Robust Mobile Robot Localization Using a Evolutionary Particle Filter

This paper proposes a novel particle filter, namely, the auxiliary iterated extended Kalman particle filter (AIEKPF). To generate the importance density, based on the auxiliary particle filtering (APF) technique the proposed filter uses the iterated extended Kalman filter (IEKF) to integrate the latest measurements into state transition density. This new filter can match the posterior density well, because of the robustness of the APF and the importance density generated by the IEKF. The performance of the presented particle filter is evaluated by two different estimation problems with the noise of Gaussian distribution and Gamma distribution, respectively. The experimental results illustrate that the AIEKPF is superior to the extended Kalman filter and some existing particle filters, such as the standard particle filter (PF), the extended Kalman particle filter, the unscented Kalman particle filter (UKPF) and the auxiliary extended Kalman particle filter, where the number of particles is relatively small, such as 200 and 1,000. However, with an increase of particles, the superiority of the proposed method may decline compared with the PF and APF as showed in the experiments. Also, the AIEKPF has less running time than the UKPF under the same conditions, and from the viewpoint of the average effective sample sizes, it is clear that the AIEKPF has the slightest degeneracy in all filters presented in the experiments.

The lack of a parameterized observation model in robot localization using occupancy grids requires the application of sampling-based methods, or particle filters. This work addresses the problem of optimal Bayesian filtering for dynamic systems with observation models that cannot be approximated properly as any parameterized distribution, which includes localization and SLAM with occupancy grids. By integrating ideas from previous works on adaptive sample size, auxiliary particle filters, and rejection sampling, we derive a new particle filter algorithm that enables the usage of the optimal proposal distribution to estimate the true posterior density of a non-parametric dynamic system. Our solution avoids approximations adopted in previous approaches at the cost of a higher computational burden. We present simulations and experimental results for a real robot showing the suitability of the method for localization.

In the application of particle filter algorithm for target tracking in wireless sensor networks, an Auxiliary particle filter (APF) and Gaussian particle filter (GPF) are discussed to solve the particle degradation problem of Particle Filter (PF). By introducing an auxiliary variable and two rounds weighted processes, APF makes the particle weights stable and relaxes particle degradation. GPF uses a Gaussian density function to approximately estimate posterior probability distribution. It doesn’t require re-sampling, and there is no particle degradation. Therefore, three algorithm are applied to single-target tracking in wireless sensor networks. Finally, the comparison of three algorithm’s performance in target tracking is presented and the simulation results are also given. From these results we can see that APF has better accuracy than PF algorithm and GPF has better accuracy and real-time performance than PF algorithm.

In this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the APF by decreasing its update frequency and the number of particles used in this research. In the lower filter (Kalman filter), zero velocity update and non-holonomic constraints are used to correct the error of the inertial navigation-derived solutions. The innovation of the design lies in the combination of upper filter (particle filter) map-matching and map-aiding methods to further constrain the navigation solutions. This proposed navigation method simplifies indoor positioning and makes it accessible to individual and group users, while guaranteeing the system’s accuracy. The availability and accuracy of the proposed algorithm are tested and validated through experiments in various practical scenarios.

Electronic interference poses a significant challenge to Unmanned Aerial Vehicle (UAV) tracking systems, compromising navigation accuracy and operational safety in critical applications such as surveillance, disaster response, and infrastructure inspection. This study introduces a novel application of the Auxiliary Particle Filter (APF) for robust UAV tracking under interference conditions, focusing on fixed-reference scenarios. The APF incorporates adaptive proposal distributions and robust weight updates to effectively mitigate interference-induced measurement errors. Through comprehensive simulation that evaluates performance under varying interference and sensor degradation scenarios, the APF demonstrates superior accuracy, achieving a mean Root Mean Square Error (RMSE) of 4.82 meters with low variability (m). This significantly outperforms traditional filters, including the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). Notably, the APF maintains stable performance even under severe interference conditions, where conventional approaches exhibit substantial degradation. Statistical validation confirms these improvements across all test scenarios (p < 0.001). The detailed implementation guidelines provided in this study enable adoption across diverse operational contexts. These findings establish the APF as a robust and reliable solution for interference-prone environments and lay the groundwork for its adaptation to more dynamic tracking scenarios in autonomous UAV operations.

This paper provides a solution for the multiple changepoint detection problems in financial time series prediction without knowing the number and location of changepoints. The proposed approach is a Sequential Monte Carlo (SMC) method for estimating GARCH based volatility models which are subject to an unknown number of changepoints. Recent Auxiliary Particle Filtering (APF) techniques are used to calculate the posterior densities and forecasts in real-time. This approach also automatically deals with the common path dependence problem of these type volatility models. We studied on simulated volatility data using GARCH model and have shown that the proposed approach works well with the generated data. For the non-linearity multiple changepoint detection problem, APF is investigated over the simulated volatility to model the switching regimes. In this study, a full structural changepoint specification is defined in which all parameters of the conditional variance of GARCH are subject to change respectively.

