DETC2005-85726 IMPROVED CURRENT STATISTIC MODEL AND ADAPTIVE FILTERING

ABSTRACT Current statistical model needs to pre-define the value of maximum accelerations of maneuvering targets. So it may be difficult to meet all maneuvering conditions. In this paper a novel adaptive algorithm for tracking maneuvering targets is proposed. The algorithm is implemented with fuzzy-controlled current statistic model adaptive filtering and unscented transformation. The Monte Carlo simulation results show that this method outperforms the conventional tracking algorithm based on current statistical model. Keywords: Current statistical model; Fuzzy logic; Unscented transformation INTRODUCTION The problem of tracking maneuvering targets has received a great of attention. The key to this problem lies in building the optimal target motion model. Various mathematical models of target motion have been developed over the past three decades, among which interacting multiple model (IMM) and current statistical model (CSM) are representativ

Algoritma Hibrid Extended Kalman Filter dan Inferensi Fuzzy untuk Penjejakan Target Bermanuver

Pada penelitian ini dikembangkan algoritma hibrid Extended Kalman Filter (EKF) dan Sistem Inferensi Fuzzy untuk mendapatkan hasil estimasi yang lebih akurat pada penjejakan target bermanuver. Logika Fuzzy telah digunakan untuk mengatur galat kovarian proses dan galat kovarian pengukuran dari proses EKF pada model sistem. Model state space yang digunakan untuk estimasi adalah model percepatan konstan, dan model pengukurannya adalah model radar. Hasil pengukuran sensor yang mengandung derau diestimasi menggunakan algoritma EKF. Kemudian galat kovarian yang dihasilkan dari proses EKF digunakan sebagai masukan pada Sistem Inferensi Fuzzy untuk koreksi berdasarkan ketidaksesuaian antara vektor inovasi dan kovarian inovasi. Hasil koreksi ini digunakan untuk mendapatkan gain Kalman yang optimal. Berdasarkan simulasi yang dilakukan menggunakan estimasi EKF dan Sistem Inferensi Fuzzy diperoleh peningkatan akurasi sebesar 59,97% dibandingkan dengan hasil pengukuran tanpa melakukan estimasi.In this paper the Extended Kalman Filter and the Fuzzy Inference System hybrid algorithm has developed to get more accurate estimation result for maneuvering target tracking. Fuzzy Logic has used to adjust the process covariance error and measurement covariance error of the Extended Kalman Filter process in the system model. The state space model used for estimation is a constant acceleration motion model, and the measurement model is a radar model. The measurement result of the sensor containing noise estimated using the Extended Kalman Filter (EKF) algorithm. Then, the covariance error resulting from the EKF process is used as input to the Fuzzy Inference System (FIS) for correction based on the mismatch between innovation vector and innovation covariance. The result of this correction used to obtain the optimal Kalman gain. The proposed system model leads to improved accuracy of 59.97% compared to measurement results without estimation in the simulation case.

Fuzzy interacting multiple model H∞ particle filter algorithm based on current statistical model

In this paper, fuzzy theory and interacting multiple model are introduced into H∞ filter-based particle filter to propose a new fuzzy interacting multiple model H∞ particle filter based on current statistical model. Each model uses H∞ particle filter algorithm for filtering, in which the current statistical model can describe the maneuver of target accurately and H∞ filter can deal with the nonlinear system effectively. Aiming at the problem of large amount of probability calculation in interacting multiple model by using combination calculation method, our approach calculates each model matching probability through the fuzzy theory, which can not only reduce the calculation amount, but also improve the state estimation accuracy to some extent. The simulation results show that the proposed algorithm can be more accurate and robust to track maneuvering target

Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov–Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture (GM) prior to a Gaussian/GM posterior (termed ‘Gaussian conjugacy’ in this paper), form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality (including target maneuver), intractable uncertainties (such as unknown inputs and/or non-Gaussian noises) and constraints (including circular quantities), etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity

Adaptive Localisation for Unmanned Surface Vehicles Using IMU-Interacting Multiple Model

Unscented Kalman Filter (UKF) remains to be a prevalent multi-sensor fusion method in many practices, including navigational tracking for Unmanned Surface Vehicles (USVs). This paper suggests that results from UKF fusion is unsatisfactory for USVs’ relatively smooth path due to UKF’s lack of versatility. Hence, it is proposed here that by replacing the UKF with Interacting Multiple Model (IMM), estimation results will better represent USV’s movement. Furthermore, this paper proposes slight modification to the IMM in order to heighten the algorithm’s confidence in switching modes. By exploiting angular velocity information from Inertial Measurement Unit (IMU), an independent mode probability can be obtained which is then injected into the IMM. Computer simulations based on maritime operations were done to show that the proposed IMU-based IMM is able to react faster to mode changes, giving more reliable outcomes

GPS/INS Integration Accuracy Enhancement Using the Interacting Multiple Model Nonlinear Filters

In this paper, performance evaluation for various single model nonlinear filters and nonlinear filters with interactingmultiple model (IMM) framework is carried out. A high gain (high bandwidth) filter is needed to response fast enoughto the platform maneuvers while a low gain filter is necessary to reduce the estimation errors during the uniformmotion periods. Based on a soft-switching framework, the IMM algorithm allows the possibility of using highly dynamicmodels just when required, diminishing unrealistic noise considerations in non-maneuvering situations. The IMMestimator obtains its estimate as a weighted sum of the individual estimates from a number of parallel filters matchedto different motion modes of the platform. The use of an IMM allows exploiting the benefits of high dynamic models inthe problem of vehicle navigation. Simulation and experimental results presented in this paper confirm theeffectiveness of the method