Rao-Blackwellized Unscented Particle Filter for a Handheld Unexploded Ordnance Geolocation System using IMU/GPS

Lee, Jong Ki; Jekeli, Christopher

doi:10.1017/s0373463310000548

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…9) Many modified particle filters have been proposed to solve these issues, and they can be classified into two categories. The filters contained in the first category are modified by choosing the optimal importance density function, 10,11) such as the extended Kalman particle filter, unscented particle filter and cubature particle filter. Although these filters could effectively alleviate the degeneracy phenomenon, they also increase the computational burden.…”

Section: Introductionmentioning

confidence: 99%

Genetic Resampling Particle Filter with Randomly Delayed Measurements and Its Application to Line-of-Sight Rate Estimation

Zhang

Wang

Qin

et al. 2019

Trans. Japan Soc. Aero. S Sci.

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To deal with randomly delayed measurements and glint noise, a novel recursive filter referred as a randomly delayed genetic resampling particle filter (RD-GRPF) is proposed in this paper. By making use of Bernoulli random variables, the measurement model is modified to describe the random delay. Then, the weight update equation is reformulated based on this model. To avoid the particle degeneration and sample impoverishment that always arise in the application of a particle filter, the genetic resampling method is utilized to resample the particles. Then, the RD-GRPF is obtained. Therefore, the filter proposed not only copes with randomly delayed measurements, it also keeps the advantage of the standard particle filter, which ensures good performance in the case of non-Gaussian noise. In addition, the RD-GRPF proposed is applied to line-of-sight (LOS) rate estimation, the model for which is also presented in this paper. A simulation was conducted and the results demonstrate the superiority of the RD-GRPF.

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Section: Introductionmentioning

confidence: 99%

Genetic Resampling Particle Filter with Randomly Delayed Measurements and Its Application to Line-of-Sight Rate Estimation

Zhang

Wang

Qin

et al. 2019

Trans. Japan Soc. Aero. S Sci.

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“…Thus this paper considers the smoothing estimation algorithm for post processing. The Rauch-Tung-Striebel (R-T-S) smoother is a fixed interval smoother, consisting of a standard Kalman filter and a set of backward processing equations (Rauch et al, 1965; Lee and Jekeli, 2011). The smoothed state estimates are derived through propagating the scaled statistics of the forward filter backward in time (Durbin and Koopman, 2012).…”

Section: Introductionmentioning

confidence: 99%

Airborne Earth Observation Positioning and Orientation by SINS/GPS Integration Using CD R-T-S Smoothing

Gong¹,

Qin²

2013

J. Navigation

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This paper addresses the issue of state estimation in the integration of a Strapdown Inertial Navigation System (SINS) and Global Positioning System (GPS), which is used for airborne earth observation positioning and orientation. For a nonlinear system, especially with large initial attitude errors, the performance of linear estimation approaches will degrade. In this paper a nonlinear error model based on angle errors is built, and a nonlinear estimation algorithm called the Central Difference Rauch-Tung-Striebel (R-T-S) Smoother (CDRTSS) is utilized in SINS/GPS integration post-processing. In this algorithm, the measurements are first processed by the forward Central Difference Kalman filter (CDKF) and then a separate backward smoothing pass is used to obtain the improved solution. The performance of this algorithm is compared with a similar smoother based on an extended Kalman filter known as ERTSS through Monte Carlo simulations and flight tests with a loaded SINS/GPS integrated system. Furthermore, a digital camera was used to verify the precision of practical applications in a check field with numerous reference points. All these validity checks demonstrate that CDRTSS is a better method and the work of this paper will offer a new approach for SINS/GPS integration for Synthetic Aperture Radar (SAR) and other airborne earth observation tasks.

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“…GPS is also subject to intentional (jamming) and unintentional RF interference. To overcome these limitations, many navigation technologies based mainly on the concept of multi-sensor integration have been proposed and tested (Mirabadi et al, 2003; Sharaf and Noureldin, 2007; Grejner-Brzezinska et al, 2008; Lee and Jekeli, 2011).…”

Section: Introductionmentioning

confidence: 99%

Network-based Collaborative Navigation in GPS-Denied Environment

2012

Self Cite

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Global Positioning System (GPS) has been used as a primary source of navigation in land and airborne applications. However, challenging environments cause GPS signal blockage or degradation, and prevent reliable and seamless positioning and navigation using GPS only. Therefore, multi-sensor based navigation systems have been developed to overcome the limitations of GPS by adding some forms of augmentation. The next step towards assured robust navigation is to combine information from multiple ground-users, to further improve the chance of obtaining reliable navigation and positioning information. Collaborative (or cooperative) navigation can improve the individual navigation solution in terms of both accuracy and coverage, and may reduce the system's design cost, as equipping all users with high performance multi-sensor positioning systems is not cost effective. Generally, 'Collaborative Navigation' uses inter-nodal range measurements between platforms (users) to strengthen the navigation solution. In the collaborative navigation approach, the inter-nodal distance vectors from the known or more accurate positions to the unknown locations can be established. Therefore, the collaborative navigation technique has the advantage in that errors at the user's position can be compensated by other known (or more accurate) positions of other platforms, and may result in the improvement of the navigation solutions for the entire group of users. In this paper, three statistical network-based collaborative navigation algorithms, the Restricted Least-Squares Solution (RLESS), the Stochastic Constrained Least-Squares Solution (SCLESS) and the Best Linear Minimum Partial Bias Estimation (BLIMPBE) are proposed and compared to the Kalman filter. The proposed statistical collaborative navigation algorithms for network solution show better performance than the Kalman filter.

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Rao-Blackwellized Unscented Particle Filter for a Handheld Unexploded Ordnance Geolocation System using IMU/GPS

Cited by 5 publications

References 18 publications

Genetic Resampling Particle Filter with Randomly Delayed Measurements and Its Application to Line-of-Sight Rate Estimation

Genetic Resampling Particle Filter with Randomly Delayed Measurements and Its Application to Line-of-Sight Rate Estimation

Airborne Earth Observation Positioning and Orientation by SINS/GPS Integration Using CD R-T-S Smoothing

Network-based Collaborative Navigation in GPS-Denied Environment

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