scholarly journals Multitarget Tracking Algorithm Based on Adaptive Network Graph Segmentation in the Presence of Measurement Origin Uncertainty

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3791
Author(s):  
Tianli Ma ◽  
Song Gao ◽  
Chaobo Chen ◽  
Xiaoru Song
Keyword(s):  
Shortest Path ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Tracking Algorithm ◽  
Multitarget Tracking ◽  
Integer Programming Problem ◽  
Tracking Accuracy ◽  
Network Graph ◽  
Adaptive Network

To deal with the problem of multitarget tracking with measurement origin uncertainty, the paper presents a multitarget tracking algorithm based on Adaptive Network Graph Segmentation (ANGS). The multitarget tracking is firstly formulated as an Integer Programming problem for finding the maximum a posterior probability in a cost flow network. Then, a network structure is partitioned using an Adaptive Spectral Clustering algorithm based on the Nyström Method. In order to obtain the global optimal solution, the parallel A* search algorithm is used to process each sub-network. Moreover, the trajectory set is extracted by the Track Mosaic technique and Rauch–Tung–Striebel (RTS) smoother. Finally, the simulation results achieved for different clutter intensity indicate that the proposed algorithm has better tracking accuracy and robustness compared with the A* search algorithm, the successive shortest-path (SSP) algorithm and the shortest path faster (SPFA) algorithm.

scholarly journals Multiple Object Tracking Using the Shortest Path Faster Association Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhenghao Xi ◽  
Heping Liu ◽  
Huaping Liu ◽  
Bin Yang
Keyword(s):  
Integer Programming ◽  
Object Tracking ◽  
Programming Problem ◽  
Shortest Path ◽  
Multiple Object Tracking ◽  
Global Optimum ◽  
Integer Programming Problem ◽  
Tracking Accuracy ◽  
Worst Case ◽  
Multiple Object

To solve the persistently multiple object tracking in cluttered environments, this paper presents a novel tracking association approach based on the shortest path faster algorithm. First, the multiple object tracking is formulated as an integer programming problem of the flow network. Then we relax the integer programming to a standard linear programming problem. Therefore, the global optimum can be quickly obtained using the shortest path faster algorithm. The proposed method avoids the difficulties of integer programming, and it has a lower worst-case complexity than competing methods but better robustness and tracking accuracy in complex environments. Simulation results show that the proposed algorithm takes less time than other state-of-the-art methods and can operate in real time.

Object Detection and Recognition Algorithm of Moving UAV Based on Machine Vision

2016 ◽  
Vol 13 (10) ◽  
pp. 7731-7737
Author(s):  
Mao Jun
Keyword(s):  
Target Tracking ◽  
Optimal Solution ◽  
Target Position ◽  
Multiple Instance Learning ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
L1 Norm ◽  
Original Distribution ◽  
Local Optimal Solution ◽  
Distribution Field

To conquer disadvantages of slow speed of target tracking algorithm in original distribution field as well as easiness of being caught in local optimal solution, one target tracking algorithm of real-time distribution field based on global matching is presented in the Thesis, thus remarkably improving performance of target tracking algorithm in distribution field. In proposed algorithm, relevant correlation coefficients will be used to substitute the similarity between target distribution filed of original L1 norm measurement and candidate distribution field. As a consequence, the target search process can concert from time domain operation to operation processing of frequency domain among which the latter has lower computation complexity and capability of global search of target position so as to conquer disadvantages such as randomness caused by sparse sampling and that the gradient descent of target tracking algorithm in original distribution field is liable to be caught in local optimal solution. In 12 challenging video sequences, compared with multiple-instance learning and tracking algorithm and tracking algorithm of original distribution field, the method proposed in the Thesis has acquired the optimum performance in tracking accuracy, success rate and speed.

scholarly journals Novel optimization using hierarchical Path finding A* (HPA*) algorithm for strategic gaming setup

2018 ◽  
Vol 7 (2.6) ◽  
pp. 54
Author(s):  
Aqsa Zafar ◽  
Krishna Kant Agrawal
Keyword(s):  
Shortest Path ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Research Area ◽  
Optimization Techniques ◽  
Path Finding ◽  
A Algorithm ◽  
Strategic Gaming ◽  
Game Industry ◽  
Future Work

