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Global Positioning System Theory And Practice

Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorithms have been conventional to training RBF network in the recent years. This study uses Stochastic Fractal Search Algorithm (SFSA) for training RBF NNs. The particles in the new algorithm explore the search space more efficiently by using the diffusion property, which is observed regularly in arbitrary fractals. To assess the performance of the proposed classifier, this network will be evaluated with the two benchmark datasets and a high-dimensional practical dataset (i.e., sonar). Results indicate that new classifier classifies sonar dataset six percent better than the best algorithm and its convergence speed is better than the other algorithms. Also has better performance than classic benchmark algorithms about all datasets.

Training Radial Basis Function Neural Network using Stochastic Fractal Search Algorithm to Classify Sonar Dataset

Inertial navigation is an edge computing-based method for determining the position and orientation of a moving vehicle that operates according to Newton’s laws of motion on which all the computations are performed at the edge level without need to other far resources. One of the most crucial struggles in Global Positioning System (GPS) and Inertial Navigation System (INS) fusion algorithms is that the accuracy of the algorithm is reduced during GPS interruptions. In this paper, a low-cost method for GPS/INS fusion and error compensation of the GPS/INS fusion algorithm during GPS interruption is proposed. To further enhance the reliability and performance of the GPS/INS fusion algorithm, a Robust Kalman Filter (RKF) is used to compensate the influence of gross error from INS observations. When GPS data is interrupted, Kalman filter observations will not be updated, and the accuracy of the position will continuously decrease over time. To bridge GPS data interruption, an artificial neural network-based fusion method is proposed to provide missing position information. A well-trained neural network is used to predict and compensate the interrupted position signal error. Finally, to evaluate the effectiveness of the proposed method, an outdoor test using a custom-designed hardware, GPS, and INS sensors is employed. The results indicate that the accuracy of the positioning has improved by 67% in each axis during an interruption. The proposed algorithm can enhance the accuracy of the GPS/INS integrated system in the required navigation performance.

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ANN-assisted robust GPS/INS information fusion to bridge GPS outage

Nowadays, the low-cost SINS/GPS integrated navigation system has been widely used. Generally, the integrated system works well in providing reliable navigation information. However, the GPS signal may be lost easily and the navigation accuracy will deteriorate badly without compensation. In order to overcome the limitation, a hybrid prediction method that combines the neural network and extended Kalman filter (EKF) is proposed. The neural network is trained when GPS signal is available and then it is used to forecast the measurement of EKF during GPS outages. In recent years, extreme learning machine (ELM) has attracted much attention and interest among scientific community for its extremely fast learning speed and superior generalization performance. Thus in this paper, the ELM is adopted and compared with Radial Basis Function neural network (RBFNN). The simulation result shows that the accuracy of the integrated navigation system is significantly improved by applying the proposed method and the ELM performs better in real-time capacity and generalization ability.

Integrating Extreme Learning Machine with Kalman Filter to Bridge GPS Outages

In this paper, a novel algorithm based on the combination of a fading filter (FF) and an extreme learning machine (ELM) is presented for Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems. In order to increase the filtering accuracy of the model, a variable fading factor fading filter based on the fading factor is proposed. It adjusts the fading factor by the ratio of the estimated covariance before and after the moment which proves to have excellent performance in our experiment. An extreme learning machine based on a Fourier orthogonal basis function is introduced that considers the deterioration of the accuracy of the navigation system during GPS outages and has a higher positioning accuracy and faster learning speed than the typical neural network learning algorithm. In the end, a simulation and real road test are performed to verify the effectiveness of this algorithm. The results show that the accuracy of the fading filter based on a variable fading factor is clearly improved, and the proposed improved ELM algorithm can provide position corrections during GPS outages more effectively than the other algorithms (ELM and the traditional radial basis function neural network).

A Novel Hybrid of a Fading Filter and an Extreme Learning Machine for GPS/INS during GPS Outages.

