This paper considers distributed estimation methods to enable the formation of Unmanned-Aerial-Vehicles (UAVs) that track a moving target. The UAVs (or agents) are equipped with communication devices ...to receive a beacon signal from the target and share information with neighboring UAVs. The shared information includes the time-of-arrival (TOA) of the beacon signal from the target and estimates on the target's location. Every UAV processes the received information from the neighbors using a single-time-scale distributed estimation protocol. This differs from multi-time-scale protocols that require (i) many consensus iterations on a-priori estimates, (ii) fast communication among agents (in general, much faster than the sampling rate of the target dynamics), and thus, more-costly communication equipment and processing units. Further, our approach outperforms most single-time-scale methods in terms of observability assumption as these methods assume that the target is observable via the measurement data received from neighboring UAVs (referred to as local-observability). This requires more communications among the sensors. In contrast, our approach is only based on global-observability assumption, and thus, requires less networking (only strong-connectivity) and communication traffic along with less computational load by data-processing once at the same time-scale of sampling target dynamics. We consider modified time-difference-of-arrival (TDOA) measurements with a constant output matrix for the linearized model. UAVs make a pre-specified formation, and by estimating the target's location via these measurements, move along with the target. Note to Practitioners-Inspired by recent development in industrial UAVs along with emerging progress in low-cost processing units, fog computing systems, and wireless communications, this paper considers mobile tracking of a moving target via a group of wireless-connected autonomous drones. In the classical tracking methods, which are prone to single-point-of-failure, all the sensors need to send their information to a ground central station over a long-range and costly data-transmission channel. In contrast, by collaborative tracking the processing and decision-making are distributed among a swarm of drones equipped with onboard miniaturized electronic parts such as sensors, microcontrollers, microprocessors, and communication units. This article provides an efficient algorithm to enable such drones to autonomously track the moving target in real-time. Note that the cost and tracking ability of the UAV swarm are directly determined by the computational efficiency and communication burden of the estimation algorithm. In this regard, most available estimation algorithms are over budget and even infeasible due to the need for fast data-transmission channels, fast CPUs, and high network traffic. Our proposed estimation technique outperforms similar algorithms in terms of required communication bandwidth, data-transmission rate, and computational resources, which considerably reduces the hardware cost and improves tracking efficiency in real-time large-scale applications. We show the feasibility and efficiency of our distributed tracking method by simulation.
Real time location/unknown target position is important in Electronic Warfare (EW) system. To find the location the hyperbolic multilateration method is used. Many algorithms are available to solve ...nonlinear hyperbolic equations. The techniques which are mostly used for solving and determining non-linear measurements are the Taylor Series method and Ezzat’s approach. Taylor series approach computes the position fix in an iterative fashion where as Ezzat’s solution gives a direct solution. In this paper to solve the non-linear measurements which are in the hyperbolic form we used two types of techniques. These two techniques are implemented on different receiver/ sensors distributions ex. square, triangle etc. In this paper we explores the optimal value for different receiver combinations and also we compares the convergence issues, relative performance for all combinations and in three dimensions. Finally we determined the standard deviation for every case and compared it for better optimal solution.
A reliable epicenter estimation method is proposed for Global Positioning System (GPS) derived seismic signal for far-field regional earthquake. The main contribution is the use of time-frequency ...analysis to estimate the time of arrival (TOA) using multilateration technique. The data from the 2004 Sumatra Andaman earthquake captured from four GPS continuously operating reference stations (GPS CORS) were used in the analysis. To validate the accuracy of the proposed method, the estimated epicenter location was compared with the data released by the United States Geological Survey (USGS). The estimated location shows an error of about 0.0572 degrees in latitude and 0.2848 degrees in longitude. The proposed analysis method could complement existing seismometer measurements, improve in understanding of geo-seismic phenomena, and plan future infrastructure development.
Abstract
A mobile multilateration measurement system developed at the Physikalisch-Technische Bundesanstalt (PTB) around 2010 has been thoroughly investigated and refined to gain better performance ...with smaller uncertainties even when applied to the calibration of large complex workpieces. The mathematical background of multilateration and the propagation of uncertainties for the algorithms involved is explained in detail. Using the example of simple 1D and 2D measuring tasks, the influence of certain parameters characterizing the setup of the measurement system on the overall uncertainty is quantified. A strategy is developed to incorporate multi-stylus measurements which are often inevitable when workpieces feature complex shapes. The findings are verified on a large involute gear which is 2 m in diameter. All measurements are performed on PTB’s large coordinate measuring machine with a working range of
5
m
×
4
m
×
2
m
.
