<title>Monopulse radar tracking using an adaptive interacting multiple-model method with extended Kalman filters</title>

The extended Kalman filter (EKF) as a primary integration scheme has been applied in the Global Positioning System (GPS) and inertial navigation system (INS) integrated system. Nevertheless, the inherent drawbacks of EKF contain not only instability caused by linearization, but also massive calculation of Jacobian matrix. To cope with this problem, the adaptive interacting multiple model (AIMM) filter method is proposed to enhance navigation performance. The soft-switching characteristic, which is provided by interacting multiple model algorithm, permits process noise to be converted between upper and lower limits, and the measurement covariance is regulated by Sage adaptive filtering on-line Moreover, since the pseudo-range and Doppler observations need to be updated, an updating policy for classified measurement is considered. Finally, the performance of the GPS/INS integration method on the basis of AIMM is evaluated by a real ship, and comparison results demonstrate that AIMM could achieve a more position accuracy.

The results of an investigation into the use of the adaptive interactive multiple model (AIMM) algorithm, in which the sampling rate was adaptively selected among a predetermined set, for the benchmark problem of the beam pointing control of a phased array radar for tracking maneuvering targets is presented. The results of the AIMM algorithm where a fixed sampling rate policy was used has also been given for the same problem for comparison purposes. (8 pages)

The grid points of the Adaptive Grid Interactive Multiple Model (AGIMM) algorithm change slowly at the target high maneuvering and fluctuate when the target is moving at a constant speed. The paper proposes an improved AGIMM algorithm based on maneuverability of target. Firstly, divide the Maneuverability zones by Filter residuals. Secondly, according to different zones, the grid points are updated in different ways to speed up the convergence of the model when the target is high maneuvering, and reduce the fluctuation of the model when the target is low maneuvering. Realize the model update method according to the target maneuverability adaptive adjustment. The simulation shows that the improved algorithm significantly reduces the tracking error regardless of whether the target turns or not.

Previous papers (1997, 1999, 2000) have described a tracking approach which utilized a Combined Kalman Filter (CKF), adaptive tracking for maneuver tracking, and the JVC association algorithm for reports to tracks, for use in Airborne Early Warning (AEW) applications. In this paper we present our incorporation of Interacting Multiple Model (IMM) tracking. First our previous AEW tracking approach is briefly reviewed as most of this approach is still utilized and forms our baseline. The new IMM approach and equations are then described. Then the two IMM tracking approaches used are discussed. One involves a two model IMM containing two constant velocity models, one a low process noise and the other a high process noise model. The other approach involves three IMM filter models, a coordinated turn filter model, and the same two constant velocity filter models as in the two model IMM approach. Results for both IMM approaches and the baseline tracker are shown. The results presented involve a 120 target scenario with two second update time with simulated radar data. In addition computer timing results are presented. These results indicate that while the three model IMM approach provides the best tracking results, it does so at a substantial computational cost. The two model IMM provides comparable tracking improvement but at a far less computational cost.

Adaptive interacting multiple model for underwater maneuvering target tracking with one-step randomly delayed measurements

Inserting synthetic objects into video sequences has gained much interest in recent years. Fast and robust vision-based algorithms are necessary to make such an application possible. Traditional pose tracking schemes using recursive structure from motion techniques adopt one Kalman filter and thus only favor a certain type of camera motion. We propose a robust simultaneous pose tracking and structure recovery algorithm using the interacting multiple model (IMM) to improve performance. In particular, a set of three extended Kalman filters (EKFs), each describing a frequently occurring camera motion in real situations (general, pure translation, pure rotation), is applied within the IMM framework to track the pose of a scene. Another set of EKFs,one filter for each model point, is used to refine the positions of the model features in the 3-D space. The filters for pose tracking and structure refinement are executed in an interleaved manner. The results are used for inserting virtual objects into the original video footage. The performance of the algorithm is demonstrated with both synthetic and real data. Comparisons with different approaches have been performed and show that our method is more efficient and accurate.

