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Sensor fusion with cointegration analysis for IMU in a simulated fixed-wing UAV
... If there is a linear combination of time series that has the properties of a stationary process, it means that these time series are cointegrated. The issues of forecasting cointegrated time series are covered in a number of sources both in general form [4,5,6] and in relation directly to economic [7,8,9] or technical parameters [10,11,12,13]. ...
This paper describes a pairs trading strategy using cointegration approach. If cointegrated pairs are thought of as such pairs, whose linear combination is a stationary process, that is, a process with stable statistical properties, then any deviation from these characteristics will be transient. If you know that such a deviation has happened, that is, a departure from the long-term equilibrium, you can forecast the direction of stock price movements and execute lucrative trades accordingly. When the difference between stock prices exceeds the prediction, we must sell the overpriced asset and acquire the undervalued one, then close the deals when the price ratio returns to long-term equilibrium. This is one form of statistical arbitrage trading strategy. During the research, it was discovered that cointegration is dependent on a variety of factors, including the time period under consideration, and that this is not the only issue. When more recent data is given more weight, it is suggested that methods for determining cointegration be developed. The importance of setting the conditions for entering and terminating a transaction, as well as the possibility of “disappearing” cointegration are also noted as issues with employing cointegrated pairings for pair trading.
... Under the assumptions that are mentioned, the above equations are quite reasonable to work. However, since the highly-coupled and nonlinear terms that are present in the model, further simplifications are required [11], [12]. For this need the small-disturbance theory is applied. ...
... The proposal was collecting UAV's flight data (e.g., airspeed, ground speed, position, altitude, angle of attack and sideslip) focused on providing accurate and synchronized timestamps to all measurements. Complementary to those studies analyzed above, other studies (see for example [11][12][13][14]) described alternative methods of estimating attitude and position during a UAV's flight performing data fusion algorithms. ...
The flight data of a fixed-wing Unmanned Aerial Vehicle (UAV) can be evaluated by its designers in order to analyze its performance, to validate the project criteria and to make new decisions based on the data analyses. In this paper, the authors propose the development of a low-cost instrumentation platform capable of collecting the following data: airspeed, orientation and altitude of the airplane, and the current drained by the electric system. Moreover, this paper presents the use of a telemetry system in order to display the flight conditions to the pilot. The system contains a variety of sensors, which were chosen based on their price, applicability and ease of use. After a test flight had been performed, the collected measurements were plotted and analyzed. Having the flight data, a set of flight characteristics might be observed.
Sensors are a popular source of information about the operation of complex dynamic technical systems. Considering data from sensors as a multidimensional time series is also used to describe cyber-physical systems. The article proposes a method for detecting system malfunctions based on the method of analyzing cointegration dependencies. It is determined that in the data for analysis it is possible to reveal cointegration dependences as facts of interdependence of data from different sensors. Calculations are given on the example of a system with 52 parameters. Out of 1,326 data pairs, 75 are cointegrated. The conducted analysis shows that the proposed method enables one to clearly illustrate situations with changes in behavior. Having identified cointegrated pairs, we can follow them, and if cointegration has ‘disappeared’, that is, at some new time interval we can no longer talk about the presence of a cointegration ratio, then something has changed in the process itself. In practice, this means either a change in technology (which the operator knows about), or a breakdown/accident/failure, due to equipment errors, changes in some parameters of the resources used. In the latter case, such information (that the process has changed) can be used to attract attention in general, which may ultimately lead to the need for equipment repair or maintenance or readjustment, etc. The analysis shows that the proposed method enables one to clearly illustrate situations with changes in behavior. As an example of using the method, we used the ready-made Tennessee Eastman Process (TEP) data set. Different pairs of data may have the ability to identify different errors. All errors cause a change in the behavior of one or several pairs of data, thus tracking the behavior of the value of random component enables identifying cases of deviation of the process from long-term equilibrium (in terms of cointegration), that is, cases of failure from the normal system operation. The results obtained are clear and objective and can be used by process operators or by a source for automatic process control.
