PROJECT TITLE :

A Dynamic Approach to Sensor Network Deployment for Mobile-Target Detection in Unstructured, Expanding Search Areas

ABSTRACT:

This paper proposes a completely unique strategy for the deployment of a static-sensor network based mostly on the employment of a target-motion probability model. The main target is on the $64000-time dynamic and optimal deployment of the network for detecting untrackable targets. The dynamic nature of the deployment refers to the on-line reconfigurability of the network as real-time info regarding the target becomes obtainable. The optimal locations of the network nodes, in flip, are determined primarily based on maximizing the chance of finding the target through the use of iso-cumulative-likelihood curves. The proposed strategy is adaptable to unstructured environments with natural terrain variation and also the presence of obstacles. Intensive simulations, a number of which are included in this paper, verified the advantage of our deployment strategy over alternative existing ways. Specifically, the proposed strategy will tangibly increase the success rate of target detection, while reducing the mean detection time, in comparison with uniform-coverage-primarily based approaches that do not contemplate probabilistic target-motion modeling. A comprehensive example is additionally included, herein, to illustrate the successful application of our proposed deployment strategy to a wilderness search and rescue scenario, where both static and mobile sensors are used inside a hybrid sensor-deployment strategy.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : A Novel Dynamic Model Capturing Spatial and Temporal Patterns for Facial Expression Analysis ABSTRACT: Incorporating spatial and temporal patterns present in facial behavior should substantially improve facial
PROJECT TITLE : Use of a Tracer-Specific Deep Artificial Neural Net to Denoise Dynamic PET Images ABSTRACT: The use of kinetic modeling (KM) on a voxel level in dynamic PET pictures frequently results in large amounts of noise,
PROJECT TITLE : Robust Unsupervised Multi-view Feature Learning with Dynamic Graph ABSTRACT: By modeling the affinity associations with a graph to lower the dimension, graph-based multi-view feature learning algorithms learn a
PROJECT TITLE : Deep Tone Mapping Operator for High Dynamic Range Images ABSTRACT: The need for a rapid tone mapping operator (TMO) capable of adapting to a wide range of high dynamic range (HDR) content on low dynamic range (LDR)
PROJECT TITLE : Dynamic Scene Deblurring by Depth Guided Model ABSTRACT: Object movement, depth fluctuation, and camera shake are the most common causes of dynamic scene blur. For the most part, present approaches use picture

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry