Tracking interacting dust: comparison of tracking and state estimation techniques for dusty plasmas

When tracking a target particle that is interacting with nearest neighbors in a known way, positional data of the neighbors can be used to improve the state estimate. Effects of the accuracy of such positional data on the target track accuracy are investigated in this paper, in the context of dusty plasmas. In kinematic simulations, notable improvement in the target track accuracy was found when including all nearest neighbors in the state estimation filter and tracking algorithm, whereas the track accuracy was not significantly improved by higher-accuracy measurement techniques. The state estimation algorithm, involving an extended Kalman filter, was shown to either remove or significantly reduce errors due to "pixel locking". It is concluded that the significant extra complexity and computational expense to achieve these relatively small improvements are likely to be unwarranted for many situations. For the purposes of determining the precise particle locations, it is concluded that the simplified state estimation algorithm can be a viable alternative to using more computationally-intensive measurement techniques.Comment: 11 pages, 6 figures, Conference paper: Signal and Data Processing of Small Targets 2010 (SPIE

An Indoor Navigation System Using a Sensor Fusion Scheme on Android Platform

With the development of wireless communication networks, smart phones have become a necessity for people’s daily lives, and they meet not only the needs of basic functions for users such as sending a message or making a phone call, but also the users’ demands for entertainment, surfing the Internet and socializing. Navigation functions have been commonly utilized, however the navigation function is often based on GPS (Global Positioning System) in outdoor environments, whereas a number of applications need to navigate indoors. This paper presents a system to achieve high accurate indoor navigation based on Android platform. To do this, we design a sensor fusion scheme for our system. We divide the system into three main modules: distance measurement module, orientation detection module and position update module. We use an efficient way to estimate the stride length and use step sensor to count steps in distance measurement module. For orientation detection module, in order to get the optimal result of orientation, we then introduce Kalman filter to de-noise the data collected from different sensors. In the last module, we combine the data from the previous modules and calculate the current location. Results of experiments show that our system works well and has high accuracy in indoor situations

Simple Online and Realtime Tracking with a Deep Association Metric

Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms. In this paper, we integrate appearance information to improve the performance of SORT. Due to this extension we are able to track objects through longer periods of occlusions, effectively reducing the number of identity switches. In spirit of the original framework we place much of the computational complexity into an offline pre-training stage where we learn a deep association metric on a large-scale person re-identification dataset. During online application, we establish measurement-to-track associations using nearest neighbor queries in visual appearance space. Experimental evaluation shows that our extensions reduce the number of identity switches by 45%, achieving overall competitive performance at high frame rates.Comment: 5 pages, 1 figur

Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter

Cognitive radio technology addresses the problem of spectrum scarcity by allowing secondary users to use the vacant spectrum bands without causing interference to the primary users. However, several attacks could disturb the normal functioning of the cognitive radio network. Primary user emulation attacks are one of the most severe attacks in which a malicious user emulates the primary user signal characteristics to either prevent other legitimate secondary users from accessing the idle channels or causing harmful interference to the primary users. There are several proposed approaches to detect the primary user emulation attackers. However, most of these techniques assume that the primary user location is fixed, which does not make them valid when the primary user is mobile. In this paper, we propose a new approach based on the Kalman filter framework for detecting the primary user emulation attacks with a non-stationary primary user. Several experiments have been conducted and the advantages of the proposed approach are demonstrated through the simulation results.Comment: 14 pages, 9 figure

Privacy in Inter-Vehicular Networks: Why simple pseudonym change is not enough

Inter-vehicle communication (IVC) systems disclose rich location information about vehicles. State-of-the-art security architectures are aware of the problem and provide privacy enhancing mechanisms, notably pseudonymous authentication. However, the granularity and the amount of location information IVC protocols divulge, enable an adversary that eavesdrops all traffic throughout an area, to reconstruct long traces of the whereabouts of the majority of vehicles within the same area. Our analysis in this paper confirms the existence of this kind of threat. As a result, it is questionable if strong location privacy is achievable in IVC systems against a powerful adversary.\u