In this paper, we investigate how the proposed navigation mode "dead reckoning + relative ranging + heading constraint" can be used to cooperatively localize a UAV swarm when GNSS (Global Navigation Satellite System) is denied. By focusing on the relative ranging performance, the wireless RF modules mounted on the cost-efficient UAVs are used to provide communication, ranging and positioning at the same time; however, its positioning accuracy is usually degraded by the noise in the actual RSSI (Received Signal Strength Indicator) measurements. To solve this problem, we analyze the influence of antenna pattern inhomogeneity and channel variation, respectively. The former mainly determines an antenna radiation function related to the yaw angle and relative position between the two measuring UAVs. The latter uses overlapping Allan variance to analyze and identify the measurement noises from outfield tests, that is, quantization noise, flicker noise, random walk noise and Gaussian white noise, which to some extent bridges the difference between the theoretical model and the practical measurement of RSSI. We also evaluate the sub-swarm size that guarantees robustness of RSS-based localization. In this way, an improved extended Kalman filter is proposed to predict and correct the colored noise by adaptively integrating the current peer-to-peer radio ranging performance and its Allan variance. To prove the effectiveness of this approach, simulation results based on real environment datasets and practical noise modeling are demonstrated.
For several applications, robots and other computer systems must provide route descriptions to humans. These descriptions should be natural and intuitive for the human users. In this paper, we present an algorithm that learns how to provide good route descriptions from a corpus of human-written directions. Using inverse reinforcement learning, our algorithm learns how to select the information for the description depending on the context of the route segment. The algorithm then uses the learned policy to generate directions that imitate the style of the descriptions provided by humans, thus taking into account personal as well as cultural preferences and special requirements of the particular user group providing the learning demonstrations. We evaluate our approach in a user study and show that the directions generated by our policy sound similar to human-given directions and substantially more natural than directions provided by commercial web services.
Non-linear error minimization methods became widespread approaches for solving the simultaneous localization and mapping problem. If the initial guess is far away from the global minimum, converging to the correct solution and not to a local one can be challenging and sometimes even impossible. This paper presents an experimental analysis of dynamic covariance scaling, a recently proposed method for robust optimization of SLAM graphs, in the context of a poor initialization. Our evaluation shows that dynamic covariance scaling is able to mitigate the effects of poor initializations. In contrast to other methods that first aim at finding a good initial guess to seed the optimization, our method is more elegant because it does not require an additional method for initialization. Furthermore, it can robustly handle data association outliers. Experiments performed with real world and simulated datasets show that dynamic covariance scaling outperforms existing methods, both in the presence and absence of data association outliers.
Multi-sensor systems are very powerful in the complex environments. The cointegration theory and the vector error correction model, the statistic methods which widely applied in economic analysis, are utilized to create a fitting model for homogeneous sensors measurements. An algorithm is applied to implement the model for error correction, in which the signal of any sensor can be estimated from those of others. The model divides a signal series into two parts, the training part and the estimated part. By comparing the estimated part with the actual one, the proposed method can identify a sensor with possible faults and repair its signal. With a small amount of training data, the right parameters for the model in real time could be found by the algorithm. When applied in data analysis for aero engine testing, the model works well. Therefore, it is not only an effective method to detect any sensor failure or abnormality, but also a useful approach to correct possible errors.
Popular problems in robotics and computer vision like simultaneous localization and mapping (SLAM) or structure from motion (SfM) require to solve a least-squares problem that can be effectively represented by factor graphs. The chance to find the global minimum of such problems depends on both the initial guess and the non-linearity of the sensor models. In this paper we propose an approach to determine an approximation of the original problem that has a larger convergence basin. To this end, we employ a divide-and-conquer approach that exploits the structure of the factor graph. Our approach has been validated on real-world and simulated experiments and is able to succeed in finding the global minimum in situations where other state-of-the-art methods fail.
The aim of this paper is to present a new INS/GPS sensor fusion scheme, based on state-dependent Riccati equation (SDRE) nonlinear filtering, for unmanned aerial vehicle (UAV) localization problem. SDRE navigation filter is proposed as an alternative to extended Kalman filter (EKF), which has been largely used in the literature. Based on optimal control theory, SDRE filter solves issues linked with EKF filter such as linearization errors, which severely decrease UAV localization performances. Stability proof of SDRE nonlinear filter is also presented and validated on a 3-D UAV flight scenario. Results obtained by SDRE navigation filter were compared to EKF navigation filter results. This comparison shows better UAV localization performance using SDRE filter. The suitability of the SDRE navigation filter over an unscented Kalman navigation filter for highly nonlinear UAV flights is also demonstrated.
