Improved Auxiliary Particle Filter for SINS/SAR Navigation

In order to obtain the relatively appropriate importance density function and alleviate the problem of particle degradation, a new improved auxiliary particle filter algorithm is proposed. After calculating the auxiliary variable, the adaptive regulator is employed to obtain the state estimation. So, the latest measurement information is efficiently utilized to establish a better importance density function in the importance sampling process. Then, the process of particle weights’ adaptive adjustment and random-weighted calculation can keep the diversity of particles and improve the filter precision; thus, it can better solve the filter problem of nonlinear system model error and noise interference. The simulation and analysis result show that the proposed algorithm can optimize the filter performance and improve the calculation precision in the positioning of the SINS/SAR integrated navigation system, compared with the other two existing filters.

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Robust Interacting Multiple Model Unscented Particle Filter for Navigation

Mathematical Problems in Engineering ◽

10.1155/2020/8871358 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Li Xue ◽

Yulan Han ◽

Chunning Na

Keyword(s):

Particle Filter ◽

Navigation System ◽

Measurement Information ◽

Interacting Multiple Model ◽

Integrated Navigation ◽

Multiple Model ◽

Integrated Navigation System ◽

Filter Performance ◽

Unscented Particle Filter ◽

Particle Filter Algorithm

In order to solve the problems of particle degradation and difficulty in selecting importance density function in particle filter algorithm, a robust interacting multiple model unscented particle filter algorithm is presented, which is based on the advantages of interacting multiple model and particle filter algorithms. This algorithm can use the unscented transformation to get the particles that contain the latest measurement information of each model and calculate the robust equivalent weight function. This robust factor is designed to adjust the estimation and variance, and the important distribution function adaptively obtained is closer to the true distribution. Then, the particles weights can be flexibly adjusted in real time by using Euclidean distance to improve the computational efficiency during the resampling process. In addition, this filter process can comprehensively describe the uncertainty of the statistics characteristic of observation noise between different models. The diversity of available particles is increased, and the filter precision is improved. The proposed algorithm is applied to the SINS/GPS integrated navigation system, and the simulation analysis results demonstrate that the algorithm can effectively improve the filter performance and the calculation precision in positioning of integrated navigation system; thus, it provides a new method for nonlinear model filter.

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Study on Maneuvering Target On-axis Tracking Algorithm of Modified Current Statistical Model

MATEC Web of Conferences ◽

10.1051/matecconf/201816002008 ◽

2018 ◽

Vol 160 ◽

pp. 02008

Author(s):

Xiong Zhenkai ◽

Li Fanying ◽

Zhang Lei

Keyword(s):

Kalman Filter ◽

Statistical Model ◽

Particle Filter ◽

Density Function ◽

Unscented Kalman Filter ◽

Tracking System ◽

Filter Performance ◽

Maneuvering Target ◽

Simulation Results ◽

Importance Density Function

Aiming at the model adaptability and the filter precision on the maneuvering target on-axis tracking, The paper put forward a filter algorithm based on modified current statistical model. The algorithm can enhance the model adaptability to the weak and non-maneuvering maneuvering target. The method uses Unscented Kalman Filter to obtain the importance density function of each particle, improves the Particle Filter estimation performance.By applying the proposed algorithm to the on-axis tracking system, the simulation results demonstrate that algorithm can effectively improve filter performance and tracking precision.

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Ant-Mutated Immune Particle Filter Design for Terrain Referenced Navigation with Interferometric Radar Altimeter

Remote Sensing ◽

10.3390/rs13112189 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2189

Author(s):

Suktae Kang ◽

Myeong-Jong Yu

Keyword(s):

Immune System ◽

Particle Filter ◽

Measurement Errors ◽

Filter Design ◽

Sar Interferometry ◽

Low Grade ◽

Radar Altimeter ◽

Terrain Referenced Navigation ◽

Auxiliary Particle ◽

Auxiliary Particle Filter

This study aims to design a robust particle filter using artificial intelligence algorithms to enhance estimation performance using a low-grade interferometric radar altimeter (IRA). Based on the synthetic aperture radar (SAR) interferometry technology, the IRA can extract three-dimensional ground coordinates with at least two antennas. However, some IRA uncertainties caused by geometric factors and IRA-inherent measurement errors have proven to be difficult to eliminate by signal processing. These uncertainties contaminate IRA outputs, crucially impacting the navigation performance of low-grade IRA sensors in particular. To deal with such uncertainties, an ant-mutated immune particle filter (AMIPF) is proposed. The proposed filter combines the ant colony optimization (ACO) algorithm with the immune auxiliary particle filter (IAPF) to bring individual mutation intensity. The immune system indicates the stochastic parameters of the ACO, which conducts the mutation process in one step for the purpose of computational efficiency. The ant mutation then moves particles into the most desirable position using parameters from the immune system to obtain optimal particle diversity. To verify the performance of the proposed filter, a terrain referenced navigation (TRN) simulation was conducted on an unmanned aerial vehicle (UAV). The Monte Carlo simulation results show that the proposed filter is not only more computationally efficient than the IAPF but also outperforms both the IAPF and the auxiliary particle filter (APF) in navigation performance and robustness.

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