Maneuvering Target Tracking Algorithm Based on IMMSCKF

Concerning low accuracy even divergence of maneuvering target tracking due to inaccurate tracking model and statistical property, an adaptive Square Root Cubature Kalman Filter (SCKF) is proposed based on the standard SCKF and modified Sage-Husa estimator. The proposed algorithm can estimate the statistical parameters of unknown system noises online, and restrain the tracking error caused by unknown system noises effectively; hence it is applied to maneuvering target tracking. The simulation is preformed latterly and experimental results show that comparing with the standard SCKF algorithm, the adaptive SCKF can achieve better accuracy and stability for maneuvering target tracking while the system noises is unknown and time variation.

The square root cubature particle filter (SRCPF) uses the square root cubature Kalman filter (SRCKF) for generating the proposal distribution. The SRCPF algorithm is easy to be implemented and has numerical stability. Moreover, the SRCKF based proposal distribution approximates the optimal importance distribution by incorporating the current measurement. Simulation results demonstrate that the SRCPF algorithm has the better performance for state estimation than the generic particle filter (GPF), extended particle filter (EPF) and unscented particle filter (UPF), and its calculation cost decreases largely.

Parameter identification of reduced-order electrochemical model simplified by spectral methods and state estimation based on square-root cubature Kalman filter

Variable Structure Multiple Model (VSMM) estimation generalizes Multiple Model (MM) estimation by assuming that the set of models used for MM estimation is time varying. By using VSMM estimation, a large model set which may cover all possible target maneuvers can be used without significant increase of computational load, while maintaining a reasonable estimation accuracy. Various implementable VSMM algorithms, like Model Group Switching (MGS), Likely Model Set (LMS) and Minimal Sub-Model-Set Switching (MSMSS) using the Interacting Multiple Model (IMM) algorithm with a sub-model-set adaptation logic have appeared in the recent literature. However, the use of these algorithms for tracking maneuvering target in clutter has not been explored. In presence of clutter, one need to use data association technique to differentiate target originated measurement from clutter. The probabilistic data association (PDA) has been popularly adopted to many algorithms for tracking in clutter. In this paper, we integrate PDA technique with MSMSS and propose a VSIMM-PDA algorithm for maneuvering target tracking in clutter. A new grating technique to account for potential model errors is used. A numeric example via multiple Monte Carlo runs, which compares the performance of the new algorithm to a standard IMM-PDA in terms of Root Mean Squared error (RMS) and percentage of track loss, is presented.© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In order to solve the problems of model performance error and abnormal measurement value, the square-root Cubature Kalman filter (SRCKF) algorithm has problems such as filter performance degradation or filter effect divergence, this paper proposes a self-estimation based on M. Adaptive Square-root Cubature Kalman Filter based on M estimation (M-ASRCKF) algorithm, this filter algorithm uses an improved Huber weight function to improve the robustness of the SRCKF algorithm; and then adds the Sage-Husa order Excellent unbiased maximum a posteriori (MAP) estimator, real-time monitoring and estimation of unknown or inaccurate noise statistics, improving the tracking accuracy of the traditional SRCKF algorithm. MATLAB simulation results show that the proposed M-estimation-based adaptive SRCKF (M-ASRCKF) algorithm has better anti-non-linearity and anti-outlier interference capabilities, and this algorithm has strong robustness and high accuracy of results.

For multi-target tracking, the IMM (interactive multiple model) algorithm has been proposed. The IMM is expected to reduce tracking errors for both nonmaneuvering and maneuvering targets. However, the IMM requires a heavy computational burden, because it utilizes multiple Kalman filters in parallel. On the other hand, the Kalman filter with a turning acceleration estimator, which can adapt a maneuvering target, has been proposed. The Kalman filter with a turning acceleration estimator is a single two-stage type filter and has a problem of setting threshold for the maneuver detector. In this paper, we propose the hybrid filter with a constant-velocity (CV) filter and a turning acceleration estimation filter. The proposed filter does not require a maneuver detector, because it integrates the outputs of two filters using the likelihood of each filter. And its computational requirement is smaller than the IMM, since it consists of only two Kalman based filters. The proposed method can prevent deteriorating tracking accuracy by reducing the risk of maneuver misdetection when a target maneuvers. We evaluate the performance of the proposed filter by computer simulation, and show the effectiveness of the proposed filter, comparing with the conventional Kalman filter and the two-stage Kalman filter.

Aiming at improving the performance of tracking unmanned aerial vehicle in the battlefield, this paper focuses on the algorithm involving tracking the maneuvering target with a distributed sensor network under non-Gaussian measurement noise. A novel distributed maximum correntropy cubature information filtering based on interactive multiple model is proposed. Taking advantage of the correntropy, we design a maximum correntropy cubature information filtering for each node to estimate the target state under non-Gaussian measurement noise. Then, distributed information fusion based on weighted average consensus is conducted to improve the stability of the sensor network. After that, the information pair is changed, so that a distributed state estimation algorithm based on interactive multiple model is developed to increase the accuracy of maneuvering target tracking. Simulation results and comparison with other algorithms in three typical non-Gaussian measurement noise scenarios are given to evaluate the effectiveness of the proposed algorithm.

