scholarly journals Distributed Space Debris Tracking with Consensus Labeled Random Finite Set Filtering

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3005 ◽  
Author(s):  
Baishen Wei ◽  
Brett Nener
Keyword(s):  
Sensor Network ◽  
Efficient Solution ◽  
Space Debris ◽  
Situational Awareness ◽  
Consensus Algorithm ◽  
Time Varying ◽  
Global Average ◽  
Random Finite Set ◽  
Finite Set ◽  
Simulation Results

Space debris tracking is a challenge for spacecraft operation because of the increasing number of both satellites and the amount of space debris. This paper investigates space debris tracking using marginalized δ -generalized labeled multi-Bernoulli filtering on a network of nodes consisting of a collection of sensors with different observation volumes. A consensus algorithm is used to achieve the global average by iterative regional averages. The sensor network can have unknown or time-varying topology. The proposed space debris tracking algorithm provides an efficient solution to the key challenges (e.g., detection uncertainty, data association uncertainty, clutter, etc.) for space situational awareness. The performance of the proposed algorithm is verified by simulation results.

scholarly journals Improved Gaussian Mixture Probability Hypothesis Density for Tracking Closely Spaced Targets

2017 ◽  
Vol 63 (3) ◽  
pp. 247-254 ◽  
Author(s):  
Huanqing Zhang ◽  
Hongwei Ge ◽  
Jinlong Yang
Keyword(s):  
State Estimation ◽  
Analytic Solution ◽  
Gaussian Mixture ◽  
Multiple Target ◽  
Probability Hypothesis Density ◽  
Random Finite Set ◽  
Multi Target Tracking ◽  
Finite Set ◽  
Estimation Precision ◽  
Simulation Results

AbstractProbability hypothesis density (PHD) filter is a suboptimal Bayesian multi-target filter based on random finite set. The Gaussian mixture PHD filter is an analytic solution to the PHD filter for linear Gaussian multi-target models. However, when targets move near each other, the GM-PHD filter cannot correctly estimate the number of targets and their states. To solve the problem, a novel reweighting scheme for closely spaced targets is proposed under the framework of the GM-PHD filter, which can be able to correctly redistribute the weights of closely spaced targets, and effectively improve the multiple target state estimation precision. Simulation results demonstrate that the proposed algorithm can accurately estimate the number of targets and their states, and effectively improve the performance of multi-target tracking algorithm.

scholarly journals Tracking an unknown time-varying number of speakers using TDOA measurements: a random finite set approach

2006 ◽  
Vol 54 (9) ◽  
pp. 3291-3304 ◽  
Author(s):  
Wing-Kin Ma ◽  
Ba-Ngu Vo ◽  
S.S. Singh ◽  
A. Baddeley
Keyword(s):  
Time Varying ◽  
Random Finite Set ◽  
Finite Set

scholarly journals Time-Varying Impedance Control of Port Hamiltonian System with a New Energy-Storing Tank

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Min Zheng ◽  
Tangqing Yuan ◽  
Tao Huang
Keyword(s):  
Control Method ◽  
Impedance Control ◽  
Conservative System ◽  
Time Varying ◽  
Control Performance ◽  
Singularity Problem ◽  
Numerical Examples ◽  
New Energy ◽  
Simulation Results ◽  
New Framework

In order to guarantee the passivity of a kind of conservative system, the port Hamiltonian framework combined with a new energy tank is proposed in this paper. A time-varying impedance controller is designed based on this new framework. The time-varying impedance control method is an extension of conventional impedance control and overcomes the singularity problem that existed in the traditional form of energy tank. The validity of the controller designed in this paper is shown by numerical examples. The simulation results show that the proposed controller can not only eliminate the singularity problem but can also improve the control performance.

