# Gaussian Processes for Signal Processing and Representation in Control Engineering
## 7 Gaussian Process in Tracking Problems  
Gaussian Process Regression finds its usability in some kind of tracking enhancement. For instance in [35], GPR is used to reconstruct the trajectory from the recorded location samples. The whole idea base for the application was called SensTrack. The creators of the application claim that more and more smartphones are equipped with powerful sensors such as GPS, WiFi, acceleration and orientation sensors, so all of them can be used to enhance the tracking of devices, for instance in areas with no GPS signal such as indoors, and most importantly to reduce the energy consumption overall (because constant GPS usage drains the battery) [35]. SensTrack smartly chooses the location sensing method between WiFi and GPS, and also reduces the sampling rate by using data from other sensors: acceleration and orientation. Data collected this way are analyzed and filtered to reconstruct a user's original trajectory with GPR. Critical locations, decided by the sensor hints, which captures most of the key features of trajectory, were taken for a training set of GPR. As a test set--predicted locations between the successive but faraway location samples were used. The authors used standard settings of GPR: mean equals 0, for notation simplicity, and kernel functions of square exponential one. The real-world paths were examined and showed that SensTrack only needs 7% GPS samples of the naive approach and saves nearly 90% GPS activated time, while still reconstructing the original trajectory with high accuracy and coverage [35]. The authors of [35] also created an equivalent of SensTrack, the main purpose of which was for use in the Internet of Things (IoT) [36]. The IoT concept consists of Internet-connected devices with computing capability and have an ability to share data over the Internet wirelessly [37]. The main principals are the same, but the overall project here is in an earlier state. Most of the assumptions there are connected to GPR based on other work [38], which is in fact written with the use of machine-learning principals created by [PERSON] [1], so it concentrates all means of the machine-learning approach, as many of the other examples cited in this review. The other kind of track problem, without actual use of GPS, is in [14]. Here, we encounter a problem of indoors tracking (which is the reason for the no-GPS approach). Contrary to other solutions, the authors did not base it on WiFi, Ultra Wide Band, Bluetooth nodes or similar. The whole 2D tracking algorithm is based on localization technologies, which use artificial light signals to substantiate positioning. Several methods to obtain such positioning exist: Received Signal Strength (RSS), Angle of Arrival (AoA), Time of Arrival (ToA) and Time Difference of Arrival (TDoA). The authors focus on an RSS based strategy with the intensity modulation of light sources for illumination as a localization system. This technique is called the Visible Light Positioning strategy (VLP), which consists of some Light Emitting Diodes (LED) for the purpose of fixed transmitter beacons and a photodetector as a receiver sensor [14]. The main parts of the project are the VLP Propagation model and the GP model. In this review we will mostly focus on how GP was handled here. A detailed description of the VLP model can be found in [14]. In general, the main purpose of it is to get an estimate of the coordinates of the location. The authors note that to solve the data oriented RSS-based VLP regression problem, there are many methods available currently, but using GP can handle problems very well, because of being very data-efficient and having an automatic optimization of hyperparameters and regularization. The authors use the predefined RBF kernel, with each entry of the RSS-base input and set to mean value 0. The hyperparameters of the RBF kernel were calculated by maximizing the log likelihood of data. Of course, this approach provides us with prediction, as well as an uncertainty for function values in new points [14].