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%.

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.

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.

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。

面向动态目标搜索与打击的空地协同自主任务分配方法

面向复杂城市环境中的运动目标搜索与打击任务,单域无人平台受视野范围和运动能力等限制,易出现目标遗漏和任务完成率低的问题。针对这些问题,提出一种面向动态目标搜索与打击的空地协同自主任务分配方法,通过结合无人机视野范围广以及无人车机动性强的特点,提升空地无人系统的任务执行效率和区域覆盖率。一方面,针对未知运动目标,提出一种基于数字信息素的目标搜索模型,以平台协同收益和区域覆盖率为优化指标,保证在尽可能短的时间周期内发现区域中的所有目标。另一方面,面向动态到达的打击任务,构建基于可行路径规划的任务分配模型,以平台能耗和任务完成时间为目标函数,在保证任务完成率的同时,提高空地协同系统的资源利用率。与现有方法相比,所提方法能够在最短的时间内发现所有目标,区域覆盖率达到55%以上,且资源利用率为84.4%。实验结果表明,所提方法具备较好的目标搜索和任务执行能力。

基于麻雀搜索算法改进的YOLOv7-ECA-SSA模型的车辆检测

为解决复杂背景下小目标车辆检测存在的误检、漏检等现象,创新性提出一种改进YOLOv7网络的目标检测算法。首先,为解决小目标车辆存在次要信息干扰问题,将高效通道注意力(ECA)机制融于YOLOv7模型的主干网络特征层,通过自适应学习来增强目标区域信息权重占比,抑制无关信息;其次,为解决神经网络检测模型训练的超参数随机经验设定性问题,将麻雀搜索算法(SSA)对检测模型训练超参数进行优化,通过内外双循环迭代方式,快速收敛出全局最优学习率,进而得到最优组的权重信息,最终提高小目标车辆检测精度。实验结果表明,基于结构优化、超参数优化的YOLOv7-ECA-SSA检测模型在BDD100K数据集上的检测精度为79.01%,比原始模型提高了5.38%,具备更好的小目标车辆检测性能。

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。

海上搜救打捞装备发展研究

搜救打捞是海上安全的最后一道防线,为国家综合运输体系建设、海洋经济可持续发展提供可靠保障;搜救打捞装备是履行海上人员安全、环境安全、财产救助、应急抢险打捞等公益性职责,保障国家海上物流供应链安全的重要支撑。本文从目标搜寻定位与探测装备、海上人命救助装备、海上环境救助装备、打捞工程专用装备四方面着手,全面梳理了国外海上搜救打捞装备的发展现状,以及我国海上搜救打捞装备的研制与应用进展。与国外发达国家相比,我国海上搜救打捞装备发展存在搜寻定位装备落后、海上人命救助能力不强、危化品处置能力较低、大吨位沉船打捞能力薄弱等问题。在进一步辨识海上搜救打捞装备发展前沿的基础上,提出了加大关键装备的研发投入、推动搜救打捞装备的升级迭代、强化科技创新能力以提升装备智能化水平、部署搜救打捞技术装备攻关工程、提供相应产业政策支持等建议,以期促进我国海上搜救打捞装备高质量发展。

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。

面向协同探测的多机雷达功率时间联合优化分配算法

针对多目标搜索及跟踪场景,研究了面向协同探测的多机雷达功率时间联合优化分配算法。首先,基于信号检测理论和克拉美-罗下界,分别推导了雷达搜索性能与跟踪性能评估指标;在此基础上,建立了面向协同探测的多机雷达功率时间联合优化分配模型,即以最大化雷达工作性能指标为优化目标,以满足给定系统资源限制为约束条件,对雷达搜索及跟踪任务中节点选择、辐射功率和任务时间等参数进行联合优化设计;最后,针对上述优化问题,采用基于内点法和粒子群算法的三步分解算法进行求解。仿真结果表明,与现有算法相比,所提算法能够在满足给定系统资源限制的条件下,有效提高雷达系统搜索性能和跟踪精度。

基于MDP的无人机避撞航迹规划研究

无人机(UAV)进行避撞前提下的目标搜索航迹规划是指在复杂且众多的环境障碍约束中通过合理规划飞行路径,以更快、更高效的形式找到目标;研究了无障碍环境条件下有限位置马尔科夫移动的规律,构建了相应的马尔科夫移动分布模型;在借鉴搜索系统航迹规划的前沿研究成果之上,结合马尔科夫决策过程理论(MDP),引入了负奖励机制对Q-Learning策略算法迭代;类比“风险井”的可视化方式将障碍威胁区域对无人机的负奖励作用直观地呈现出来,构建了复杂障碍约束环境下单无人机目标搜索航迹规划模型,并进行仿真实验证明该算法可行,对航迹规划算法的设计具有一定的参考意义。