Optimal search for moving targets with sensing capabilities using multiple UAVs

This paper studies the problem of using multiple unmanned air vehicles (UAVs) to search for moving targets with sensing capabilities. When multiple UAVs (multi-UAV) search for a number of moving targets in the mission area, the targets can intermittently obtain the position information of the UAVs from sensing devices, and take appropriate actions to increase the distance between themselves and the UAVs. Aiming at this problem, an environment model is established using the search map, and the updating method of the search map is extended by considering the sensing capabilities of the moving targets. A multi-UAV search path planning optimization model based on the model predictive control (MPC) method is constructed, and a hybrid particle swarm optimization algorithm with a crossover operator is designed to solve the model. Simulation results show that the proposed method can effectively improve the cooperative search efficiency and can find more targets per unit time compared with the coverage search method and the random search method.

Multi-UAV Distributed Collaborative Coverage for Target Search Using Heuristic Strategy

Collaborative coverage for target search using a group of unmanned aerial vehicles(UAVs)has received increasing attention in recent years.However,the design of distributed control strategy and coordination mechanisms remains a challenge.This paper presents a distributed and online heuristic strategy to solve the problem of multi-UAV collaborative coverage.As a basis,each UAV maintains a probability grid map in the form of a locally stored matrix,without shared memory.Then we design two evaluation functions and related technical strategies to enable UAVs to make state transfer or area transfer decisions in an online self-organizing way.The simulation results show that the algorithm integrates geometric features such as parallel search and internal spiral search,and is not interfered by factors such as sudden failure of UAVs,changes in detection range,and target movement.Compared with other commonly used methods for target search,our strategy has high search e±ciency,good robustness,and fault tolerance.

Grover quantum searching algorithm based on weighted targets

The current Grover quantum searching algorithm cannot identify the difference in importance of the search targets when it is applied to an unsorted quantum database, and the probability for each search target is equal. To solve this problem, a Grover searching algorithm based on weighted targets is proposed. First, each target is endowed a weight coefficient according to its importance. Applying these different weight coefficients, the targets are represented as quantum superposition states. Second, the novel Grover searching algorithm based on the quantum superposition of the weighted targets is constructed. Using this algorithm, the probability of getting each target can be approximated to the corresponding weight coefficient, which shows the flexibility of this algorithm. Finally, the validity of the algorithm is proved by a simple searching example.

Using Multiple Satellites to Search for Maritime Moving Targets Based on Reinforcement Learning

Searching for maritime moving targets using satellites is an attracting but rather difficult problem due to the satellites' orbits and discontinuous visible time windows.From a long term cyclic view,a non-myopic method based on reinforcement learning(RL)for multi-pass multi-targets searching was proposed.It learnt system behaviors step by step from each observation which resulted in a dynamic progressive way.Then it decided and adjusted optimal actions in each observation opportunity.System states were indicated by expected information gain.Neural networks algorithm was used to approximate parameters of control policy.Simulation results show that our approach with sufficient training performs significantly better than other myopic approaches which make local optimal decisions for each individual observation opportunity.

THREE PROBLEMS IN SEARCHING FOR A MOVING TARGET BETWEEN TWO SITES

Suppose that a moving target moves randomly between two sites and its movement is modeled by a homogeneous Markov chain. We consider three classical problems： （1） what kind of strategies are valid？ （2） what strategy is the optimal？ （3） what is the infimum of expected numbers of looks needed to detect the target？ Problem （3） is thoroughly solved, and some partial solutions to problems （1） and （2） are achieved.

Target acquisition performance in the presence of JPEG image compression

This paper presents an investigation on the effect of JPEG compression on the similarity between the target image and the background,where the similarity is further used to determine the degree of clutter in the image.Four new clutter metrics based on image quality assessment are introduced,among which the Haar wavelet-based perceptual similarity index,known as HaarPSI,provides the best target acquisition prediction results.It is shown that the similarity between the target and the background at the boundary between visually lossless and visually lossy compression does not change significantly compared to the case when an uncompressed image is used.In future work,through subjective tests,it is necessary to check whether this presence of compression at the threshold of just noticeable differences will affect the human target acquisition performance.Similarity values are compared with the results of subjective tests of the well-known target Search_2 database,where the degree of agreement between objective and subjective scores,measured through linear correlation,reached a value of 90%.