Improvement of dead reckoning accuracy is essential for robotic localization system and has been intensively studied. However, existing solutions cannot provide accurate positioning when a robot suffers from changing dynamics such as wheel slip. In this paper, we propose an interacting multiple model (IMM) framework to detect and compensate for wheel slip. Firstly, two different types of extended Kalman filter (EKF) are designed to consider both no-slip and slip dynamics of mobile robots. Then a support vector machine (SVM) for slip estimation is constructed using real world training data. The trained model is utilized along with the two EKFs in the IMM framework. The approach is evaluated with experiments and the results show that the proposed approach improves positioning and slip compensation compared to the conventional approach.Keywordsdead reckoninginteracting multiple modelsupport vector machineslip detection

In this paper, we propose a novel approach for Failure Detection and Identification (FDI) in nonlinear systems based on the Interacting Multiple Model (IMM) Extended Kalman Filter (EKF) approach. In the nonlinear system FDI application, the main idea consists of representing each failure mode by a model and combining the outputs of EKF's based on different models in a near-optimal way. This IMM-FDI filter provides not only failure detection and identification but also a near-optimal estimate of the system state (even during a failure). The approach has been applied successfully to a problem of spacecraft autonomy for the detection and identification of sensor (gyro, star tracker) and actuator failures. The results of this application show that IMM-EKF detects and identifies failures much more rapidly and reliably than the multi-hypothesis EKF. Furthermore, it handles satisfactorily both permanent and transient failures. Current efforts are underway to perform extensive validation testing on high-fidelity simulation models of representative spacecraft.

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.

This paper presents a tracking algorithm based on the interacting multiple model (IMM) algorithm and pseudo linear Kalman filter (PKF) in order to improve the performance of traditional methods in maneuvering target tracking. The IMM-PKF algorithm uses the constant velocity (CV) model and coordinated turn (CT) model to form a model set, and the filtering algorithm uses PKF. In target tracking, IMM is used to improve the matching between state prediction and actual motion, therefore improving the accuracy of maneuvering tracking. The performance of extended Kalman filter (EKF), PKF, and IMM-PKF is compared through simulation and experiment. The results show that the IMM-PKF method has the best tracking accuracy among the three methods in helicopter tracking.

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.

An algorithm to estimate the tangential and normal accelerations directly using the Doppler radar measurement in an online closed loop form is proposed. Specific works are as follows: first, the tangential acceleration and normal acceleration are taken as the state variables to establish a linear state transition equation; secondly, the decorrelation unbiased conversion measurement Kalman filter (DUCMKF) algorithm is proposed to deal with the strongly nonlinear measurement equation; thirdly, the geometric relationship between the range rate and the velocity direction angle is used to obtain two estimators of the velocity direction angle; finally, the interactive multiple model (IMM) algorithm is used to fuse the estimators of the velocity direction angle and then the adaptive IMM of current statistical model based DUCMKF (AIMM-CS-DUCMKF) is proposed. The simulation experiment results show that the accuracy and stability of DUCMKF are better than the sequential extended Kalman filter algorithm, the sequential unscented Kalman filter algorithm, and converted measurement Kalman filter algorithms; on the other hand they show that the AIMM-CS-DUCMKF can obtain the high accuracy of the tangential and normal accelerations estimation algorithm.

In the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) of non-linear system, the mathematical model and the noise statistics are essential; this limitation was settled by adaptive algorithm. The adaptive extended Kalman filter (AEKF) and adaptive unscented filter (AUKF) were proposed to solve the filtering problem of the system with unknown mathematical model or noise statistics in information fusion. The simulation result shows that the adaptive algorithm in EKF and UKF can improve the performance of filter and decrease the computing time.

An adaptive direct recurrent neural network model is developed for nonlinear dynamic system modelling in this paper. The model adaptation is achieved with the extended Kalman filter (EKF). A novel recursive algorithm is proposed to calculate the Jacobian matrix in the model adaptation so that the algorithm is simple and converges fast. The effectiveness of the developed adaptive model is demonstrated by applying to modelling a simulated continuous stirred tank reactor (CSTR). The model converges to the new process dynamics very quickly after a constant disturbance is added, and therefore can be used as an adaptive model in the adaptive model predictive control or internal model control for time-varying systems or fault tolerant control of nonlinear systems.

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.