Maneuvering Target Tracking Algorithm Research Articles

A Maneuvering Target Tracking Algorithm Based on the Interacting Multiple Models

In view of the limitation of the traditional Kalman filter with which the tracking has big calculation amount and low tracking precision base on the model of CV or CA movement, a algorithm is suggested in the present article which is the Interacting Multiple Models Kalman filter(IMM-KF) with the CV and CA model. Under keeping invariant of the tracking precision of linear motion, This method can make the tracking precision of curve motion approach the linear motion’s. The system simulation results show that this algorithm improved the tracking precision of system with the IMM-KF. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.285 1

Separated Maneuvering Target Tracking Algorithm Along with Three Cartesian Coordinates for Three‐Dimension Radar

AbstractWhen tracking a maneuvering target with a three‐dimensional radar, which can measure range, azimuth, and elevation of the targets, block processing managed by a Markov chain is often adopted under the framework of the interactive multiple model (IMM) estimator. However, tracking performance may degrade because different maneuvers influence each other among three Cartesian coordinates. In order to solve this problem, this paper presents a separated maneuvering target tracking algorithm. First, it formulates the diagonalization and minimization of the upper bound of the modified converted measurement noise covariance matrix as an optimization problem. Then, the existence and uniqueness of the solution for this optimization problem are demonstrated. Finally, it exploits the IMM algorithm to estimate the target states along with each Cartesian coordinate separately. The tracking performance of the proposed algorithm is compared with the square root IMM with L‐D factorization, IMM with sequential importance sampling particles, and multiple‐model estimation with model‐group switching. Monte‐Carlo simulation results demonstrate that the root mean square error of the proposed tracking algorithm is lower than that of the above algorithms for maneuvering target tracking.

Probability hypothesis density filter based on strong tracking MIE for multiple maneuvering target tracking

Taking into account the difficulties of multiple maneuvering target tracking due to the unknown target number and the uncertain acceleration, a novel multiple maneuvering target tracking algorithm based on the Probability Hypothesis Density (PHD) filter and Modified Input Estimation (MIE) technique is proposed in this paper. First, the unknown acceleration vector is added to the target state to form a new augmented state vector. Then, strong tracking filter multiple fading factors are introduced to the MIE method which can adjust the prediction covariance and the corresponding filter gain at different rates in real time, so that the MIE method can adaptively track high maneuvering targets well. Finally, we combine this adaptive MIE method with the PHD filter, which can effectively track multiple maneuvering targets without much prior information. Simulation results show that the proposed algorithm has a higher tracking precision and a better real-time performance than the conventional maneuvering target tracking algorithms.

Maneuvering target tracking algorithm based on fuzzy interacting multiple model

Maneuvering target tracking algorithm based on fuzzy interacting multiple model

Second-Order Markov Chain Based Multiple-Model Algorithm for Maneuvering Target Tracking

A multiple-model algorithm for maneuvering target tracking is proposed. It is referred to as a second-order Markov chain (SOMC)-based interacting multiple-model (SIMM) algorithm. The target maneuver process is modeled by a SOMC to incorporate more information. SIMM adopts a merging strategy similar to that of the interacting multiple-model (IMM) algorithm, except that the one-step model transition probabilities are updated based on the SOMC. A scheme is proposed to design the transition probabilities of the SOMC for target tracking. The performance of the proposed SIMM algorithm is evaluated via several scenarios for maneuvering target tracking. Simulation results demonstrate the effectiveness of SIMM compared with IMM, the second-order IMM (IMM2) algorithm, and the likely-model set (LMS) algorithm. It is shown that SIMM performs about the same as IMM2 but requires only n filters versus n2 filters in IMM2 for n models. The effectiveness and efficiency of combining SIMM and LMS for state estimation are also demonstrated in the simulation.

Improved Particle Filter for Target Tracing Application based on ChinaGrid

Most practical target tracking are usually maneuvering, while most target tracking algorithm are linear filter. More estimation error is introduced from linear filter. Nowadays more and more researchers pay their attention in Maneuvering Target Tracking algorithm. Particle filter has been developed for estimation of nonlinear system states. This paper presents an improved particle filter, which can apply the maneuvering target tracking problem. In practice, the particle filter would take abundant computation for estimate the maneuvering target tracking. The ChinaGrid system use the agile and distributed federations to reduce the computing time, which achieve to fast resolution for particle filter computation of target tracing application. Lastly the simulation proves it.

