What Is GEKF Algorithm For Tracking Moving Targets In A Wireless Sensor Network?

If they do not, but are within the acceptable margin of error of ±20√4=±40’’, each angle is corrected to create a sum of 360˚. For this project, the angles added to 360˚ 00’ 10’’. After each angle is corrected, angle 412 is 78˚31’03’’, angle 123 is 99˚01’23’’, angle 234 is 92˚32’21’’, and angle 341 is 89˚53’13’’. After using the Total Station, the Auto level is used to measure the elevations of each point. The known elevation outside of Powell is 380’. The Auto level is placed between each point to measure the backsight and foresight. Once all the points are measured, all the backsights and foresights can be measured with the difference taken to find the error. With this project, the error was -0.128’. Next, the pacing between each point must be taken to find the correction for each elevation with the equation, (distance from known elevation/total level network distance) X total error. The total pacing for the project is 235 steps with a pace of 2.31 feet per step. After the corrections, point 4 is 379.384’, point 3 is 378.182’, point 2 is 379.093’, and point 1 is 379.645’. A transit is used to find the true north from the magnetic

Attending to the average accuracy results, the highest accuracy obtained by the Hassanat distance which achieved the first rank in all noise levels, followed by Lorentzian distance. The Lorentzian distance achieved the second rank only at level noise 10 %, 30%, 40%, 50%, 80% and 90%, but at level noise 20%, 60%, and 80% outperformed by Canberra distance and at level noise 40 %, it outperformed by Manhattan distance, where these distances, Canberra and Manhattan achieved the second rank in theses level of noise. The Canberra distance achieved the third rank at a level of noise 80% and 50%, 40%, 30% and 10 %. This distance at level noise 10 % was equivalent to the Square chi-square distance in the result. At a level of noise 90%, the

Therefore, a theoretical uncertainty value that accommodates both uncertainties of the measurement and the equipment is calculated. The theoretical uncertainty calculation is primarily based on the propagation of error formula. The theoretical uncertainty calculation is as follows.

A WSN is a type of wireless networks that consists of collection sensor nodes which are tiny devices. Each sensor node of the network has different processing capability. It may contain multiple types of memory (program, data and flash memories), have a RF transceiver, have a power source (e.g., batteries and solar cells), and accommodate various sensors and actuators. The nodes communicate wirelessly and often self-organize after being deployed in an ad hoc fashion [13, 14]. Optimum need of each sensor node is to maximize its own utility function. Also the whole network requires resource assignments balance to perform in a useful and efficient way. This chapter presents a brief survey on WSNs showing its types, characterizing features, protocols and applications.

The various papers on the recent explorations and growth in the field of Wireless Sensor Networks and attacks in WSN’s have been reviewed and discussed below:

The most detailed parts of this paper will be the accounts of his method and hypothesis. The literature review will only briefly recall bits and pieces of other research papers due to the similarities in the topics each mentions. The results will be short because there is only so much to describe. The discussion will go more into explaining the results and their meaning.

In this project, it is challenging issue to track multi-target as camera motion is unknown. To deal with this issue and targets occlusion, the camera parameters are calculated and targets were also tracked within the 3D coordinates [W. Choi] at the same time. To be specific, it is aimed 1) to solve the multi-object tracking issue by using a single non-calibrated camera, [W. Choi] 2). to deal with complicated scenes in which multiple pedestrians are moving and occluding each other [W. Choi] at the same time, 3). to calculate the 2D/3D trajectories within the camera reference system [W. Choi].

prediction. This is due to the different layouts of the spatial boundaries used in model fitting and prediction,

In [28], it is shown that the proposed method is not correct. Sharifzadeh and colleagues [29] have proposed another version of the method that eliminates the flaws of the previous version, but it is computationally costly.

The sensor releases a pulse of light, known as the trigger, and computes the time taken for the signal to be received. The distance the signal has travelled can be directly calculated from this data. Light and sound are the most common types of signals used with this technique [1].

A number of different wireless technilogies have been developed for inddor localisation like Infra Red , Ultra Sound , RFID , WLAN, Bluetooth etc.Before the various positioning systems are discussed, we examine a few of the wireless localisation positioning algorithms developed to inproe the accuracy of localisation.

Kalman filter is a widely-used recursive technique for tracking linear dynamical systems under Gaussian noise. The Kalman filter, is an algorithm which uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those that would be based on a single measurement alone. More formally, the Kalman filter operates recursively on streams of noisy input data to produce a statistically optimal estimate of the underlying system state.

spectively. We first introduce the notations of our model and list them in Table II. Our input data,

The various detection methods are produced based on the mobility models and the network assumptions. Due to the resource constrained nature of the sensor nodes the detection methods encountered have following limitations. The paper presents the classification of various detection methods. Based on the mobility model also the detection methods have been classified which has been discussed in this paper. From the discussion it is found that Efficient Distributed Detection method suits well for detecting replicated nodes in the Wireless Sensor Networks.

Mobile positioning technology has become an important area of research, for emergency as well as for commercial services. Mobile positioning in cellular networks will provide several services such as, locating stolen mobiles, emergency calls, different billing tariffs depending on where the call is originated, and methods to predict the user movement inside a region. The evolution to location-dependent services and applications in wireless systems continues to require the development of more accurate and reliable mobile positioning technologies. The major challenge to accurate location estimation is in creating techniques that yield acceptable performance when the direct path from the transmitter to the receiver is