Position Algorithm Research Articles

DETECTING MULTIPATH EFFECTS ON SMARTPHONE GNSS MEASUREMENTS USING CMCD AND ELEVATION-DEPENDENT SNR SELECTION TECHNIQUE

Abstract. Regarding increasing smartphone receivers' usage in science and industry, they must improve their positioning algorithms to increase positioning accuracy and location-based software's productivity. For this goal, various studies have been presented to remove or adjust the errors in the GNSS signal received by smartphones. Nevertheless, so far, no study has been conducted to investigate the effect of the multipath effect on smartphone observations. Various algorithms have been performed to study the effect of multipath, from detecting and removing this effect by correcting errors in the signal processing step to weighting the measurements to reduce the effect of multipath on the observations in the positioning step.In this article, an attempt has been made to compare the results of two different algorithms, CMCD and Selection Signal, based on SNR elevation-dependent in detecting the effect of multipath on smartphones to check their performance for detecting measurements contaminated with multipath.

A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control.

Due to the complexity of autonomous mobile robot's requirement and drastic technological changes, the safe and efficient path tracking development is becoming complex and requires intensive knowledge and information, thus the demand for advanced algorithm has rapidly increased. Analyzing unstructured gain data has been a growing interest among researchers, resulting in valuable information in many fields such as path planning and motion control. Among those, motion control is a vital part of a fast, secure operation. Yet, current approaches face problems in managing unstructured gain data and producing accurate local planning due to the lack of formulation in the knowledge on the gain optimization. Therefore, this research aims to design a new gain optimization approach to assist researcher in identifying the value of the gain's product with a qualitative comparative study of the up-to-date controllers. Gains optimization in this context is to classify the near perfect value of the gain's product and processes. For this, a domain controller will be developed based on the attributes of the Fuzzy-PID parameters. The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). It is expected that the domain controller will give a positive impact to the path planning position and angular PID controller algorithm that meet the autonomous demand.

Framework to Emulate Spacecraft Orbital Positioning Using GNSS Hardware in the Loop

The paper presents a framework to emulate spacecraft orbits using GNSS hardware in the loop that enables the evaluation of new orbital positioning algorithms. The framework software generates the spacecraft orbit and the GNSS signals, including the most common perturbations. These signals are modulated and transmitted by a software-defined radio and received by a commercial GPS receiver. The system is validated using a test orbit, where the GPS receiver accurately determines the spacecraft positions. Moreover, using raw data provided by the receiver, the spacecraft positions have also been determined by software for a low earth orbit, in which civil GPS receivers do not work.

Indoor high-precision visible light positioning system using Jaya algorithm.

Several indoor positioning systems that utilize visible light communication (VLC) have recently been developed. Due to the simple implementation and high precision, most of these systems are dependent on received signal strength (RSS). The position of the receiver can be estimated according to the positioning principle of the RSS. To improve positioning precision, an indoor three-dimensional (3D) visible light positioning (VLP) system with the Jaya algorithm is proposed. In contrast to other positioning algorithms, the Jaya algorithm has a simple structure with only one phase and achieves high accuracy without controlling the parameter settings. The simulation results show that an average error of 1.06 cm is achieved using the Jaya algorithm in 3D indoor positioning. The average errors of 3D positioning using the Harris Hawks optimization algorithm (HHO), ant colony algorithm with an area-based optimization model (ACO-ABOM), and modified artificial fish swam algorithm (MAFSA) are 2.21 cm, 1.86 cm and 1.56 cm, respectively. Furthermore, simulation experiments are performed in motion scenes, where a high-precision positioning error of 0.84 cm is achieved. The proposed algorithm is an efficient method for indoor localization and outperforms other indoor positioning algorithms.

An Improved Particle Filter Map Matching Algorithm for Personal Inertial Positioning

An Improved Particle Filter Map Matching Algorithm for Personal Inertial Positioning

Indoor Visible Light Positioning Based on Improved Whale Optimization Method With Min-Max Algorithm