Maneuvering Target Tracking Algorithm Based on Improved "Self-Adaptive Turning Rate" Model

Multi-model (interactive multi-model )tracking is an effective method for maneuvering target tracking, and turning model is widely studied and applied in multi-model tracking modeling and model set establishment. A detailed study is carried out on the self-adaptive turning model modeling method for maneuvering target tracking. Also a summary is made about the method of establishing tracking model by calculating the turning rate. The shortcomings of literature [4、6]'s self-adaptive turning model in actual application is also pointed out, with two modeling methods adopting average turning rate presented accordingly. The simulation test proves the necessity of improvement and the validity of the new model.

Performance Evaluation of Unscented Kalman Filter for Gaussian/non-Gaussian Tracking Application

State estimation theory is one of the best mathematical approaches to analyze variants in the states of the system or process. The state of the system is defined by a set of variables that provide a complete representation of the internal condition at any given instant of time. Filtering of Random processes is referred to as Estimation, and is a well defined statistical technique. There are two types of state estimation processes, Linear and Nonlinear. Linear estimation of a system can easily be analyzed by using Kalman Filter (KF) but is optimal only when the model is linear .But Most of the state estimation problems are nonlinear, thereby limiting the practical applications of the KF and EKF. Unscented Kalman filter and Particle filter are best known for nonlinear estimates. The approach in this paper is to analyze the algorithm for maneuvering target tracking using bearing only measurements for both Gaussian /Nongaussian distributions where UKF provides better probability of state estimation. Montecarlo computer simulations are used to analyse the performance .The simulations results showed that UKF provides better performance for Gaussian distributed models compared to the nongaussian models. DOI: http://dx.doi.org/10.11591/ijece.v3i1.326

S-IMM: Switched IMM Algorithm for Maneuvering Target Tracking

S-IMM: Switched IMM Algorithm for Maneuvering Target Tracking

Steady State Filters Bank Based Interacting Multiple-Model-Extended Viterbi Algorithm for Maneuvering Target Tracking

The performance of Interacting Multiple-Model (IMM) algorithm depends on model set and filters bank. Most of the improved IMM algorithms originate from designing more reasonable and effective model set structures. One example in case is the Interacting Multiple-Model-Extended Viterbi (IMMEV) algorithm, which can not only inherit the effective cooperation strategies of the IMM algorithm, but also adapt to the outside world like variable structure IMM algorithm by eliminating those mode transition probabilities that are harmful. However, this design approach is difficult to achieve significant tracking performance and substantial complexity reduction, simultaneously. In this paper, the steady state filters bank based Interacting Multiple-Model-Extended Viterbi algorithm for maneuvering target tracking is presented. The main idea of the algorithm is to further improve the IMM-EV algorithm performance and reduce its complexity by designing a new filters bank. The new approach is based on the steady state filters (ab andabg filters) bank using IMM-EV algorithm design instead of a Kalman filters (second and third order filters) bank. Real maneuvering target tracking application experiments demonstrate that the SS-IMM–EV algorithm with some suitable choice of ”m” is superior to the IMM, Fast-IMM and IMM-EV algorithms, which can obtain a considerable tracking performance and reduce complexity markedly.

FEATURE AIDED SWITCHING MODEL SET APPROACH FOR MANEUVERING TARGET TRACKING

Feature aided maneuver detector is popular for its low detection delay and high detection probability in decision-based single- model maneuvering target tracking (MTT) algorithms. We propose a switching model-set approach based on the feature aided maneuver detector for MTT. The flltering error dynamics in terms of detection delay are presented and a upper bound for detection delay with given standard Kalman flltering errors is accessed. Subsequently, a feature aided maneuver detector is introduced to enhance detection performance, and the flltering algorithm is proposed, including detailed flltering steps and computational formulae. Simulation results show that the proposed algorithm outperforms the popular autonomous multiple model (AMM) and interacting MM (IMM) algorithms.

A novel algorithm for multiple maneuvering target tracking

It is difficult to track multiple maneuvering targets in clutter due to the uncertain acceleration and the disturbance of the clutter. Taking these into account, a strong tracking modified input estimation (STMIE) is presented in this paper, which is combined with maximum entropy fuzzy joint probabilistic data association for multiple maneuvering target tracking. Strong tracking multiple fading factors are introduced in order to enhance the tracking performance of MIE for high maneuvering targets. The prediction covariance can be adjusted in real time by the multiple time-varying fading factors and the different data channels are faded at different rates. Simulations show the effectiveness of the proposed method for the cross and high maneuvering target tracking. