Tao Pang

Application of Neural Network Aided Particle Filter in GPS Receiver Autonomous Integrity Monitoring

According to the measurement noise feature of GPS receiver and the sample impoverishment problem with the basic particle filter, an improved particle filter based on neural network algorithm is proposed. Using back-propagation (BP) neural network to adjust the particles with too high and too low weight, firstly, the larger weight particles are respectively splitted into two smaller weight particles. Then, abandoning the particles with very small weight, and adjust the particles with smaller weight by using the neural network. Therefore, the diversity of the sample particles is improved. The improved particle filter algorithm is combined with the likelihood ratio method for GPS receiver autonomous integrity monitoring (RAIM). By using the likelihood ratio as a consistency test statistic to achieve the fault detection, satellite fault detection is undertaken by checking the cumulative likelihood ratio of system state with detection threshold. By taking advantage of the relationship in statistical values between the total cumulative likelihood ratio and partial cumulative likelihood ratio, the number of fault satellite can be determined. Based on the real GPS raw data, the simulation results demonstrate that the improved particle filter under the conditions of non-Gaussian measurement noise can effectively detect and isolate fault satellite, and improve the performance of fault detection.

GPS Receiver Autonomous Integrity Monitoring Algorithm Based on Improved Particle Filter

Reliability of a navigation system is one of great importance for navigation purposes. Therefore, an integrity monitoring system is an inseparable part of aviation navigation system. Failures or faults due to malfunctions in the systems should be detected and repaired to keep the integrity of the system intact. According to the characteristic of GPS (Global Positioning System) receiver noise distribution and particle degeneracy and sample impoverishment problem in particle filter, an improved particle filter algorithm based on genetic algorithm for detecting satellite failures is proposed. The combination of the re-sampling method based on genetic algorithm and basic particle filter is used for GPS receiver autonomous integrity monitoring (RAIM). Dealing with the low weight particles on the basis of the genetic operation, genetic algorithm is used to classify the particles. It brings the selection, crossover and mutation operation in genetic algorithm into the basis particle filter .The method for detecting satellite failures which affect only subsets of system measurement. In addition to a main particle filter, which processes all the measurements to give the optimal state estimate, a bank of auxiliary particle filters is also used, which process subsets of the measurements to provide the state estimates which serve as failure detection references. The consistency of test statistics for detection and isolation of satellite fault is established. The failure detection is undertaken by checking the system state logarithmic likelihood ratio (LLR). The RAIM algorithm combined the genetic particle filter and the likelihood method is illustrated in detail. Experimental results based on GPS real raw data demonstrate that the algorithm under the condition of non- Gaussian measurement noise can improve the accuracy of state estimation, effectively detect and isolate fault satellite, improve the performance of the fault detection. Experimental results demonstrate that the proposed approach is available and effective for GPS RAIM.

Research and analysis of GNSS performance test methods

GNSS performance test is necessary at various stages such as initial design, full operational capability and system modernization, and is also the important guarantee of GNSS continuous and reliable operation. At present, Chinas BeiDou navigation satellite system (BDS) is in the construction stage of developing from the regional navigation system to the global navigation system, which is quite vital to carry out the study on relevant performance test methods. Firstly, this paper systematically studies the GNSS performance test projects carried out in the different stages of development in view of GPS, Galileo and some augmentation systems including WAAS, GBAS, EGNOS. And the test purpose, test content, test method, the experimental reference and test results are analyzed in detailed. Then, based on the status of Chinas BeiDou navigation satellite system, the related suggestions for performance test methods of BDS are put forward. The study is of great reference value to establishing BDS performance test environment and developing test methods.

Research on GPS RAIM Algorithm Using PF Based on PSO

To solve the problem of basic particle filter (PF), a novel GPS receiver autonomous integrity monitoring (RAIM) method was proposed, which was based on an algorithm combining particle swarm optimization particle filter (PSO-PF) with likelihood ratio test. The test statistic of fault satellite detection was set up, and the probability distribution of the log-likelihood ratio test statistic was analyzed. The consistency test is undertaken by checking the cumulative log-likelihood ratio (LLR) of system states. The velocity and position of particles were updated by particle swarm optimization algorithm, which make the particles of PF approximate the true system state to improve the posterior probability density of system state. Collecting the raw GPS data, the proposed algorithm was verified. The simulation result demonstrates that the proposed algorithm can effectively detect and isolate fault satellite under conditions of non-Gaussian measurement noise.

Research on BDS/GPS Integrated Navigation Satellite Selection Algorithm Based on Particle Swarm Optimization

In the multi-constellation satellite navigation system, all the visible satellites are used for positioning, which will increase the computation amount of the receiver and affect the real-time positioning. How to quickly and effectively select visible satellites for positioning is a research topic. For this problem, a satellite selection algorithm based on Particle Swarm Optimization (PSO) is proposed. In this method, the visible satellite is numbered, random grouping, and each group as a particle; the velocity-displacement model in the PSO keeps the particles gradually close to the minimum value of the GDOP. Under a series of simulation experiments, the key parameters such as inertia weight factor, acceleration coefficient and maximum velocity of PSO are determined. Besides, local search based on chaos mechanism are introduced, which can avoid the results of PSO algorithm into local optimum. Finally, the performance of satellite selection with PSO is confirmed to be remarkable by the simulation experiments under different numbers of selected satellites. The results show that this method can quickly select satellites under BDS and GPS system, and the result meets receiver positioning accuracy.

Research on RAIM Algorithm Based on GPS/BDS Integrated Navigation

In multi-constellation integrated navigation system, the multiplication of visible satellites makes the receiver autonomous integrity algorithm (RAIM) applied in approach flight phase of civil aviation become possible. For the GPS/BDS integrated navigation RAIM algorithm applied to approach procedure with vertical guidance (APV) phase of flight, an algorithm based on BDS and GPS positioning solution weighted average solution is proposed. Firstly, the algorithm obtains the weighted average solution satisfying the vertical-guided approach by searching for the optimal weight ratio and uses it as the final solution. Then, the distance between the final solution and BDS, GPS positioning solution is used as the test statistic, and the detection threshold is calculated through the maximum allowable false alarm probability. In the end, the fault is detected by comparing the detection statistic and the detection threshold. By collecting the raw GPS/BDS data, the proposed algorithm was verified. The simulation results demonstrate that the proposed algorithm can effectively detect fault satellites.

BDS receiver baseband signal tracking processing algorithm

In order to optimize the baseband signal processing algorithm of BeiDou Navigation Satellite System (BDS) software receiver and improve the accuracy of pseudorange measurement and positioning, the baseband signal processing algorithm is analyzed. In this paper, the delay lock loop (DLL) is adapted for C/A code tracking, and the frequency locked loop (FLL) assisted Costas phase locking loop (PLL) is used for carrier tracking. Moreover, the different PLL discriminators are analyzed in detail. According to the simulation results of the discriminators, the two-quadrant arctangent discriminator is selected as the phase discriminator in the Costas PLL for BDS receiver. The simulation results showed that the improved baseband processing algorithm can perform reliable tracking for BDS receiver baseband signal. Our work will be helpful to better understand and design of BDS software receiver.

Wavelet analysis approach to fault detection for GPS integrity monitoring

Reliability of a navigation system is one of greatimportance for navigation purposes. Therefore, anintegrity monitoring system is an inseparable part of aviation navigation system. Failures or faults due to malfunctions in the systems should be detected and repaired to keep the integrity of the system intact. In this paper, we present a method based on wavelet analysis for detecting failures. The raw pseudorange measurements are transformed by wavelet transform. Then the data jumping can be detected through the different wavelet scales. Therefore, the satellite fault could be detected. Two kinds of GPS satellite fault detection methods are given in detail, and the advantages and disadvantages of them are compared. Based on the wavelet analysis, the performance of fault detection is evaluated using the real measured data. The effectiveness of the proposed approach is illustrated in a problem of GPS (Global Positioning System) autonomous integrity monitoring system.

Accuracy Improvement of GPS Positioning Based on GA-Aided Particle Filter

Aiming at the weight degeneracy phenomena in particle filter algorithm, a resampling method improving the diversity based on GA-aided particle filter was presented. Taking the advantage of genetic algorithm ( GA ) in selection ,crossover and inheritance to make up for the shortcoming of resampling. Genetic operation on particles in real number domain is adapted to reduce the complex of the genetic algorithm. And the evolutionary idea of genetic algorithm was combined with particle filter, by using selection, and mutation to improve the weight degeneracy and diversity of particle filter. This GA-aided particle filter was applied in the established GPS system nonlinear dynamic state space model. The experimental results based on the collected real GPS data is compared with the tradition particle filter, and compared with the effective number of particles and particle distribution. The experimental results indicated that the GA-aided particle filter can increase the number of particle, and effectively solve the particle degradation phenomena, the estimation accuracy of GA-aided particle filter is better than that of particle filter (PF).

Accuracy Improvement of GPS Positioning Based on Genetic Particle Filter

Aiming at the weight degeneracy phenomena in particle filter algorithm, a resampling method improving the diversity based on genetic particle filter was presented. Taking the advantage of genetic algorithm (GA) in selection, crossover and inheritance to make up for the shortcoming of resampling. Genetic operation on particles in real number domain is adapted to reduce the complex of the genetic algorithm. And the evolutionary idea of genetic algorithm was combined with particle filter, by using selection, and mutation to improve the weight degeneracy and diversity of particle filter. This genetic particle filter was applied in the established GPS system nonlinear dynamic state space model. The experimental results based on the collected real GPS data is compared with the tradition particle filter, and compared with the effective number of particles and particle distribution. The experimental results indicated that the genetic particle filter can increase the number of particle, and effectively solve the particle degradation phenomena, the estimation accuracy of genetic particle filter is better than that of particle filter (PF).