Iterative learning algorithm with a quadratic criterion for linear time-varying systems

2002 ◽  
Vol 216 (3) ◽  
pp. 309-316 ◽  
Author(s):  
S N Huang ◽  
K K Tan ◽  
T H Lee
Keyword(s):  
Learning Algorithm ◽  
Linear Time ◽  
Tracking Error ◽  
Iterative Learning ◽  
Tracking Performance ◽  
Time Varying ◽  
Control Scheme ◽  
Time Varying Systems ◽  
Simulation Results ◽  
Learning Law

A novel iterative learning controller for linear time-varying systems is developed. The learning law is derived on the basis of a quadratic criterion. This control scheme does not include package information. The advantage of the proposed learning law is that the convergence is guaranteed without the need for empirical choice of parameters. Furthermore, the tracking error on the final iteration will be a class K function of the bounds on the uncertainties. Finally, simulation results reveal that the proposed control has a good setpoint tracking performance.

scholarly journals Optimized Gateway Placement for Interference Cancellation in Transmit-Only LPWA Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3884 ◽  
Author(s):  
Hongxian Tian ◽  
Mary Weitnauer ◽  
Gedeon Nyengele
Keyword(s):  
Low Power ◽  
Sensor Network ◽  
Interference Cancellation ◽  
Greedy Algorithms ◽  
Sensor Nodes ◽  
Wide Area ◽  
Delivery Ratio ◽  
Large Network ◽  
Packet Delivery ◽  
Simulation Results

We study the placement of gateways in a low-power wide-area sensor network, when the gateways perform interference cancellation and when the model of the residual error of interference cancellation is proportional to the power of the packet being canceled. For the case of two sensor nodes sending packets that collide, by which we mean overlap in time, we deduce a symmetric two-crescent region wherein a gateway can decode both collided packets. For a large network of many sensors and multiple gateways, we propose two greedy algorithms to optimize the locations of the gateways. Simulation results show that the gateway placements by our algorithms achieve lower average contention, which means higher packet delivery ratio in the same conditions, than when gateways are naively placed, for several area distributions of sensors.

scholarly journals Dynamic Routing and Coordination of Cluster for Unmanned Aerial Vehicle (UAV) Swarms

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ali Abbas ◽  
Bhawani Shankar Chowdhry ◽  
Muhammad Saqib ◽  
Vishal Dattana
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Efficient Solution ◽  
High Mobility ◽  
Combinatorial Problem ◽  
Communication Graph ◽  
Frequent Change ◽  
Swarm Optimization ◽  
Wide Range ◽  
Aerial Vehicle ◽  
Simulation Results

The flying networks provide an efficient solution for a wide range of military and commercial purposes. The demand for portable and flexible communication is directed towards a quick growth in interaction among unmanned aerial vehicles (UAVs). Due to the frequent change in topology and high mobility of vehicles, routing and coordination becomes a challenging task. To maximize the throughput of the network, this study addresses the UAV swarm’s problems related to the coordination and routing and defines the proposed solution to solve these issues. For this, a network is assumed which contains an equal number of dynamic vehicles. It also presents the communication graph concept of UAVs and designs a fixed-wing UAV model to improve the efficiency of the network in terms of throughput. Furthermore, the proposed algorithm based on Cauchy particle swarm optimization (CPSO) aims towards the better performance of UAV swarms and aims to solve the combinatorial problem. The simulation results show and confirm the performance of the proposed algorithm.

scholarly journals Efficient and Energy Scheme for Wireless Rechargeable Sensor Network

2018 ◽  
Vol 9 (2) ◽  
pp. 265
Author(s):  
S V Saravanan
Keyword(s):  
Sensor Network ◽  
Network Lifetime ◽  
Communication Skill ◽  
Critical Role ◽  
Power Capacity ◽  
Simulation Results ◽  
Coverage And Connectivity ◽  
Wireless Rechargeable Sensor Network

<span lang="EN-US">The wireless rechargeable sensor network is attractive crucial and important in recent years for the advancement of wireless energy communication skill. The previous explore shown that not all of sensors can be recharged due to the limitation of power capacity to mobile chargers can carry. If a sensor playing a critical role in a sensing task cannot function as usual due to the exhausted energy, then the sensing task will be interrupted. Therefore, this paper proposes a novel recharging mechanism taking the priorities of sensors into consideration such that mobile chargers can recharge the sensor with a higher priority and the network lifetime can be efficiently sustained. The priority of each sensor depends on its contribution to the sensing task, including the coverage and connectivity capabilities. Based on the priority, the sensor with a higher priority will be properly recharged to extend the network lifetime. Simulation results show that the proposed mechanism performs better against the related work in network lifetime.</span>

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