In game Industry, the most trending research area is shortest path finding. There are many video games are present who are facing the problem of path finding and there is various algorithms are present to solve this problem. In this paper brief introduction is given in the most using algorithm for path finding and A* algorithm has been proved the best algorithm for resolving the problem of shortest path finding in games. It provides the optimal solution for path finding as compare to other search algorithm. At the start of the paper, brief introduction about the path finding is given. Then the reviews of different search algorithm are presented on the basis of path finding. After that information of A* algorithm and optimization techniques are described. In the last, application and examples how the path finding techniques are used in the game is addressed and future work and conclusion are drawn.

scholarly journals Research on Classification of Daily Load Curve of Distribution Network Based on Improved SSA-FCM

2021 ◽  
Vol 2121 (1) ◽  
pp. 012011
Author(s):  
Haoran Shi ◽  
Rong Cao ◽  
Wenbo Hao ◽  
Mingyu Xu ◽  
Heng Hu ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Search Algorithm ◽  
Distribution Network ◽  
Optimal Solution ◽  
Cluster Center ◽  
Load Curve ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Three Phase ◽  
Daily Load

Abstract In the analysis of three-phase unbalance in distribution network, the accuracy of daily load curve classification results determines the size of three-phase unbalance. Aiming at the shortcomings of Fuzzy C-Means (FCM), a fuzzy C-Means clustering algorithm (SSA-FCM) optimized based on Sparrow Search Algorithm (SSA) is proposed. The cluster validity evaluation index is introduced to get the optimal quantity of clusters, and the SSA is used to search for the initial cluster center, which solves the problem that the FCM algorithm relies on the initial value and is easy to converge to local optimal solution. The simulation results show that, compared with the FCM algorithm, the load curves classified into the same category by SSA-FCM are closer together.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

On the Optimal Precoder Design for Energy-Efficient and Secure MIMO Systems

2017 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Tung T. Vu ◽  
Ha Hoang Kha
Keyword(s):  
Energy Efficiency ◽  
Mimo Systems ◽  
Search Algorithm ◽  
Multiple Input Multiple Output ◽  
Research Work ◽  
Computational Cost ◽  
Optimal Solution ◽  
Secrecy Rate ◽  
Highly Nonlinear ◽  
Precoder Design

In this research work, we investigate precoder designs to maximize the energy efficiency (EE) of secure multiple-input multiple-output (MIMO) systems in the presence of an eavesdropper. In general, the secure energy efficiency maximization (SEEM) problem is highly nonlinear and nonconvex and hard to be solved directly. To overcome this difficulty, we employ a branch-and-reduce-and-bound (BRB) approach to obtain the globally optimal solution. Since it is observed that the BRB algorithm suffers from highly computational cost, its globally optimal solution is importantly served as a benchmark for the performance evaluation of the suboptimal algorithms. Additionally, we also develop a low-complexity approach using the well-known zero-forcing (ZF) technique to cancel the wiretapped signal, making the design problem more amenable. Using the ZF based method, we transform the SEEM problem to a concave-convex fractional one which can be solved by applying the combination of the Dinkelbach and bisection search algorithm. Simulation results show that the ZF-based method can converge fast and obtain a sub-optimal EE performance which is closed to the optimal EE performance of the BRB method. The ZF based scheme also shows its advantages in terms of the energy efficiency in comparison with the conventional secrecy rate maximization precoder design.

scholarly journals Optimization strategy of wireless charger node deployment based on improved cuckoo search algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yang Wang ◽  
Feifan Wang ◽  
Yujun Zhu ◽  
Yiyang Liu ◽  
Chuanxin Zhao
Keyword(s):  
Sensor Network ◽  
Sensor Node ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Sensor Nodes ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Received Power

AbstractIn wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

Fast JPDA multitarget tracking algorithm

1989 ◽  
Vol 28 (2) ◽  
pp. 371 ◽  
Author(s):  
James L. Fisher ◽  
David P. Casasent
Keyword(s):  
Tracking Algorithm ◽  
Multitarget Tracking
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