DBSCAN and TD Integrated Wi-Fi Positioning Algorithm

A density-based spatial clustering of applications with noise (DBSCAN) and three distances (TD) integrated Wi-Fi positioning algorithm was proposed, aiming to enhance the positioning accuracy and stability of fingerprinting by the dynamic selection of signal-domain distance to obtain reliable nearest reference points (RPs). Two stages were included in this algorithm. One was the offline stage, where the offline fingerprint database was constructed and the other was the online positioning stage. Three distances (Euclidean distance, Manhattan distance, and cosine distance), DBSCAN, and high-resolution distance selection principle were combined to obtain more reliable nearest RPs and optimal signal-domain distance in the online stage. Fused distance, the fusion of position-domain and signal-domain distances, was applied for DBSCAN to generate the clustering results, considering both the spatial structure and signal strength of RPs. Based on the principle that the higher resolution the distance, the more clusters will be obtained, the high-resolution distance was used to compute positioning results. The weighted K-nearest neighbor (WKNN) considering signal-domain distance selection was used to estimate positions. Two scenarios were selected as test areas; a complex-layout room (Scenario A) for post-graduates and a typical large indoor environment (Scenario B) covering 3200 m2. In both Scenarios A and B, compared with support vector machine (SVM), Gaussian process regression (GPR) and rank algorithms, the improvement rates of positioning accuracy and stability of the proposed algorithm were up to 60.44 and 60.93%, respectively. Experimental results show that the proposed algorithm has a better positioning performance in complex and large indoor environments.

The Estimation of the Potential for Using Smart-Trackers as a Part of a Medical Indoor-Positioning System

This research aims to estimate the feasibility of using smart-bracelets as a part of a medicine indoor-positioning system, to monitor the health status and location of patients in a hospital. The smart-bracelet takes on the role of a token of the system and can measure pulse, blood pressure and saturation and provide data transmission over the BLE. The distance between token and anchor was calculated by the RSSI. The position of a token and anchor relative to each other was determined by the trilateration method. The results of the research showed that the accuracy of the developed system in a static position is 1.46 m and exceeds 3 m in a dynamic position. Results of experiments showed that measurements from the smart bracelets are transmitted to the server of the system without distortion. The study results indicated that smart-bracelets could be used to locate patients inside a hospital or estimate their current health state. Given the low accuracy of systolic pressure measurement, it is recommended to develop an algorithm that will allow smooth measuring error for higher-precision estimation of the patient`s general health state. In addition, it is planned to improve the positioning algorithm.

Mobile Industrial Robots Localization Algorithm Based on Improved Multidimensional Scale and Received Signal Strength Indication

Aiming at the problem of large error in the location algorithm based on MDS-MAP when the distance between mobile industrial robots is not measurable, a mobile industrial robot location algorithm based on improved MDS-MAP is proposed. Experimental simulation shows that the algorithm can achieve good positioning effect. When the distance between mobile industrial robots is measurable, the positioning algorithm based on RSSI achieves good positioning effect. Therefore, this paper discusses the influence of different anchor robot selection methods on the positioning accuracy of RSSI positioning algorithm. The experimental simulation shows that when the selection method of anchoring robot is that the unknown robot with adjacent anchoring robot uses the original anchoring robot for positioning and the unknown robot without anchoring robot uses the adjacent positioning robot as the anchoring robot for positioning, its positioning effect is the best, and it can still achieve good positioning effect when there are few anchoring robots.

Small batteryless wireless solar powered GPS receivers for subcentimeter land deformation monitoring

<p>We design, implement, and validate a unique permanently deployed land deformation monitoring system using small (brick sized), cheap (approximately $100 USD), batteryless, solar powered singleband GPS wireless sensor nodes. Both hardware and software were designed, implemented, and validated by us. Constraints by our hardware and application prompted us to design a unique distributed relative static positioning algorithm designed for intermittent poor quality phase observable measurements, for sites with high multipath and high node densities requiring good solution accuracies; the static solutions were calculated on a daily basis. Our algorithm used a quarter of the bandwidth that would typically be required for an RF link used for a comparable application. GPS on time was observed to vary greatly from as little as 0.5 hours a day in winter to over 8 hours a day and summer in one of our tests. Typical solution precision was 4 mm 2DRMS. Simulations predicted an undesirable slowly changing solution bias that would repeat every year.</p>

Small batteryless wireless solar powered GPS receivers for subcentimeter land deformation monitoring

<p>We design, implement, and validate a unique permanently deployed land deformation monitoring system using small (brick sized), cheap (approximately $100 USD), batteryless, solar powered singleband GPS wireless sensor nodes. Both hardware and software were designed, implemented, and validated by us. Constraints by our hardware and application prompted us to design a unique distributed relative static positioning algorithm designed for intermittent poor quality phase observable measurements, for sites with high multipath and high node densities requiring good solution accuracies; the static solutions were calculated on a daily basis. Our algorithm used a quarter of the bandwidth that would typically be required for an RF link used for a comparable application. GPS on time was observed to vary greatly from as little as 0.5 hours a day in winter to over 8 hours a day and summer in one of our tests. Typical solution precision was 4 mm 2DRMS. Simulations predicted an undesirable slowly changing solution bias that would repeat every year.</p>

Geometric-Manifold-Assisted Distributed Navigation Probabilistic Information Fusion Cooperative Positioning Algorithm

Positioning information is the cornerstone of a new generation of electronic information technology applications represented by the Internet of Things and smart city. However, due to various environmental electromagnetic interference, building shielding, and other factors, the positioning source can fail. Cooperative positioning technology can realize the sharing of positioning information and make up for the invalid positioning source. When one node in the cooperative positioning network has error, the positioning stability of all nodes in the whole cooperative network will be significantly reduced, but the positioning probability information technology can effectively reduce the impact of mutation error. Based on this idea, this paper proposes an information-geometry-assisted distributed algorithm for probabilistic cooperative fusion positioning (IG-CP) of navigation information. The position information of different types of navigation sources is utilized to establish a probability density model, which effectively reduces the influence of a single position error on the whole cooperative position network. Combined with the nonlinear fitting characteristics of the information geometric manifold, mapping and fusion of the ranging information between cooperative nodes on the geometric manifold surface are conducted to achieve cooperative positioning, which can effectively improve the stability of the positioning results. The proposed algorithm is simulated and analyzed in terms of the node positioning error, ranging error, convergence speed, and distribution of the cooperative positioning network. The simulation results show that our proposed cooperative positioning algorithm can effectively improve the positioning stability and display better positioning performance.