Interacting Multiple Model Seeker Filter for Tracking Evasive Targets (original) (raw)
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Target tracking enhancement using a Kalman filter in the presence of interference
2009 IEEE International Geoscience and Remote Sensing Symposium, 2009
In this paper we present a new target tracking enhancement system that uses a Kalman filter in the presence of interference. If the radar (seeker) is affected by different types of interference, this will affect the missile trajectory towards the target and may cause inaccurate tracking. In the new system a six-state Kalman filter is utilized to perform the tracking task and to carry out smoothing to the corrupted trajectory. This also provides good information about the target velocity in three dimensions which is very important information about the target. A three dimensional scenario between target (with high manoeuvre) and missile is used to illustrate the performance of the system in the case when (i) no interference is present and (ii) interference is present. The performance of the filtered trajectory using the Kalman tracker will be assessed for different guidance methods: including (i) proportional navigation (ii) pure pursuit and (iii) constant bearing. The Kalman improvement for the tacking for the three guidance method will be analysed.
Adaptive Extended Kalman Filter For Ballistic Missile Tracking
2017
In the current work, adaptive extended Kalman filter (AEKF) is presented for solution of ground radar based ballistic missile (BM) tracking problem in re-entry phase with unknown ballistic coefficient. The estimation of trajectory of any BM in re-entry phase is extremely difficult, because of highly non-linear motion of BM. The estimation accuracy of AEKF has been tested for a typical test target tracking problem adopted from literature. Further, the approach of AEKF is compared with extended Kalman filter (EKF). The simulation result indicates the superiority of the AEKF in solving joint parameter and state estimation problems.
This paper consider the nonlinear state estimate problem for tracking maneuvering targets. Two methods are introduced to overcome the difficulty of non-linear model. The first method uses Interacting Multiple Model (IMM) which includes 2, 3, 4 and 10 models. These models are linear, each model stands for an operation point of the nonlinear model. Two model sets are designed using Equal-Distance Model-Set Design for each. The effect of increasing the number of models, separation between them and noise effect on the accuracy is introduced. The second method uses Second order Extended Kalman Filter (EKF2) which is a single nonlinear filter. Both methods are evaluated by simulation using two scenarios. A comparison between them is evaluated by computing their accuracy, change of operation range and computational complexity (computational time) at different measurement noise. Based on this study for small range of variation of nonlinear parameter, and low noise the EKF2 introduced quick and accurate tracking. For a large range of nonlinearity and good separation between models of IMM, at minimum noise large and small numbers of models of IMM introduced best accuracy but as the noise increase large number keeps higher accuracy until the large numbers and small numbers of IMM introduced bad accuracy. At high noise optimizing number of models and separation between model sets, IMM introduces better accuracy.
Kalman filter design for target tracking
IEEE Transactions on Aerospace and Electronic Systems, 1980
The problem of solving the matrix Riccati differential equation in the design of Kalman filters for the target tracking problem is considered. An algebraic transformation method is used to reduce the order of the Riccati differential equation and to obtain explicit expressions for the filter gains (in terms of the interceptor/target separation range) which results in a substantial reduction of the computer burden involved in estimating the target states. The applicability of the transform technique is demonstrated for the receiver thermal noise and the target glint noise cases.
Comparing the state estimates of a Kalman filter to a perfect IMM against a maneuvering target
14Th International Conference on Information Fusion, 2011
Tracking maneuvering targets is an important problem. A study was previously performed to compare the state estimation accuracy of a Kalman filter to an interacting multiple model (IMM) for a maneuvering target. The authors defined a maneuvering index to quantify the degree of maneuvering. Their study then compared the state estimates of the two filters as a function of this index. Their results showed that an IMM provides significant improvement over a Kalman filter. That study was revisited and this paper discusses the differing results observed. Our results show that the IMM does improve overall state estimations but much less than in the previous study. This improvement is due to the smaller state estimation errors that the IMM provides over the Kalman filter during the non-maneuvering intervals, rather than the complete domination in performance of the IMM that the previous study revealed. As a result, the "0.5 rule" that the previous authors identified, should be revised.
Interacting multiple model forward filtering and backward smoothing for maneuvering target tracking
2009
The Interacting Multiple Model (IMM) estimator has been proven to be effective in tracking agile targets. Smoothing or retrodiction, which uses measurements beyond the current estimation time, provides better estimates of target states. Various methods have been proposed for multiple model smoothing in the literature. In this paper, a new smoothing method, which involves forward filtering followed by backward smoothing while maintaining the fundamental spirit of the IMM, is proposed. The forward filtering is performed using the standard IMM recursion, while the backward smoothing is performed using a novel interacting smoothing recursion. This backward recursion mimics the IMM estimator in the backward direction, where each mode conditioned smoother uses standard Kalman smoothing recursion. Resulting algorithm provides improved but delayed estimates of target states. Simulation studies are performed to demonstrate the improved performance with a maneuvering target scenario. The comparison with existing methods confirms the improved smoothing accuracy. This improvement results from avoiding the augmented state vector used by other algorithms. In addition, the new technique to account for model switching in smoothing is a key in improving the performance.
Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2019
In this work, an improved interactive multiple model-based smoothing algorithm is proposed for tracking and interception of a maneuvering target. The smoothed states are obtained at some past scan (k À N), but the target interception happens at the current scan. This helps to obtain the real-time benefits of the smoothing. Fixed-lag smoothing interactive multiple model algorithm is employed to follow the target more precisely. Real-time benefit of smoothing is used to improve the decision making in order to achieve better target interception before it reaches its desired destination. Three different simulation environments have been used to analyze the performance and validity of the proposed algorithm. The simulation results are further extended to compare each case in terms of hit error and root-mean-square error.