Abstract
This paper reports a real-time localization algorithm system that has a main function to determine the location of devices accurately. The model can locate the smartphone position passively (which do not need a set on a smartphone) as long as the Wi-Fi is turned on. The algorithm uses Intersection Density, and the Nonlinear Least Square Algorithm (NLS) method that utilizes the Lavenberg-Marquart method. To minimize the localization error, Kalman Filter (KF) is used. The algorithm is computed under Matlab approach. The most obtained model will be implemented in this Wi-Fi tracker system using RSSI-based distance for indoor crowd monitoring. According to the experiment result, KF can improve Hit ratio of 81.15 %. Hit ratio is predicting results of a location that is less than 5 m from the actual area (location). It can be obtained from several RSSI scans, the calculation is as follows: the number of non-error results divided by the number of RSSI scans and multiplied by 100%.
Highlights
In 4.0 era, crowd monitoring/tracking system has become very useful application because it provides some summaries and insights about flow, direction, density and activity of people in certain public and private areas
The methods can be used in the crowd monitoring system, e.g., image-based and non-image-based method
That approach has several other disadvantages: it only covers a small line-of-sight (LoS) area and difficult to obtain high estimation accuracy when overlap and occlusion exist in the crowd
Summary
In 4.0 era, crowd monitoring/tracking system has become very useful application because it provides some summaries and insights about flow, direction, density and activity of people in certain public and private areas. A prior work described the use of a real-time Wi-Fi tracking system for business intelligence in the retail company [1]. The methods can be used in the crowd monitoring system, e.g., image-based and non-image-based method. Image or videobased system requires high-cost and complex computation. That approach has several other disadvantages: it only covers a small line-of-sight (LoS) area and difficult to obtain high estimation accuracy when overlap and occlusion exist in the crowd. The non-image-based method can overcome drawbacks in image-based methods, especially in the cost factor, and it can cover a high LoS area
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