Extended Kalman filtering and Interacting Multiple Model for tracking maneuvering targets in sensor netwotrks (original) (raw)
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Novel Nonlinear Filtering & Prediction Method for Maneuvering Target Tracking
IEEE Transactions on Aerospace and Electronic Systems, 2009
A new nonlinear filtering and prediction (NFP) algorithm with input estimation is proposed for maneuvering target tracking. In the proposed method, the acceleration level is determined by a decision process, where a least squares (LS) estimator plays a major role in detecting target maneuvering within a sliding window. We first illustrate that the optimal solution to minimize the mean squared error (MSE) must consider a trade-off between the bias and error variance. For the application of target tracking, we then derive the MSE of target positions in a closed form by using orthogonal space decompositions. Then we discuss the NFP estimator, and evaluate how well the approach potentially works in the case of a set of given system parameters. Comparing with the traditional unbiased minimum variance filter (UMVF), Kalman filter, and interactive multiple model (IMM) algorithms, numerical results show that the newly proposed NFP method performs comparable or better in all scenarios with significantly less computational requirements.
Novel Approach for Nonlinear Maneuvering Target Tracking Based on Input Estimation
Applied Mechanics and Materials, 2011
Trajectories of aerial and marine vehicles are typically made of a succession of smooth trajectories, linked by abrupt changes, i.e., maneuvers. Notably, modern highly maneuvering targets are capable of very brutal changes in the heading with accelerations up to 15 g. As a result, we model the target behavior using piecewise deterministic Markov models, driven by parameters that jump at unknown times. Over the past years, real-time (or incremental) optimization based smoothing methods have become a popular alternative to nonlinear filters, such as the Extended Kalman Filter (EKF), owing to the successive relinearizations that mitigate the linearization errors that inherently affect the EKF estimates. In the present paper, we propose to combine such methods for tracking the target during non-jumping phases with a probabilistic approach to detect jumps. Our algorithm is shown to compare favorably to the state of the art Interacting Multiple Model (IMM) algorithm, especially in terms of target's velocity estimation, on a set of meaningful and challenging trajectories.
Bearing-only 2D maneuvering target tracking using smart interacting multiple model filter
Digital Signal Processing, 2022
In this paper, along with reviewing and analyzing the maneuvering target tracking model, the multiplemodel Interacting Multiple Model algorithm is used to solve the maneuvering target tracking problem in the presence of measurement noise. In addition, for reliable estimation another method is proposed, which uses higher-order Markov models to describe the system behavior precisely. It means that the previous two models are used to predict the next model of target in order to present a more better algorithm than the first-order IMM algorithm. In this approach, two models are employed. For each model Extended Kalman Filter is used to randomly estimate states of the target. The final estimation of the maneuvering target consists of these two models. Final target estimation is obtained from a weighted sum of all state estimates. In addition, target tracking is presented with two modes for noise measurement: one is an adaptive method and the other is an assignment of an integer amount considering problem circumstances. In the end, the results are compared.
IEEE Transactions on Fuzzy Systems, 2019
Tracking a maneuvering target is an important technology in real life. However, due to complex environment and diversity of sensors, sensors' errors need to be optimized with respect to various motion states during the tracing process. In this paper, we first propose how to unify the coordinate system and data preprocessing in case of tracking using multiple-sensors. We then combine fuzzy sets with a novel trace optimization method based on extended Kalman filter with nested probabilisticnumerical linguistic information. We present a case study of trace optimization of an unknown maneuvering target in Sichuan province in China. We solve the case by using both the proposed method and the traditional extended Kalman filter and offer comparative analysis to validate the proposed approach.
Second-order EKF and Unscented Kalman Filter Fusion for Tracking Maneuvering Targets
2007 IEEE International Conference on Information Reuse and Integration, 2007
When dealing with target tracking problem for maneuvering targets, it may be the case that a first order extended Kalman filter can not track the target and diverges due to neglecting the higher order terms of Taylor series. This paper studies two other filters which are more appropriate for maneuvering targets (with nonlinear state space equations). These two filters are entitled as second-order extended Kalman filter (SOEKF) and unscented Kalman filter (UKF). SOEKF uses Hessian matrix (second term of Taylor series) which may help solving the divergence problem. UKF is also useful as it works with the main nonlinear formula without the need to use any approximation. Both of the state space equations (process equation and measurement equation) is assumed to be nonlinear. In order to enhance the accuracy of tracking process sensor fusion approach is also applied for both of the filters. The number of sensors is assumed to be two. A comparison analysis is made between the two filters alone (without fusion approach) and also when sensor fusion is applied.