•Redundancy of the intermediate parameters in the identification is avoided.•Identification model for the rotating component pose error twists is established.•Separation algorithm for PIGEs & PDGEs ...using least squares condition is developed.•Stability against random errors is enhanced, thus improving accuracy and precision.•Additional constraints that could complicate the solving are no longer needed.
The multilateration measurement acquires sufficient data for the identification of the position-independent geometric errors (PIGEs) and position-dependent geometric errors (PDGEs) of the rotary axis. However, because of redundancy in the used intermediate parameters, existing identification methods suffer from a loss of accuracy and precision caused by the relatively lower stability to the influences of random errors. Meanwhile, this may result in a more complicated process if additional constraints are added to reinforce stability. This paper proposes a non-redundant method for identifying both the PIGEs and PDGEs of the rotary axis. Firstly, the identification model, in which the rotating component pose errors are fully and non-redundantly described by the twists, is established to identify the pose errors caused by geometric errors. Then, by deriving the relationship between the pose error twists and the PIGE & PDGE parameters, the sequential decoupling algorithm applying the least squares condition, together with identification models, for both the PIGEs and PDGEs is developed. Comparative simulations and experiments are carried out. With no redundancy, the proposed method exhibits smaller discrepancies between the identified and preset test lengths in the cone path test simulation, yielding an average improvement of 15.73% and 56.97% in two identification modes, respectively. Furthermore, the method demonstrates smaller discrepancies between the predicted and measured test data in the cross-validation experiment with an average improvement of 80.21% and 86.87% in the two modes, respectively. These results jointly verify the improvement in accuracy for identifying the PIGE and PDGE parameters. In addition, an overall reduction of the PIGE and PDGE uncertainties is observed in the Monte-Carlo simulations, which verifies the precision improvement.
Display omitted
Multilateration based approaches are widely accepted as the most adequate solution for geometric characterisation of medium and large machine tools. However, its application, either in a sequential ...mode or in a simultaneous approach, leads to industrial limitations such as total time consumption or thermal drift that prevent an automatic calibration. This work presents an integrated multilateration verification procedure where a tracking interferometer is directly attached to the manufacturing system spindle as a tool, which tackles several of the mentioned limitations. Results of both simulations and experimental tests show that levels of uncertainty in the range of micrometres can be guaranteed.
As automatic dependent surveillance–broadcast (ADS-B) becomes more prevalent, the placement of on-ground sensors is vital for Air Traffic Control (ATC) to control the airspace. However, the current ...sensors are placed in an unstructured way that keeps some areas without coverage, and others are over-densified by sensors. Therefore, areas with coverage anomalies may cause issues that inhibit accurate ADS-B verifications as well as the availability of ADS-B altogether. In this paper, we tackle the ADS-B-specific optimal sensor placement (OSP) problem. Of importance are both the optimal coverage and the secure and accurate verification of received ADS-B messages. Specifically, we take into account the following objectives. First, we determine the minimum required number of sensors in order to cover a certain area like Europe. Second, we produce a better placement of the current sensors with respect to the security and accuracy of geometric dilution of precision (GDOP). Finally, we calculate how far the current sensor setup is from our derived optimal solution as well as the cost to reach the optimality. Our experiments show that the ideal fitness score for solving the OSP is below 0.1, meaning that the mean squared error (MSE) of the required and achieved GDOPs is significantly small, thus accomplishing a near-optimal setup.
One of the problems associated with methods using to determine the location of users of mobile devices within indoors, based on Wi-Fi radio signals, is the time-consuming procedure for setting up and ...placing equipment, which includes building a map of the room, creating a map of radio signals, or calibrating the radio signal propagation model. In solving this problem, it is planned to use complex indoor localization technique based on the usage of ontology and the SLAM method, which includes the phase of forming a training sample, as well as the phase of simultaneous navigation and mapping. The SLAM method is based on The Gaussian Process Latent Variable Model (GP-LVM) and includes requirements for correlation of the signal level values of the nearest points of the user's localization, for which the parameters of the correlation function are configured based on the training sample. The proposed method is based on solving the regression problem using machine learning methods to form a training sample, as well as solving the problem of reducing the dimension for simultaneous navigation and map construction. As a training sample, the smartphone's internal sensor readings (steps and rotation angles) and Wi-Fi received signal strength values obtained using crowd calculations are used. The resulting training sample is used to determine the parameters of the correlation function that sets the correlation between the user's localization points. The proposed ontology is intended to determining the user's entrance to the room and searching for Wi-Fi access points.
PDF
