A Bayesian framework with an auxiliary particle filter for GMTI-based ground vehicle tracking aided by domain knowledge

A novel T-S fuzzy particle filtering algorithm based on fuzzy C-regression clustering

Pedestrian tracking with a single motion model and a single cue cannot be robust against combination of changes of target motion and appearance, caused by either static or dynamic changes in surrounding environments. Different from previous work, this paper proposes to refer to both multiple motion models and cues to improve accuracy of single pedestrian tracking. By integrating the compensated motion model into model filtering of interacting multiple model particle filter (IMMPF) and updating mode probabilities using the state with the maximum color based likelihood weight, the interacting stage of IMMPF generates more accurate approximation of the mixed a priori probability distribution of the target state. Within the search window centering at the predicted state of the constant velocity (CV) model of IMMPF, feature matching of scale- and rotation-invariant feature descriptors helps locate the target. Experimental results show that the proposed scheme is robust against the change of target motion, occlusion, rotation, scaling, and illumination variations. The average RMSE and overlap ratio of the proposed scheme significantly outperforms color based IMMPF using compensated motion model and fast L1-tracker.

In this paper, we present an easy-hardware-implementation multiple model particle filter (MMPF) for maneuvering target tracking. In the proposed filter, the sampling importance resampling (SIR) filter typically used for nonlinear and/or non-Gaussian application is extended to incorporating multiple models that are composed of a constant velocity (CV) model and a "current" statistical (CS) model, and the Independent Metropolis Hasting (IMH) sampler is utilized for the resampling unit in each model. Compared with the bootstrap MMPF, the proposed MMPF requires no knowledge of models and model transition probabilities for different maneuvering motions, and keeps a constant number of particles per model at all times. This allows a regular pipelined hardware structure and can be implemented in hardware easily. Furthermore, using the IMH sampler for the resampling unit avoids the bottleneck introduced by the traditional systematic resampler and reduces the latency of the whole implementation. Simulation results indicate that the proposed filter has approximately equal tracking performance with the bootstrap MMPF. Hardware architecture of the IMH sampler and its corresponding sample unit are presented, and a parallel architecture consisting of CV model processing element (PE), CS model PE and a central unit (CU) is described. The proposed architecture is evaluated on a Xilinx Virtex-II Pro FPGA platform for a maneuvering target tracking application and the results show many advantages of the proposed MMPF over existing approaches in terms of efficiency, lower latency, and easy hardware implementation.

This paper presents two methods for maneuvering radar target tracking, one is called variable structure multiple model particle filter (VSMMPF), which combines the variable structure multiple model (VSMM) filter with a Sampling-Importance Resampling (SIR) particle filter (PF); The other one is called modified adaptive gird multiple model (AGMM), which is suitable when model parameter scope is unknown beforehand. PF is used to deal with problems with arbitrary nonlinearities and non-Gaussian noise. Thus the combination of VSMM and PF is able to deal with problems of nonlinearities, non-Gaussian and maneuvering. As one of the VSMM algorithms, Grid adaptation (GA) is unique one which can produce new model-set that are not predefined and model parameter can be any value within continuous model space. However, it only used to tracking maneuvering target in 2D coordinate turn system. Our modified AGMM could track generic planar maneuvering target. Simulation results indicate that performance of the algorithm VSMMPF outperforms interacting multiple model (IMM) PF and the AGMM PF which combines AGMM with PF runs well especially when model scope is unknown in advance.

Particle filters can provide better results than Kalman filter based algorithms in the problems where the nonlinear elements are dominant. In this study the problem of tracking an airborne platform using radar measurements, which has nonlinearities both in measurement and system models is considered. Different models are used for different target maneuvers; extended Kalman filter (EKF), interacting multiple model (IMM) structure and multiple model particle filter are compared on a sample scenario.