A note on auxiliary particle filters

This paper proposes two new particle filters, namely, the auxiliary extended Kalman particle filter (AEKPF) and the auxiliary unscented Kalman particle filter (AUKPF). The theory governing the newly proposed filtering techniques is developed and the algorithms are described and contrasted. Next, a series of tests is presented in which the new filters are compared against the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and several existing particle filters. The test results are from simulations with synthetic mathematical models that incorporate elements that are nonlinear, non-stationary, and stochastic. Performance results are presented for various degrees of model nonlinearity including first, second, and third order systems. Furthermore, experimental results are also reported comparing the filters performances with different signal to noise ratios and noise models, including Gaussian, Cauchy, and Gamma distributions. Various metrics are used to compare the filters performances and to make conclusions about future work. It is shown to be advantageous to use certain particle filters depending on the noise distribution of the system of interest. In particular, the AUKPF and the AEKPF outperform existing particle filters in many cases.

Varying rotate speed can cause changes in a measured gearbox vibration signal. There is a need to develop a technique to provide accurate state indicator of gearbox under fluctuating rotate speed conditions. This paper presents an approach for gearbox fault detection under varying rotate speed condition based on auxiliary particle filter. Firstly, the model of vibration part which sensitive to the alternating rotate speed condition was established based on the relation of cosine signal three points sampling values. Then this part vibration signal was estimated based on auxiliary particle filter. Based on these the residual signal was obtained which lower sensitiveness to the alternating rotate rate condition. Thus the gearbox fault was detected by the residual signal statistic quantity kurtosis and amplitude of Fourier transform. Finally, the different work condition vibration signals of the laboratory gearbox under varying rotate speed condition were detected and signal processing was studied with those signals as examples. The results show that the proposed method is feasible and effective.

Tracking a maneuvering target weakens the performance of predictive-model-based Bayesian state estimators (Kalman Filter). Therefore, the particle is used to track maneuverable targets instead of Kalman filter and its extensions. The particle filter proved more efficiency compared to Kalman filter and its extensions, e.g. Extended Kalman Filter (EKF) and Interacting Multiple Model (IMM). Unfortunately, due to the highly uncertainty and incompleteness of the information in a highly-maneuverable target-tracking problem, the advantage of the particle filter is weakened. Both auxiliary and smoothing particle filter were proposed to overcome this problem. In this paper, we compare the performance of both auxiliary and smoothing particle filter in tracking a highly maneuverable target. We applied both algorithms to track a maneuverable target in a multiple-sensors network. Monte Carlo simulation showed that the smoothing particle filter has a better performance when compared to auxiliary particle filter in tracking a maneuvering target.

The particle filter has attracted considerable attention in visual tracking due to its relaxation of the linear and Gaussian restrictions in the state space model. It is thus more flexible than the Kalman filter. However, the conventional particle filter uses system transition as the proposal distribution, leading to poor sampling efficiency and poor performance in visual tracking. It is not a trivial task to design satisfactory proposal distributions for the particle filter. In this paper, we introduce an improved particle filtering framework into visual tracking, which combines the unscented Kalman filter and the auxiliary particle filter. The efficient unscented auxiliary particle filter (UAPF) uses the unscented transformation to predict one-step ahead likelihood and produces more reasonable proposal distributions, thus reducing the number of particles required and substantially improving the tracking performance. Experiments on real video sequences demonstrate that the UAPF is computationally efficient and outperforms the conventional particle filter and the auxiliary particle filter.KeywordsParticle FilterImportance SamplingObservation ModelVisual TrackingUnscented Kalman FilterThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Sampling density design for particle filters

In this work, we propose a new ground moving target indicator (GMTI) radar based ground vehicle tracking method which exploits domain knowledge. Multiple state models are considered and a Monte-Carlo sampling based algorithm is preferred due to the manoeuvring of the ground vehicle and the non-linearity of the GMTI measurement model. Unlike the commonly used algorithms such as the interacting multiple model particle filter (IMMPF) and bootstrap multiple model particle filter (BS-MMPF), we propose a new algorithm integrating the more efficient auxiliary particle filter (APF) into a Bayesian framework. Moreover, since the movement of the ground vehicle is likely to be constrained by the road, this information is taken as the domain knowledge and applied together with the tracking algorithm for improving the tracking performance. Simulations are presented to show the advantages of both the new algorithm and incorporation of the road information by evaluating the root mean square error (RMSE).

International audience

Particle flow filters have been recently developed as an alternative approach for nonlinear filtering. The particles approximating the prior are migrated using differential equations to be distributed according to the posterior. Computationally tractable exact solutions only exist for linear Gaussian models. For other scenarios, approximations are required and it is not fully understood how these approximations impact the movement of the particles and the subsequent propagation of error in the filter. An alternative approach is to use the particle flow methods to perform the importance sampling step within a particle filtering framework. Existing methods along these lines involve either intensive calculation or the construction of a transport map, which can be challenging. In this paper, we propose to use existing particle flow methods in an auxiliary particle filter. The flows are used to sample auxiliary variables; and these allow us to identify importance sampling distributions that are well-matched to the posteriors. Simulations results indicate that the auxiliary particle filters we develop have accuracy and computational complexity similar to that of the underlying particle flow filters.