Nowadays, low-cost Global Positioning System (GPSyinertial Navigation System (INS) integration is widely used. Numerous techniques based on Kaiman Filter (KF) and Artificial Neural Networks (ANNs) are proposed to fuse the GPS and INS data. Kaiman filter is an optimal real-time data fusion method for GPS/INS integration while GPS signal is available. But when GPS outages, Kaiman filter cannot provide estimated position errors for INS. Without compensation, navigation accuracy will deteriorate badly along with time. ANNs are able to handle the problem of non-linearity and map input-output relationships without prior knowledge. In order to provide continuous, accurate and reliable navigation solution even during GPS outages, we proposed a novel model of combining UKF and BP neural network algorithms for INS errors compensation. UKF is an implementation of KF with great performance and used to ensure the high accuracy when GPS is available. BP is a most widely used method of training a multi-layer Feed-Forward Artificial Neural Networks (FFANNs). On the basis of enough training, it can predict INS position error when GPS signal is blocked. The model has been verified to have good performance for fusing GPS and INS data, even when GPS signal is unavailable.

A low-cost GPS/INS integration based on UKF and BP neural network

As the focus on highly intelligent robots continues, a problem that cannot be ignored has emerged: resource con-straints. Considering the game problem of resource limitation and the level of intelligence, we focus on lightweight intelligence. This work is a further refinement of our previous work, a heterogeneous lightweight intelligent multi-robot system. In-spired by the nature creatures “octopus” and “ants”. First, we propose a heterogeneous centralized-distributed architecture, which can make robots collaboration more flexible and non-redundant. Second, to reflect lightweight intelligence, we use the Raspberry Pi, a low computing and power consumption internet of things (IoT) device, as a processing platform and first propose a quantitative definition of the lightweight intelligent system. Then, combining the centralized-distributed architecture and the lightweight computing platform, we propose an adapted algorithm called OCTOANTS and apply it to the simultaneous localization and mapping (SLAM) field. The OCTOANTS architecture consists of one brain and eight tentacles, which can achieve complex things with proper collaboration between them. Finally, we use heterogeneous cameras and heterogeneous algorithms to form a lightweight intelligent collaborative system that can run in the real world. On the low-grade platform Raspberry Pi our heterogeneous tentacles frame rate can reach 41fps and 99.8fps respectively, power consumption is only 2W and 1.2W. At the same time, our heterogeneous system is on average 7.2% more accurate than the state-of-the-art homogeneous system and can be applied to a wider range of application scenarios, demonstrating the superiority and feasibility of our OCTOANTS.

OCTOANTS: A Heterogeneous Lightweight Intelligent Multi-Robot Collaboration System with Resource-constrained IoT Devices

Spacecraft telemetry time series data is the main basis for performance monitoring and real-time status analysis of spacecraft. The similarity measurement of time series is one of the important research areas. Aiming at the shortcomings of existing time series similarity measurement methods, this paper proposes a dynamic time warping algorithm based on adaptive segmentation(ASDTW). Aiming at the problem of excessive computational overhead of the Dynamic Time Warping algorithm(DTW), the algorithm divides the original sequence into several sequence segments, and defines the distance of the sequence segments according to their geometric characteristics. In the dynamic matching stage, sequence segments are used as the basic matching unit to solve the problem that the computation overhead caused by traditional point-by-point matching strategy is too high. Finally, this paper verifies the validity and feasibility of the ASDTW algorithm based on the actual telemetry data of a spacecraft. By comparing with the two baselines, the ASDTW algorithm greatly improves the efficiency of the algorithm under the premise of ensuring the measurement accuracy, and solves the problem of The problem of excessive computational time overhead of the DTW algorithm can more effectively support space mission planning.

Qian Zhang

Papers

A low-cost GPS/INS integration based on UKF and BP neural network

OCTOANTS: A Heterogeneous Lightweight Intelligent Multi-Robot Collaboration System with Resource-constrained IoT Devices

A Similarity Measurement Algorithm for Spacecraft Telemetry Time Series