzed to qualitatively evaluate the convergence performance of pseudo measurement estimator.Finally,on-orbit target approaching scenario is simulated by using semi-physical simulation system,which is used to verify the convergence performance of proposed estimator.During the simulation,both the known and unknown maneuvering acceleration cases are considered to demonstrate the robustness of coordinated localization strategy.

Multiple target localization in wireless visual sensor networks

Target localization is an important service in wireless visual sensor networks （WVSN）. Although the problem of single target localization has been intensively studied, few consider the problem of multiple target localization without prior target information in WVSN. In this paper, we first investigate the architecture of WVSN where data transmission is reduced to only target positions. Since target matching is a key issue in the multiple target localization, we propose a statistical method to match corresponding targets to located targets in world coordinates. In addition, we also consider scenarios where occlusion or limited field of view （FOV） occurs. The proposed method utilizes target images to the greatest extent. Our experimental results show that the proposed method obtains a more accurate result in targets localization compared with the camera discard scheme, and saves significant amounts of energy compared with other feature matching schemes.

Efficient closed-form solution for target localization using TDOA measurements in the presence of sensor position errors

An efficient solution for locating a target was proposed, which by using time difference of arrival （TDOA） measurements in the presence of random sensor position errors to increase the accuracy of estimation. The cause of position estimation errors in two-stage weighted least squares （TSWLS） method is analyzed to develop a simple and effective method for improving the localization performance. Specifically, the reference sensor is selected again and the coordinate system is rotated according to preliminary estimated target position by using TSWLS method, and the final position estimation of the target is obtained by using weighted least squares （WLS）. The proposed approach exhibits a closed-form and is as efficient as TSWLS method. Simulation results show that the proposed approach yields low estimation bias and improved robustness with increasing sensor position errors and thus can easily achieve the Cramer-Rao lower bound （CRLB） easily and effectively improve the localization accuracy.

Target localization and parameters estimation by sonar system with explosions as underwater sound sources

Underwater target localization and parameters(azimuth and range) estimation by the method of utilizing explosions as underwater sound sources are described in this paper.The narrow beam reverberation model of the target echo signal is researched to estimate the target azimuth in reverberation background.Estimation errors of target azimuth and range are studied and proved to approximately meet Gauss distribution.Then the variance formula of target range error is deduced.Simulation experiments are applied to research the target range error and its standard deviation,and a series of measures to improve the estimation accuracy of target range are proposed.It is confirmed by the data processing results of simulations and lake experiments that the proposed method can accurately locate underwater target at a long distance on the condition of a certain underwater explosion range error.

Trajectory Optimization for Target Localization and Sensor Bias Calibration with Bearing-Only Information

The problem of trajectory optimization of an unmanned aerial vehicle(UAV)for static target localization with biased bearing measurements is considered.The angular bias in sensor measurements is modeled as an additive constant in the observation model and jointly estimated with the position of the target.The necessary conditions for system observability of this estimation problem is first derived analytically with geometrical interpretations provided.The trajectory of UAV is designed based on the Fisher Information Matrix(FIM)considering physical constraints to enhance the system observability.Simulation results with Monte-Carlo runs are presented to demonstrate the improvement in target localization with biased measurements by UAV trajectory optimization.

UnderwaterWaste Recognition and Localization Based on Improved YOLOv5

With the continuous development of the economy and society,plastic pollution in rivers,lakes,oceans,and other bodies of water is increasingly severe,posing a serious challenge to underwater ecosystems.Effective cleaning up of underwater litter by robots relies on accurately identifying and locating the plastic waste.However,it often causes significant challenges such as noise interference,low contrast,and blurred textures in underwater optical images.A weighted fusion-based algorithm for enhancing the quality of underwater images is proposed,which combines weighted logarithmic transformations,adaptive gamma correction,improved multi-scale Retinex(MSR)algorithm,and the contrast limited adaptive histogram equalization(CLAHE)algorithm.The proposed algorithm improves brightness,contrast,and color recovery and enhances detail features resulting in better overall image quality.A network framework is proposed in this article based on the YOLOv5 model.MobileViT is used as the backbone of the network framework,detection layer is added to improve the detection capability for small targets,self-attention and mixed-attention modules are introduced to enhance the recognition capability of important features.The cross stage partial(CSP)structure is employed in the spatial pyramid pooling(SPP)section to enrich feature information,and the complete intersection over union(CIOU)loss is replaced with the focal efficient intersection over union(EIOU)loss to accelerate convergence while improving regression accuracy.Experimental results proved that the target recognition algorithm achieved a recognition accuracy of 0.913 and ensured a recognition speed of 45.56 fps/s.Subsequently,Using red,green,blue and depth(RGB-D)camera to construct a system for identifying and locating underwater plastic waste.Experiments were conducted underwater for recognition,localization,and error analysis.The experimental results demonstrate the effectiveness of the proposed method for identifying and locating underwater plastic waste,and it has good localization accuracy.