Recently, the Min-Max algorithm has been widely used in various indoor positioning systems due to its simplicity and robustness. In this article, an improved whale optimization algorithm (IWOA) associated with the Min-Max algorithm is proposed to improve the positioning accuracy concerning the original Min-Max algorithm for visible light positioning (VLP) systems. In the proposed algorithm, the three-dimensional (3-D) region of interest (RoI) affiliated with the target node (TN) is first obtained using the original Min-Max algorithm, and then the IWOA is employed to find the accurate position of the TN in the 3-D RoI with the lowest fitness value. In software simulations, the signal-to-noise ratio (SNR) value of the VLP system is 15 dB and the standard deviation of measured distance noise is 1 m. Our software simulation results show that in the case of two-dimensional (2-D) positioning, the averaged positioning error (APE) of the proposed IWOA-Min-Max algorithm is decreased by 84.6%, 52.1%, 1.9%, and 19.93%, respectively, for the original Min-Max algorithm, the extended Min-Max algorithms, the whale optimization algorithm (WOA), and the least square estimation (LSE). Furthermore, in the case of 3-D positioning, it is also reduced by 81.7%, 75.4%, 8.5%, and 63.9%, respectively, compared with that of the above four existing algorithms. Finally, hardware-in-the-loop simulation (HILS) results are also provided to verify the effectiveness of the proposed IWOA-Min-Max algorithm.

A Neural-enhanced Factor Graph-based Algorithm for Robust Positioning in Obstructed LOS Situations

A Neural-enhanced Factor Graph-based Algorithm for Robust Positioning in Obstructed LOS Situations

Radar Interferometric Phase Ambiguity Resolution Using Viterbi Algorithm for High-Precision Space Target Positioning

Radar Interferometric Phase Ambiguity Resolution Using Viterbi Algorithm for High-Precision Space Target Positioning

Novel Chromatic Differential Confocal Matrix Sensor Using Multiband Spectral Images

In this letter, a novel chromatic differential confocal matrix (CDCM) sensor is proposed and demonstrated for accurate and fast three-dimensional (3D) profiling without mechanical scanning. In contrast to conventional chromatic confocal microscopy (CCM) methods that require spectral expansion and a peak-intensity-wavelength detection algorithm for axial positioning, our CDCM employs the intensity difference between a pair of spectral confocal images to accurately discriminate surface height. Due to the elimination of spectral expansion required by a conventional CCM, the CDCM has an at least one order of magnitude higher axial sensitivity and surface profiling efficiency than a conventional CCM. In the CDCM, axial scanning is achieved by residual chromatic aberration of an achromatic objective and by constructing a difference intensity signal between two spectral confocal images. Spectral confocal images are acquired in a parallel manner by using a digital micromirror device as an array of illumination pinholes and digitally applying the virtual detection pinhole concept. Quantitative evaluation experiments with the proof-of-principle sensor system indicate that with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> NA 0.25 objective, the CDCM can achieve better than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.1~\mu \text{m}$ </tex-math></inline-formula> axial accuracy with an axial measurement range of approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$28~\mu \text{m}$ </tex-math></inline-formula> , a lateral resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.87~\mu \text{m}$ </tex-math></inline-formula> .

Distributed Spatio-Temporal Information Based Cooperative 3D Positioning in GNSS-Denied Environments

A distributed spatio-temporal information based cooperative positioning (STICP) algorithm is proposed for wireless networks that require three-dimensional (3D) coordinates and operate in the global navigation satellite system (GNSS) denied environments. Our algorithm supports any type of ranging measurements that can determine the distance between nodes. We first utilize a finite symmetric sampling based scaled unscented transform (SUT) method for approximating the nonlinear terms of the messages passing on the associated factor graph (FG) with high precision, despite relying on a small number of samples. Then, we propose an enhanced anchor upgrading mechanism to avoid any redundant iterations. Our simulation results and analysis show that the proposed STICP has a lower computational complexity than the state-of-the-art belief propagation based localizer, despite achieving an even more competitive positioning performance.

Distributed Bearing-Only Formation Control for Heterogeneous Nonlinear Multi-Robot Systems

This paper addresses the bearing-only formation tracking problem for heterogeneous nonlinear multi-robot systems. In contrast to position and distance-based formation algorithms, the robots can only measure the bearing information from their neighbors to achieve cooperation while the state information is unavailable. This characteristic is able to be implemented in the hardware to reduce the requirements of the sensors. We construct a compensation function in the proposed controller to eliminate the effect of the unknown nonlinear terms in the system. This compensation function is also based on bearing measurements, which guarantees that the overall controller is bearing-only. The stability of the proposed formation tracking strategy can be ensured by Lyapunov techniques. Moreover, we analyze the performance of the protocol for moving leaders, where the formation tracking error can be restricted in a bounded set. Finally, the simulation results are presented to validate the feasibility of the proposed algorithm for both fixed and moving leaders.

