Target tracking using Interactive Multiple Model for Wireless Sensor Network

Chen

International Journal of Distributed Sensor Networks

2018

In order to solve the problem of spectrum scarcity in wireless sensor networks, cognitive radio technology can be introduced into wireless sensor networks, giving rising to cognitive radio sensor networks. Delay-sensitive data applications in cognitive radio sensor networks require efficient real-time communication. Opportunistic pipeline routing is a potential technology to reduce the delay, which can use nodes outside the main forwarding path forward data opportunistically when the transmission fails. However, the energy efficiency of cognitive radio sensor networks with opportunistic pipeline routing is low, and the data transmission delay can be further optimized. In view of this situation, we propose the delay optimal opportunistic pipeline routing scheme named Variable Duty Cycle for Opportunistic Pipeline Routing (VDCOPR). In the Variable Duty Cycle for Opportunistic Pipeline Routing scheme, the nodes employ high duty cycle in the area far from the sink, and low duty cycle in the area near to the sink, which can achieve the balance of energy consumption and reduce the data transmission delay while not affecting network lifetime. The theoretical analysis and experimental results show that, compared with previous opportunistic pipeline routing, energy consumption of network is relatively balanced and the data transmission delay can be reduced by 36.6% in the Variable Duty Cycle for Opportunistic Pipeline Routing scheme.

Section: Analysis Of Energy Consumptionmentioning

confidence: 99%

Delay optimal opportunistic pipeline routing scheme for cognitive radio sensor networks

Chen

International Journal of Distributed Sensor Networks

2018

“…Conte et al studied on how to track underwater targets [16], Asmaa et al introduced target tracking in wireless sensor networks [17]. Moreover, there are other studies about target tracking, such as underwater passive target tracking [18], target tracking with range-only information [19], target tracking using interactive multiple model [20], target tracking utilizing underwater wireless sensor networks [21], multi-target tracking with multi-bernoulli filter [22].…”

Section: Related Workmentioning

confidence: 99%

A novel fast target tracking method for UAV aerial image

2017

Unmanned aerial vehicles (UAV) are able to achieve autonomous flight without drivers, and UAV has been a key tool to extract space data. Therefore, how to detect the trajectories of targets from UAV aerial image sequences is of great importance. Because local features are suitable to detect target tracking, we exploit scaleinvariant feature transform (SIFT) features to describe the interesting keypoints of targets. The main innovation of this paper is to utilize Multiple hypothesis tracking (MHT) algorithm to track an object (target) in a series of image sequences. Particularly, we develop a MHT framework based on a multidimensional assignment formulation and a sliding time window policy. To obtain target tracking from UAV aerial image sequences, three steps should be done, that is, 1) Breaking each track set into tracklet at a specific time, 2) Estimating the association cost of each track set, 3) Merging trajectory fragments to a longer one iteratively. Finally, we collect several UAV aerial image sequences with different target density to construct a dataset, and experimental results demonstrate the effectiveness of the proposed algorithm.

“…The indoor positioning methods mentioned above have certain limitations, and hence, a method based on the wireless sensor network (WSN) and received signal strength indicator (RSSI) is more applicable. (10,11) Lin et al used an optimized fingerprint-based positioning algorithm to achieve a positioning accuracy of higher than 3.5 m in a garage. (12) In this work, we optimize the general radio-propagation-model-based localization algorithm to achieve an accuracy of higher than 4 m. On the other hand, an improved general regression neural network (GRNN) positioning method based on an optimized method is proposed to make the positioning accuracy higher than 2.4 m.…”

Section: Introductionmentioning

confidence: 99%

Improved Indoor Localization Based on Received Signal Strength Indicator and General Regression Neural Network

Xu¹,

Wang²,

Zhang³

et al. 2019

Sensors and Materials

Keywords: received signal strength indicator (RSSI), ZigBee, localization, filter, maximum likelihood estimation (MLE), general regression neural network (GRNN)Nowadays, indoor positioning is becoming one of the most important issues in smart cities. With the rapid progress of wireless communication and digital electronic technology, wireless sensor networks (WSNs) have been developed and are playing an important role in indoor positioning systems. The received signal strength indicator (RSSI) is adopted by most rangebased localization algorithms. However, the positioning system based on the RSSI is vulnerable to environmental interference and the RSS itself is unstable. To tackle this problem, we propose an improved indoor localization based on the RSSI and general regression neural network (GRNN). In the raw data processing module, an improved average filter is proposed to make the raw data stable and reliable. Then, an improved weighted centroid localization algorithm (IWCLA) is proposed to revise the positioning result on the basis of maximum likelihood estimation (MLE). In the view of the complex and changeable indoor environment, an improved GRNN localization algorithm is proposed to achieve better applicability and higher positioning accuracy. The effectiveness of the proposed methods is verified in different cases through simulation and experiment studies. . Her current research interests include network-based control systems, multiagent systems, and complex networks.