UAV Velocity Measurement for Ground Moving Target

To satisfy the demand of measuring the velocity of ground moving target through unmanned aerial vehicle(UAV)electro-optical platform,two velocity measurement methods are proposed.Firstly,a velocity measurement method based on target localization is derived,using the position difference between two points with the advantages of easy deployment and realization.Then a mathematical model for measuring target velocity is built and described by 15 variables,i.e.UAV velocity,UAV attitude angular rate,camera direction angular rate and so on.Moreover,the causes of velocity measurement error are analyzed and a formula is derived for calculating the measurement error.Finally,the simulation results show that angular rate error has a strong influence on the velocity measurement accuracy,especially the UAV pitch angular rate error,roll angular rate error and the camera angular altitude rate error,thus indicating the direction for improving velocity measurement precision.

Virtual local target method for avoiding local minimum in potential field based robot navigation

A novel robot navigation algorithm with global path generation capability is presented. Local minimum is a most intractable but is an encountered frequently problem in potential field based robot navigation.Through appointing appropriately some virtual local targets on the journey, it can be solved effectively. The key concept employed in this algorithm are the rules that govern when and how to appoint these virtual local targets. When the robot finds itself in danger of local minimum, a virtual local target is appointed to replace the global goal temporarily according to the rules. After the virtual target is reached, the robot continues on its journey by heading towards the global goal. The algorithm prevents the robot from running into local minima anymore. Simulation results showed that it is very effective in complex obstacle environments.

Siamese Dense Pixel-Level Fusion Network for Real-Time UAV Tracking

Onboard visual object tracking in unmanned aerial vehicles(UAVs)has attractedmuch interest due to its versatility.Meanwhile,due to high precision,Siamese networks are becoming hot spots in visual object tracking.However,most Siamese trackers fail to balance the tracking accuracy and time within onboard limited computational resources of UAVs.To meet the tracking precision and real-time requirements,this paper proposes a Siamese dense pixel-level network for UAV object tracking named SiamDPL.Specifically,the Siamese network extracts features of the search region and the template region through a parameter-shared backbone network,then performs correlationmatching to obtain the candidate regionwith high similarity.To improve the matching effect of template and search features,this paper designs a dense pixel-level feature fusion module to enhance the matching ability by pixel-wise correlation and enrich the feature diversity by dense connection.An attention module composed of self-attention and channel attention is introduced to learn global context information and selectively emphasize the target feature region in the spatial and channel dimensions.In addition,a target localization module is designed to improve target location accuracy.Compared with other advanced trackers,experiments on two public benchmarks,which are UAV123@10fps and UAV20L fromthe unmanned air vehicle123(UAV123)dataset,show that SiamDPL can achieve superior performance and low complexity with a running speed of 100.1 fps on NVIDIA TITAN RTX.

Influence of local environmental target constraints on enterprise OFDI

By collecting the environmental target constraints in the annual government work report of prefecture-level cities,this paper studies its impact on local enterprises’OFDI.The main conclusions are as follows:(1)Both direct and indirect constraints have a significant positive impact on enterprises’OFDI,and the degree of direct constraints is stronger than that of indirect constraints.(2)Environmental target constraints of local governments will affect corporate OFDI behavior by affecting production costs,local economic development level and local openness,and(3)There are regional heterogeneity and investment type heterogeneity in the impact of environmental goal constraints on corporate OFDI.The suggestions are as follows:government departments can appropriately strengthen the constraint intensity of environmental targets and formulate specific restraint rules for industries with different levels of pollution,so as to effectively make use of the technology spillover effects brought about by OFDI to promote the upgrading of domestic industrial structure.When making OFDI,enterprises should clarify the investment motivation,strengthen corporate social responsibility,and make use of technology spillover effect to promote the upgrading and development of home country industry while being beneficial to their own development.