This paper presents a novel approach for an adaptive Extended Kalman Filter (EKF), which is able to handle bad signal quality caused by shading or loss of Doppler Effect for low cost Global Navigation Satellite System (GNSS) receiver and Inertial Measurement Unit (IMU) sensors fused in a loosely coupled way. It uses the estimated position error as well as the speed to calculate the standard deviation for the measurement uncertainty matrix of the Kalman Filter. The filter is very easy to implement, because some conversions of the measurement, as well as the state variables, are made to reduce the complexity of the Jacobians, which are used in the EKF filter algorithm. The filter implementation is tested within a simulation and with real data and shows significantly better performance, compared to a standard EKF. The developed filter is running in realtime on an embedded device and is able to perform position and attitude estimation of a vehicle with low cost sensors.
Many robotics problems such as simultaneous localization and mapping (SLAM) and bundle adjustment (BA) can be formulated as a nonlinear least squares graph optimization. In these strategies, each node in the graph represents a robot position or a sensor measurement obtained at that position. Edges represent constraints between these nodes. The first part of the problem known as front-end is generating the graph. The second part, known as back-end, aims to find a configuration that best explains the constraints usually modeled as error functions. Clearly this is an expensive operation since every movement or measurement adds nodes to the graph that may become optimization hard for limited real-time applications. In order to overcome this problem, an innovative method called incremental smoothing, optimizes only a small subset of the graph nodes, reducing the computational load considerably. Besides, to explore this novel algorithm we propose a state-level hardware-in-the loop (HIL) for a small fixed-wing UAV. It consists of an aerial flight simulator extended with our sensor fusion plugin and the UAV aerodynamic model, an autopilot and a ground control station. MAVLink communications protocol interfaces these modules. Furthermore, we validate our approach with three mission scenarios exploiting different maneuvers ranging from a simple square flight to a much more challenging lawn-mower search pattern. Experiments have shown that the proposed method performed satisfactorily.
Txfonts In micro-electro-mechanical system based inertial navigation system (MEMS-INS)/global positioning system (GPS) integrated navigation systems, there exist unknown disturbances. This paper deals with the problem of adaptive robust estimation (ARE) to obtain high estimation accuracy. A filter gain matrix is designed to obtain the adaptive robust algorithm, in which the H∞ norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range, and further more the estimation error is minimized by calculating the adaptive weight matrix. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances. The proposed method is evaluated in the integrated navigation system, and the simulation results show that it is effective in position and velocity estimation.
The classical filtering and prediction problem is re-examined using the Bode-Sliannon representation of random processes and the “state-transition” method of analysis of dynamic systems. New results are: (1) The formulation and methods of solution of the problem apply without modification to stationary and nonstationary statistics and to growing-memory and infinitememory filters. (2) A nonlinear difference (or differential) equation is derived for the covariance matrix of the optimal estimation error. From the solution of this equation the coefficients of the difference (or differential) equation of the optimal linear filter are obtained without further calculations. (3) The filtering problem is shown to be the dual of the noise-free regulator problem. The new method developed here is applied to two well-known problems, confirming and extending earlier results. The discussion is largely self-contained and proceeds from first principles; basic concepts of the theory of random processes are reviewed in the Appendix.
The development of effective target tracking and collision avoidance algorithms is essential to the success of unmanned aerial vehicle (UAV) missions. In a dynamic environment, path planning for UAVs is often based on predicted obstacle and target motion. In this paper, an extended Kalman filter (EKF) is first used to estimate the states of a moving object detected by a UAV from its measured position in space. The optimal object trajectory is then predicted from the estimated object states and using the motion model defined for Kalman filtering. Finally, the quality of the predicted trajectory is evaluated by computing the variance of the prediction error. Simulation results are presented to demonstrate the effectiveness of the proposed approach.
The authors analyse the error behaviour of the robust extended Kalman filter (REKF) for nonlinear stochastic systems. On the basis of some standard results about the boundedness of stochastic processes, it is specified that stability of the REKF cannot be guaranteed. In order to solve this problem, a novel method is proposed to design the REKF so that the sufficient conditions to ensure filter stability will be fulfilled. Furthermore, an adaptive scheme is adopted to automatically tune the error covariance matrix in response to the changing environment. Numerical example shows the superiority of the proposed adaptive REKF over the usual extended Kalman filter (EKF), the REKF and the adaptive EKF.
Fusăn de sensores para um vant via suavizaçăo incremental baseada em grafos-fatores
- A C D C Rodrigues
- Rio De Janeiro
Gnss position estimation based on unscented kalman filter
- F Zbu
- Y Zhang
- X Su
- H Li
- H Guo
Uma misslo de monitoramento para 0 projeto vant-ime: Operaçăo regiao serrana-rj
- R A D R L R M E Magalhães Nete
- Jacy Montenegro