Remote Pedestrian Localization Systems for Resource-Constrained Environments: A Systematic Review

The steady increase in the number of elderly citizens represents a likelihood of an increased burden on the family, government, healthcare, and social services since many of these elderly people cannot live independently without assistance from a caregiver. As such, there is an increase in demand for services in terms of technologies to address the urgent needs of the aging population. Remote monitoring, which is based on non-invasive, non-intrusive, and wearable sensors, actuators, and communication and information technologies, offers efficient solutions that bridge the gaps between healthcare and where elderly people really want to live every day. The rate at which such platforms have been adopted is extremely low in low-developed countries and rural areas, one of the main reasons being the lack or scarcity of some resources that these systems take for granted. In other words, these systems are designed for developed countries but are very much needed in resource-constrained environments as well. This study provides an in-depth, state-of-the-art systematic review of the current outdoor remote pedestrian localization systems to identify their suitability for resource-constrained environments. After checking 35 survey papers from the last ten years to the best of our knowledge, this is the first survey that investigates the suitability of existing pedestrian localization systems for a resource-constrained environment. This study is based on PRISMA guidelines to provide a replicable work and report the studies’ main findings. A total of 37 works published between 2012, and January 2023 have been identified, analyzed, and key information that described the devices and tools used, communication technologies, position estimate technologies, methods, techniques and algorithms, and resource optimization strategies currently used by the localization systems was extracted to help us answer our question. The results indicate they are not fit for a resource-constrained environment as most assume the availability of infrastructures such as Wi-Fi, Internet, cellular networks, and digital literacy, among others, for their systems to operate properly, which are limited or not available in the resource-constrained environment described in this review.

A Cooperative Positioning Algorithm via Manifold Gradient for Distributed Systems

A Cooperative Positioning Algorithm via Manifold Gradient for Distributed Systems

Intelligent Cross-axis Solar Power Tracking System

The sun is an abundant and powerful renewable energy resource that plays a crucial role in sustainable energy production. With efficient power generation in mind, here is a system designed to make intelligent judgements based on a condition so that the solar panel is always tilted towards the maximum sunlight. The hybrid intelligent cross-axis solar power tracking system is built using the microcontroller, light sensors, solar panel, and motors. Here, there are two methods used namely, the Sun Position Algorithm and the other is using the sensors. The algorithm uses the azimuth angle and the sun’s altitude to identify the position of the sun depending on the geo location. For maximum power generation from the solar panel, we use the sensor-based technique tocome into picture whenever the output voltage is less than the fixed threshold voltage using the Sun Position Algorithm.

Techno-Environmental Feature-based Sun Tracking Model: Fabrication & Optimal Analyses by Precise Position Algorithm

Techno-Environmental Feature-based Sun Tracking Model: Fabrication & Optimal Analyses by Precise Position Algorithm

Mobile Anchor Route Scheduling with an Iterative Sensor Positioning Algorithm in Wireless Sensor Networks

Locating deployed sensor nodes in wireless sensor networks is a crucial issue for general environmental monitoring applications. Most current positioning technologies use stationary anchors to compute the positions of sensor nodes. However, placing the static anchors in the monitoring area is not a trivial job, especially in hazardous environments. Applying a mobile anchor is a good solution. This paper proposes an iterative sensor positioning (ISP) algorithm that uses the neighboring-positioned nodes to assist the mobile anchor in locating the unknown nodes. In addition, a mobile anchor route scheduling method, named MARS, is also proposed to shorten the traveling distance of the mobile anchor. MARS uses a projection mechanism and the ISP algorithm to reduce the traveling distance. Simulation results indicate that MARS can reduce the traveling distance of the mobile anchor by about 21–49% compared with the SCAN method. In the unfavorable scenario of the projection mechanism, MARS still provides the mobile anchor with a traveling distance that is 34% less than the SCAN method. The mobile anchor, when applying the MARS method, is able to locate all unknown nodes in only 45% to 70% of the traveling distance required by the one using the SCAN method in the scenario favorable to the ISP algorithm, while still reducing the traveling distance by 15% to 21% in the scenario unfavorable to the ISP algorithm. In the non-ideal signal scenario, the positioning size of the positioning area of the ISP algorithm when using the MARS method is about 53% to 74% less than when using the Triangulation algorithm.