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 addresses the problem of data fusion for joint multiple manoeuvring target tracking and classification where centralised versus decentralised architectures are contrasted. The proposal advocates the use of a hybrid approach combining a particle filter (PF) like method to deal with system nonlinearities, Fitzgerald's cheap joint probabilistic data association filter (CJPDAF) for the purpose of data association/target estimation problems and multiple model filter (MMF) to handle the issue of target manoeuvrability, yielding CJPDA-MMPF algorithm. The feasibility and the performances of the proposal have been demonstrated using complex scenarios involving a set of Monte Carlo simulations dealing with four crossing targets with different operations modes. Constant velocity model and constant turn rate model were employed to generate the manoeuvres. The results of the simulations demonstrate the feasibility and the superiority of the centralised fusion architecture approach when combined with multiple model particle filter and cheap joint data association filter.

A novel particle filter for multiple target tracking with track-before-detect measurement models is proposed. Particle filters efficiently perform target tracking under nonlinear or non-Gaussian models. However, their application to multiple target tracking suffers from the curse of dimensionality. We introduce an efficient particle filter for multiple target tracking which deals with the curse of dimensionality better than previously developed methods. The proposed algorithm is tested and compared to other multiple target tracking particle filters.

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.

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.

This work considers the estimation of the seismic events using a Bayesian filtering method, a multiple model particle filtering (MMPF) framework, in particular, from the noisy seismic records. According to the nonlinearity of the seismic signals, the nonlinear dynamical system can be used for the modelling of the signal. In geological studies, it is very important to accurately estimate the events from the recorded seismic signals, under noisy environment in particular since the signal is usually acquired usually under noisy conditions resulting in the difficulty in information extraction. This work proposes the MMPF, an effective filter in a sequential Monte Carlo method, in cooperated with the seismic wavelet model for the estimation of the seismic events under the noisy environments. To evaluate the performance of the method, a set of synthetic noisy signals with different numbers of events, noise levels, was created and used in the simulations. Results illustrates that the proposed MMPF can provide the excellent seismic event estimates under noisy environment.

Accurate tracking of elite athletes for performance monitoring allows sports scientists to optimize training to gain a competitive edge. An important challenge in this application is that the maneuverability of the athletes is high and the traditional Kalman filter (KF) will not provide satisfactory tracking accuracy. Further, high update rates, of the order of tens of updates per second for each player, are often required and hence, the tracking algorithm considered should be computationally efficient. In this paper we propose a computationally efficient multiple model particle filter (MM-PF) algorithm for tracking maneuvering objects. It uses a Gaussian proposal density based on the unscented KF and a deterministic sampling technique and provides tracking accuracy similar to that of the augmented MM-PF, but with much lower computational cost. The performance of the proposed algorithm was verified using simulations and data collected in field trials. The trials were conducted with the Australian Institute of Sport using a localization system we have designed.

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.

As evidenced by the recent works of many researchers, the particle-filtering (PF) framework has revolutionized probabilistic visual target tracking. In this paper, we present a new particle filter tracking algorithm that incorporates the multiple-model (MM) paradigm and the technique of state partitioning with parallel filters. Traditionally, most tracking algorithms assume that a target operates according to a single dynamic model. However, the single-model assumption can cause the tracker to become unstable, particularly when the target has complex motions and when the camera has abrupt ego-motions. In the new tracking algorithm, a target was assumed to operate according to one dynamic model from a finite set of models. The switching process from one model to another was governed by a <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">jump</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Markov</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">process</i> . Based on the improved MM particle filter framework, we offer a new design strategy that adopts the state-partitioning technique and a bank of parallel extended Kalman filters to construct a better proposal distribution to achieve further estimation accuracy. We have conducted extensive testing for the proposed tracking algorithm, and key outcomes were given in the results section. It has been demonstrated by the experiments that this approach gave significantly improved estimations, enabling the new particle filter to effectively track human subjects.

In this paper, we address the problem of detecting the appearance time of a target and tracking its state with a particle filter in the Track-Before-Detect context. We show that it is possible to model the problem as a quickest detection change problem in a Bayesian framework. In this case, the posterior density of the target time appearance is a mixture where each component represents the hypothesis that the target arrived at a given time. As the posterior density is intractable in practice, we propose to approximate each component of the mixture by a particle filter, and we show that the weights of the mixture can be computed recursively thanks to quantities provided by the different particle filters. The overall filter yields good performance.