Analysis on Spiral Search Pattern for Moving Target

The distribution function of the target moving in constant velocity and linear course and its meeting condition to the searcher are analyzed.Another proof method for spiral search pattern is presented and the mathematic model of the target possible position is established when performing the linear search.Base on them,the wrong idea about the spiral search pattern can be

Computational Studies on Detecting a Diffusing Target in a Square Region by a Stationary or Moving Searcher

In this paper, we compute the non-detection probability of a randomly moving target by a stationary or moving searcher in a square search region. We find that when the searcher is stationary, the decay rate of the non-detection probability achieves the maximum value when the searcher is fixed at the center of the square search region;when both the searcher and the target diffuse with significant diffusion coefficients, the decay rate of the non-detection probability only depends on the sum of the diffusion coefficients of the target and searcher. When the searcher moves along prescribed deterministic tracks, our study shows that the fastest decay of the non-detection probability is achieved when the searcher scans horizontally and vertically.

Searching and Evaluation of Target Corporation in Merger and Acquisition

In order to analyze the sources of merger and acquisition(M & A)information,and the relationship between the M & A information and the M & A corporation management bureau,the searching and evaluation procedure of M & A target was discussed.The standard modes and procedures of finding the sources of the M & A target,examination of the M & A information,depicting on the future of M & A,and evaluation of the M & A target,were brought forward.

Searching for a Target Traveling between a Hiding Area and an Operating Area over Multiple Routes

We consider the problem of searching for a target that moves between a hiding area and an operating area over multiple fixed routes. The search is carried out with one or more cookie-cutter sensors, which can detect the target instantly once the target comes within the detection radius of the sensor. In the hiding area, the target is shielded from being detected. The residence times of the target, respectively, in the hiding area and in the operating area, are exponentially distributed. These dwell times are mathematically described by Markov transition rates. The decision of which route the target will take on each travel to and back from the operating area is governed by a probability distribution. We study the mathematical formulation of this search problem and analytically solve for the mean time to detection. Based on the mean time to capture, we evaluate the performance of placing the searcher(s) to monitor various travel route(s) or to scan the operating area. The optimal search design is the one that minimizes the mean time to detection. We find that in many situations the optimal search design is not the one suggested by the straightforward intuition. Our analytical results can provide operational guidances to homeland security, military, and law enforcement applications.

Quantum search for unknown number of target items by hybridizing fixed-point method with trail-and-error method

For the unsorted database quantum search with the unknown fraction λ of target items, there are mainly two kinds of methods, i.e., fixed-point and trail-and-error.(i) In terms of the fixed-point method, Yoder et al. [Phys. Rev. Lett.113 210501(2014)] claimed that the quadratic speedup over classical algorithms has been achieved. However, in this paper, we point out that this is not the case, because the query complexity of Yoder’s algorithm is actually in O(1/λ01/2)rather than O(1/λ1/2), where λ0 is a known lower bound of λ.(ii) In terms of the trail-and-error method, currently the algorithm without randomness has to take more than 1 times queries or iterations than the algorithm with randomly selected parameters. For the above problems, we provide the first hybrid quantum search algorithm based on the fixed-point and trail-and-error methods, where the matched multiphase Grover operations are trialed multiple times and the number of iterations increases exponentially along with the number of trials. The upper bound of expected queries as well as the optimal parameters are derived. Compared with Yoder’s algorithm, the query complexity of our algorithm indeed achieves the optimal scaling in λ for quantum search, which reconfirms the practicality of the fixed-point method. In addition, our algorithm also does not contain randomness, and compared with the existing deterministic algorithm, the query complexity can be reduced by about 1/3. Our work provides a new idea for the research on fixed-point and trial-and-error quantum search.

A new energy efficient management approach for wireless sensor networks in target tracking

This paper mainly studied the problem of energy conserving in wireless sensor networks for target tracking in defensing combats. Firstly, the structures of wireless sensor nodes and networks were illustrated;Secondly, the analysis of existing energy consuming in the sensing layer and its calculation method were provided to build the energy conserving objective function;What’s more, the other two indicators in target tracking, including target detection probability and tracking accuracy, were combined to be regarded as the constraints of the energy conserving objective function. Fourthly, the three energy conserving approaches, containing optimizing the management scheme, prolonging the time interval between two adjacent observations, and transmitting the observations selectively, were introduced;In addition, the improved lion algorithm combined with the Logistic chaos sequence was proposed to obtain sensor management schemes. Finally, simulations had been made to prove the effectiveness of the proposed methods and algorithm.