Correct knowledge of noise statistics is essential for an effective estimator in maneuvering target tracking. In practice, however, the noise statistics are usually unknown or not perfectly known. To deal with the estimation problem in linear discrete-time systems with Markov jump parameters, where the measurement noise covariance is unknown, a novel approach is presented in this paper. This approach is based on the interacting multiple model (IMM) framework. An H ∞ filter is employed to construct a noise statistics estimator to obtain the information which is necessary for the IMM algorithm. In our approach, even the priori knowledge of noise statistics is not needed. The noise statistics loss problem is solved while the merits of IMM algorithm is reserved. The effectiveness of the proposed approach is demonstrated in comparison with single-model H ∞ filter through Monte Carlo simulation for maneuvering target tracking.

An improved Interacting Multiple Model tracking algorithm is presented to solve the maneuvering target tracking problem. The model set including Constant Velocity Model, Constant Acceleration Model and Current Statistical Model is designed, and the Interacting Multiple Model method is used to select model. The improved unscented Kalman filter algorithm is proposed to enhance filtering effect. The simulation and actual tracking experiment results indicate that the algorithm in maneuvering target tracking is valid.

Estimation of 3D piecewise coordinated turn target maneuvers with unknown turning rates is considered. The State Dependent Differential-Difference Riccati Equation (SDDRE) and the Interacting Multiple Model (IMM) approaches are compared. The comparison is kept as simple as possible and effort is made to perform it on a common basis. The SDDRE uses a non-linear modeling of the target's equations of motion that takes into account the rotation of the velocity and acceleration vectors as the target maneuvers. The IMM implements two models: a constant acceleration target model and a simple uncoupled coordinated turn maneuver model using the average-expected turn rate. The simulated trajectory is a combination of constant velocity trajectory segments and coordinated turn segments. The simulations show advantages and disadvantages of each of the approaches. It is demonstrated that the SDDRE approach filter is not sensitive to the trajectory orientation and that it has faster transient thus demonstrates a kind of adaptivity to the turning rate level. The IMM approach filter has the advantages that when the target trajectory is close to one of the hypotheses it has better performance than the SDDRE based filter.

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.

Underwater acoustic sensor networks (UASNs) play an important role in the ocean's protection. They can realize real-time data collection, monitoring, exploration, and many other underwater applications by connecting and coordinating seafloor sensors and underwater vehicles. To achieve these application objectives, such as fishes tracking in biological monitoring field and submarines tracking in military field, target tracking is one of the key techniques. This paper presents a centralized fusion algorithm based on the interacting multiple models and the adaptive Kalman filter (IMMCFAKF) for target tracking in UASNs. Specifically, by introducing an adaptive forgetting factor into the optimal centralized fusion Kalman filter algorithm, the optimal centralized fusion adaptive Kalman filter (CFAKF) algorithm is obtained first. Then, combining the superiorities of both the optimal CFAKF algorithm and the conventional IMM algorithm, the optimal IMMCFAKF is achieved. The numerical simulations are provided to demonstrate the effectiveness of the proposed optimal IMMCFAKF algorithm.

Video synthetic aperture radar (VideoSAR) combines SAR imaging and video techniques together, enabling continuous surveillance of a specific area, which has the advantages of high frame rate, high resolution, the ability of real-time tracking, etc. For VideoSAR systems, detecting and tracking the shadows of moving targets can effectively avoid the complex algorithms needed for moving target location and refocusing. In this paper, an interactive multiple model (IMM) filter is designed for VideoSAR moving target tracking task. Considering of the maneuverability of ground moving target is more complex than aerial target, we define three different target motion models according to the motion characteristics of ground moving targets, including a stop model and a rotated model. The experimental results show that the proposed IMM filter outperforms conventional filter for ground moving target tracking application.

To increase the probability of destroying a maneuvering target (e.g. ballistic missile), a framework of multi-missiles interception is presented in this paper. Each intercepting missile is equipped with an IR Image seeker which can provide excellent stealth ability during its course of tracking the ballistic missile. Such intelligent ranging system integrates the Interacting Multiple Model (IMM) technique and the concept of reachable set to find the optimal interception results by minimizing the energy of pursuing the maneuvering target. The proposed guidance law of every missile interceptor is designed based on pursuit and evasion game theory while considering the motion of the target in 3-D space such that the distance between the missiles and the target is minimized. Finally, extensive computer simulations have been conducted to validate the performance of the proposed system.

Research on Tracking Method of Space Target Based on Interacting Multiple Models