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Nowadays, finding the Unmanned Aerial Vehicle (UAV) position in the absence of GPS is attractive and challenging problem in the research community. In this paper, we present a novel algorithm for mini UAV indoor localization based on distance measurements between the UAV and the existing infrastructure consisting of WiFi Access Points. Our algorithm uses two well-known techniques from the literature: Multi-dimensional Scaling (MDS) and Weighted Centroid Localization (WCL). Through extensive simulations we have shown that our algorithm is very suitable for indoor localization of mini UAVs. For small radio-range error, our algorithm exhibits a small localization error of less than 5% of the radio range.
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... As a result, the cost of deploying base stations increases accordingly. The work [8] proposed indoor positioning of UAVs using WiFi signal ranging. However, this method needs to obtain the exact location of each AP in advance to achieve distance-based WiFi localization. ...
... The feature extraction of feature space φ(F) is performed to calculate the projection from φ(F) to the feature vector space. The jth sample is projected to the kth coordinate axis V k , as shown in Equation (8). ...
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To improve the limited number of fixed access points (APs) and the inability to dynamically adjust them in fingerprint localization, this paper attempted to use drones to replace these APs. Drones have higher flexibility and accuracy, can hover in different locations, and can adapt to different scenarios and user needs, thereby improving localization accuracy. When performing fingerprint localization, it is often necessary to consider various factors such as environmental complexity, large-scale raw data collection, and signal strength variation. These factors can lead to high-dimensional and complex nonlinear relationships in location fingerprints, thereby greatly affecting localization accuracy. In order to overcome these problems, this paper proposes a kernel global locally preserving projection (KGLPP) algorithm. The algorithm can reduce the dimensionality of location fingerprint data while preserving its most-important structural information, and it combines global and local information to avoid the problem of reduced information and poor dimensionality reduction effects, which may arise from considering only one. In the process of location estimation, an improved weighted k-nearest neighbor (IWKNN) algorithm is adopted to more accurately estimate the target’s position. Unlike the traditional KNN or WKNN algorithms, the IWKNN algorithm can choose the optimal number of nearest neighbors autonomously, perform location estimation and weight calculation based on the actual situation, and thus, obtain more-accurate location estimation results. The experimental results showed that the algorithm outperformed other algorithms in terms of both the average error and localization accuracy.
... In [10], the authors present a novel algorithm for indoor localization of UAVs based on RSSI, with Wi-Fi access points and a priori known locations. ...
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UAV communications have seen a rapid rise in the last few years. The drone class of UAV has particularly become more widespread around the world, and illicit behavior using drones has become a problem. Therefore, localization, tracking, and even taking control of drones have also gained interest. Knowing the frequency of a target signal, its position can be determined (as the angle of arrival with respect to a fixed receiver point) using radio frequency-based localization techniques. One such technique is represented by the subspace-based algorithms that offer highly accurate results. This paper presents the implementation of the MUSIC algorithm on an SDR-based system using a uniform circular antenna array and its experimental evaluation in relevant outdoor environments for drone localization. The results show the capability of the system to indicate the AoA of the target signal. The results are compared with the actual direction computed from the log files of the drone application and validated with a professional direction-finding solution (i.e., Narda SignalShark equipped with the automatic direction-finding antenna).
... However, vision-based localization systems present a higher cost of acquisition per vehicle compared to other technologies. Moreover, the scanning rate of these devices can compromise the accuracy of localization in harsh conditions [17] such as varying illumination conditions or high-density obstacle environments, both common situations within industrial environments like warehouses [18][19][20]. ...
... Among the most important topics in WSNs, localization is one of the key tasks for determining or tracking the locations of target objects. Thus, location information is valuable in various realworld scenarios, including monitoring and tracking of human in homes and buildings, or during emergency situations [6][7][8][9][10], elderly and patient tracking in healthcare centers and hospitals [11,12], worker tracking in tunnels and construction sites [13,14], position detection of products in warehouses [6], mobile robot and unmanned vehicle tracking [15,16], location-based services in shopping malls [17,18], and so on. ...
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... Also, they require complex designs for the interrogation nodes, in addition to sophisticated pattern recognition algorithms [11]. ...
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Localization in Wireless Sensor Networks (WSNs) has been a challenging problem in the last decade. The most explored approaches for this purpose are based on multidimensional scaling (MDS) technique. The first algorithm that introduced MDS for nodes localization in sensor networks is well known as MDS-MAP. Since its appearance in 2003, many variations of MDS-MAP have been proposed in the literature. This paper aims to provide a comprehensive survey of the localization techniques that are based on MDS. We classify MDS-based algorithms according to different taxonomy features and different evaluation metrics.
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In this paper, a Visible Light Positioning (VLP) system using Frequency Division Multiple Access (FDMA) with square waves is presented. For the multiple access technology, the properties of square waves in the frequency domain is exploited. Neighboring LEDs will use multiples of the ground frequency of the first LED where the receiver performs a Fast Fourier Transform (FFT) and retrieves the Received Signal Strength (RSS) for every LED. In order to facilitate implementation, the LEDs are transmitting pilot tones in a non-synchronized way to the receiver, and thus requiring no backbone network. The positioning algorithm uses the RSS to perform triangulation and finds the position by taking the least square fit. Practical results show that this VLP system has position errors smaller then 10 cm when an photodiode is used which has a Field Of View (FOV) of 70°. Simulation results show that when a photodiode with a FOV of 90° is applied, the position error can drop to a few centimeter within the entire test bench surface. The key advantage of the system is the use of unsynchronized low bandwidth transmitters, leading to an easy implementation using current high efficiency LED drivers.