基于MeanShift与Group Search Optimizer的视频目标跟踪方法

针对传统的Meanshift方法在目标快速运动的情况下,目标跟踪易丢失问题,提出了一种将Meanshift与Group Search Optimizer融合的视频目标跟踪方法。该方法可以对运动目标位置进行全局搜索,根据Meanshift算法的跟踪结果判断是否要进行Group Search Optimizer算法优化,并且在目标跟踪丢失时可以快速重新找回原目标进行跟踪,提高了目标跟踪的鲁棒性。实验结果表明该方法可以有效提高对快速运动目标跟踪的准确性,具有较好的鲁棒性。

Optimal Coordinated Search for a Discrete Random Walker

This paper presents the search technique for a lost target. A lost target is random walker on one of two intersected real lines, and the purpose is to detect the target as fast as possible. We have four searchers start from the point of intersection, they follow the so called Quasi-Coordinated search plan. The expected value of the first meeting time between one of the searchers and the target is investigated, also we show the existence of the optimal search strategy which minimizes this first meeting time.

改进头脑风暴算法在多AUV协同搜索动态目标中的应用

针对搜索水中动态目标问题,提出一种基于改进头脑风暴优化(brain storm optimization,BSO)算法的多自主式水下航行器(autonomous underwater vehicle,AUV)协同搜索方法。该方法采用基于马尔可夫过程的运动预测目标存在概率,联合探测信息与预测信息更新目标存在概率。AUV间共享目标存在概率、环境不确定度、协调信息素等信息,各自利用滚动优化策略规划搜索路径。对该方法进行了有效性和鲁棒性的仿真验证。仿真结果表明,该方法能搜索到不同运动模式的水中动态目标,搜索效果优于随机算法、遍历算法等传统算法和BSO智能算法,且对AUV的不同初始出发位置不敏感,提高了战术使用的灵活性。

面向电磁目标探测的无人机集群区域分割方法

针对电磁目标搜索任务中现有覆盖式路径规划算法存在的无人机初始分布不合理、集群任务起止时间一致性差等问题,依据现实无人机集群集中投放的初始场景,设计了一种等时倾向的区域分割算法。该算法以最小化无人机间最大任务用时差为优化目标,通过改变无人机搜索区域大小影响无人机的任务用时。算法具有二级结构,第一级初始粗分割,解决边界点迭代次数过多的问题;第二级以任务时间偏差值作为调整值,保证了各机的任务用时一致性。仿真实验表明:该算法更适用于无人机集中投放的场景,缩短了无人机个体间的任务等待时间,便于资源的二次调度,有利于多阶次任务的同步执行。

复杂环境下异构无人机集群协同目标搜索方法

为实现无人机集群高效且精准的多目标搜索,考虑集群成员中探测范围广的高空无人机和机动能力较强的低空无人机,提出了一种新颖的异构无人机集群协同目标搜索方法,根据不同无人机的特性快速实现区域搜索以及多目标的精准搜索。高空无人机采用基于数字信息素的区域搜索算法确定目标存在区域,在较短时间内实现区域覆盖。低空无人机在其指引下前往目标区域,并根据目标信号强度采用改进狼群算法实现对目标的精准搜索。对于传统狼群算法信息共享不足、步长固定和易陷入局部最优的问题,采用粒子群算法、自适应参数调整和差分进化方法进行改进,提高了算法的全局寻优能力和收敛速度。仿真实验证明了所提出的模型与方法在复杂环境下目标搜索任务中的有效性和优越性。

基于神经网络架构搜索的X射线图像违禁品检测算法

为了提高卷积神经网络设计的自动化程度并进一步提高复杂背景下违禁品检测的准确率和速度,提出了一种基于神经网络架构搜索的X射线图像违禁品检测算法。首先,设计逐层渐进式搜索策略和多分支搜索空间,并基于批量归一化指标为每一个layer结构搜索最佳侧分支;然后,逐层搜索构建新的骨干网络组件;最后,组成由数据驱动的新目标检测模型。该算法在数据集HiXray、OPIXray、PIDray上分别取得了83.4%、87.2%、70.4%的检测精度。实验结果表明,本文算法能够自适应数据集并自动搜索出性能更好的Backbone组件,与FCOS、YOLOv4等主流算法相比,有效提高了复杂背景下违禁品检测的准确率和速度。