IMM-UKF algorithm and IMM-EKF algorithm for tracking highly maneuverable target: a comparison
2005
This paper aims to contribute in solving the problem of model-based body motion estimation by using data coming from visual sensors. We consider the case of state estimation in jump Markov nonlinear systems. The Interacting Multiple Model (IMM) algorithm is specially designed to track accurately targets whose state and/or measurement (assumed to be linear) models changes during motion transition. However, when these models are nonlinear, the IMM algorithm must be modified in order to guarantee an accurate track. In this paper we propose to compare the results given by an IMM algorithm Extended Kalman filter based (IMM-EKF) versus those given by an IMM algorithm Unscented Kalman filter based (IMM-UKF) in tracking target assumed to be highly maneuverable.
Performance comparison of EKF and particle filtering methods for maneuvering targets
Digital Signal Processing, 2007
Online tracking of maneuvering targets is a highly nonlinear and challenging problem that involves, at every time instant, the estimation not only of the unknown state in the dynamic model describing the evolution of the target, but also the underlying model accounting for the regime of movement. In this paper we review and compare several sequential estimation procedures, that use appropriate strategies for coping with various models that account for the different modes of operation. We focus on the application of the recently proposed cost-reference particle filtering (CRPF) methodology, which aims at the estimation of the system state without using probability distributions. The resulting method has a more robust performance when compared to standard particle filtering (SPF) algorithms or the interactive multiple model (IMM) algorithm based on the use of the well known extended Kalman filter (EKF). Advantages and disadvantages of the considered algorithms are illustrated and discussed through computer simulations.
Extended target tracking using an IMM based Rao-Blackwellised unscented Kalman filter
2008 9th International Conference on Signal Processing, 2008
An extended target tracking problem for high resolution sensors is considered. An ellipsoidal model is proposed to exploit sensor measurement of target extent, which can provide extra information to enhance tracking accuracy, data association performance, and target identification. Due to the presence of high nonlinearity of the model, a Rao-Blackwellised unscented Kalman filter (RBUKF) is adopted in this paper. In contrast to the most commonly used extended Kalman filter (EKF), the RBUKF provides more accurate and reliable estimation performance, without increasing any computational complexity. An interacting multiple model (IMM) technique is combined with the RBUKF method to adapt the target maneuver and motion mode switching problem which is vital for nonlinear filtering. The developed IMM-RBUKF algorithm on extended target tracking problem is validated and evaluated by computer simulations.
Performance comparison of the two-stage kalman filtering techniques for target tracking
2010
The two-stage filtering methods, such as the wellknown augmented state Kalman estimator (AUSKE) and the optimal two-stage Kalman estimator (OTSKE), suffer from some major drawbacks. These drawbacks stem from assuming constant acceleration and assuming the input term is observable from the measurement equation. In addition, these methodologies are usually computationally expensive. The innovative optimal partitioned state Kalman estimator (OPSKE) developed to overcome these drawbacks of traditional methodologies. In this paper, we compare performance of the OPSKE with the OTSKE and the AUSKE in the maneuvering target tracking (MTT) problem. We provide some analytic results to demonstrate the computational advantages of the OPSKE.
An Efficient Approach for Target Tracking and Error Minimization by Kalman Filtering Technique
2007 International Conference on Electrical Engineering, 2007
Target tracking and size estimation have always been The only limitation with most of the proposed solutions for an active field of research because of its widely spread target tracking was their over-emphasis on over all dynamic applications in navigational, air defense and missile interception modeling throughout the period of scans irrespective of systems, etc. In this paper error-minimization and to increase hit dynamic changes in target's behaviors. However if target's probability has been focused keeping in view the limitations -on, l imposed by practical constraints. An efficient practical approach positi very aclrtintetcan be asured has been modeled and tested practically in this research work, apredcted fo ver sretiontals ofiteim and pose which tackles a maneuvering target and its dynamics in real time. approach used as the prediction and filtering algorithm, many