Research on UWB Indoor Positioning Algorithm under the Influence of Human Occlusion and Spatial NLOS

Ultra-wideband (UWB) time-of-flight (TOF)-based ranging information in a non-line-of-sight (NLOS) environment can display significant forward errors, which directly affect positioning performance. NLOS has been a major factor limiting the improvement of UWB positioning accuracy and its application in complex scenarios. Therefore, in order to weaken the influence of the indoor complex environment on the NLOS environment of UWB and to further improve the performance of positioning, in this paper, we first analyze the factors and characteristics of NLOS formation in an indoor environment. The NLOS is divided into fixed NLOS influenced by spatial structure and dynamic random NLOS influenced by human occlusion. Then, the anchor LOS/NLOS information map is established by making full use of indoor spatial a priori information. On this basis, a robust adaptive extended Kalman filtering algorithm based on the anchor LOS/NLOS information map is designed, which is not only effectively able to exclude the influence of spatial NLOS, but can also optimize the random error. The proposed algorithm was validated in different experimental scenarios. The experimental results show that the positioning accuracy is better than 0.32 m in complex indoor NLOS environments.

A Novel UWB Positioning Method Based on a Maximum-Correntropy Unscented Kalman Filter

Aiming at the problem of measurement-information abnormal-error and nonlinear filtering in UWB navigation and positioning, an ultra wideband position algorithm based on a maximum cross-correlation entropy unscented Kalman filter is proposed. The algorithm first obtains the predictive state estimate and the covariance matrix through traceless transformation. Then, it reconstructs observation information using the nonlinear regression method based on the maximum cross-correlation entropy criterion, which enhances the robustness of the unscented Kalman filter algorithm for heavy-tailed noise. The simulation and actual test results show that this algorithm has better positioning accuracy and stability than the traditional filter algorithm in a non Gaussian noise environment. This algorithm effectively solves the problem that UWB indoor location is easily affected by indoor environments, resulting in fixed deviation for that location.

Robust sequential adaptive Kalman filter algorithm for ultrashort baseline underwater acoustic positioning

Affected by dynamic changes in the complex marine environment, ultrashort baseline (USBL) systems may exhibit continuous gross errors in underwater target positioning, resulting in the distortion of the target coordinates. To effectively detect and eliminate continuous gross errors in USBL underwater acoustic positioning, a robust sequential adaptive Kalman filter (RSAKF) algorithm is proposed in this paper. The RSAKF algorithm employs sequential filtering to decompose all measurement updates into multiple submeasurement updates and uses the fading memory weighted average method to estimate the one-step prediction mean square error of the metrics for each submeasurement update. Then, the RSAKF algorithm adopts an adaptive correction method of submeasurement noise variance, which eliminates the influence of continuous gross errors through a more targeted adaptive correction of each submeasurement noise variance. The effectiveness of the algorithm was quantitatively analyzed using a USBL positioning simulation experiment, and the results showed that the continuous gross errors rejection rate of the RSAKF algorithm reached 84.12%. The point error of the RSAKF algorithm is improved by 62.65%, 46.76%, 36.09%, and 26.48% compared with the Kalman filter (KF), KF based on Huber, KF based on Institute of Geodesy and Geophysics, and the maximum correntropy KF, respectively. The USBL positioning remotely operated vehicle experiment was conducted in the South China Sea, and the results showed that the RSAKF has the best filtering accuracy. Simulation and actual measurement experiments verified that the RSAKF algorithm can effectively eliminate the influence of continuous gross errors.

Feedback-based algorithm for aerial cell's trajectory using deep learning for efficient performance in 6G

Sixth (6th) Generation (6G) wireless communication proposes Non-Terrestrial aerial Network (NTN) architecture to support dynamic scaling of both coverage and capacity. In this paper, we discuss Low Altitude Platform (LAP) based aerial cell for NTN which augments the terrestrial cell through Dual Connectivity (DC) or Carrier Aggregation (CA) on an independent and non-interfering frequency carrier. The determination of an optimal trajectory for a LAP based aerial cell is critical in the deployment for improving its resource utilization within the given flight time. The available literature on selecting trajectory for drone cell have independently considered the data traffic requirements or the mobility of the User Equipment (UE) while improving the throughput or coverage respectively. We propose for the first time; a novel Zone-based Drone Positioning and Trajectory algorithm for selecting UEs to be served by a drone cell as well as for choosing the position and trajectory of the LAP based aerial cell. The proposed algorithm utilizes a new feedback quantity from the UE called Feedback for Aerial Cell Trajectory (FACT), which contains UE's predicted data traffic, traffic preference, and mobility state. The algorithm and FACT are presented mathematically with a combination of closed-form equations with a deep learning (Long Short-Term Memory (LSTM)) model and evaluated through extensive simulations. The simulation shows improvement by 44% in spectrum resource utilization of an aerial cell and a 27% improvement in user throughput over available schemes in the literature.