Quantification of non-coding RNA target localization diversity and its application in cancers

SubceUular localization is pivotal for RNAs and proteins to implement biological functions. The localization diversity of protein interactions has been studied as a crucial feature of proteins, considering that the protein-protein interactions take place in vari- ous subceltutar locations. Nevertheless, the localization diversity of non-coding RNA （ncRNA） target proteins has not been sys- tematically studied, especially its characteristics in cancers. In this study, we provide a new algorithm, non-coding RNA target localization coefficient （ncTALENT）, to quantify the target localization diversity of ncRNAs based on the ncRNA-protein interaction and protein subcellular localization data. ncTALENT can be used to calculate the target localization coefficient of ncRNAs and measure how diversely their targets are distributed among the subcellular locations in various scenarios. We focus our study on long non-coding RNAs （IncRNAs）, and our observations reveal that the target localization diversity is a primary characteristic of IncRNAs in different biotypes. Moreover, we found that IncRNAs in multiple cancers, differentially expressed cancer IncRNAs, and IncRNAs with multiple cancer target proteins are prone to have high target localization diversity. Furthermore, the analysis of gastric cancer helps us to obtain a better understanding that the target localization diversity of IncRNAs is an important feature closely related to clinical prognosis. Overall, we systematically studied the target localization diversity of the IncRNAs and uncovered its association with cancer.

Constrained Weighted Least Squares Location Algorithm Using Received Signal Strength Measurements

Determine the location of a target has gained considerable interest over the past few years. The Received Signal Strength(RSS) measurements and Differential RSS(DRSS) measurements can be converted to distance or distance ratio estimates for constructing a set of linear equations. Based on these linear equations, a constrained weighted least Squares(CWLS) algorithm for target localization is derived. In addition, an iterative technique based on Newton's method is utilized to give a solution. The covariance and bias of the CWLS algorithm is derived using perturbation analysis. Simulation shows that the proposed estimator achieves better performance than existing algorithms with reasonable complexity.

A Rapid DNA Mini-prep Method for Large-Scale Rice Mutant Screening

A high throughput rice DNA mini-preparation method was developed. The method is suitable for large-scale mutant bank screening as well as large mapping populations with characteristics of maintaining relatively high level of DNA purity and concentration. The extracted DNA was tested and suitable for regular PCR amplification （SSR） and for Targeting Induced Local Lesion in Genome （TILLING） analysis.

The use of localized proteomics to identify the drivers of Alzheimer's disease pathogenesis

Alzheimer’s disease（AD）is broadly defined by dementia and the presence of specific neuropathological features in the brain（amyloid plaques,neurofibrillary tangles（NFTs）and congophilic amyloid angiopathy）.However,the rate of disease progression,type of cognitive impairment,and extent of neuropathology vary widely in patients with AD（Murray et al.,2011）.

Targeted Local Immunization in Scale-Free Peer-to-Peer Networks

The power-law node degree distributions of peer-to-peer overlay networks make them extremely robust to random failures whereas highly vulnerable under intentional targeted attacks. To enhance attack survivability of these networks, DeepCure, a novel heuristic immunization strategy, is proposed to conduct decentralized but targeted immunization. Different from existing strategies, DeepCure identifies immunization targets as not only the highly-connected nodes but also the nodes with high availability and/or high link load, with the aim of injecting immunization information into just right targets to cure. To better trade off the cost and the efficiency, DeepCure deliberately select these targets from 2-local neighborhood, as well as topologically-remote but semantically-close friends if needed. To remedy the weakness of existing strategies in case of sudden epidemic outbreak, DeepCure is also coupled with a local-hub oriented rate throttling mechanism to enforce proactive rate control. Extensive simulation results show that DeepCure outperforms its competitors, producing an arresting increase of the network attack tolerance, at a lower price of eliminating viruses or malicious attacks.

High-resolution melting-based TILLING of γ ray-induced mutations in rice

Targeting Induced Local Lesions IN Genomes （TILLING） is a reverse genetics strategy for the high-throughput screening of induced mutations.γ, radiation, which often induces both insertion/deletion （Indel） and point mutations, has been widely used in mutation induction and crop breeding. The present study aimed to develop a simple, high-throughput TILLING system for screening γ ray-induced mutations using high-resolution melting （HRM） analysis. Pooled rice （Oryza sativa） samples mixed at a 1：7 ratio of Indel mutant to wild-type DNA could be distinguished from the wild-type controls by HRM analysis. Thus, an HRM-TILLING system that analyzes pooled samples of four M2 plants is recommended for screening γ, ray-induced mutants in rice. For demonstration, a γ, ray-mutagenized M2 rice population （n=4560） was screened for mutations in two genes, OsLCT1 and SPDT, using this HRM-TILLING system. Mutations including one single nucleotide substitution （G→A） and one single nucleotide insertion （A） were identified in OsLCT1, and one tdnucleotide （TTC） deletion was identified in SPDT. These mutants can be used in rice breeding and genetic studies, and the findings are of importance for the application of γ, ray mutagenesis to the breeding of rice and other seed crops.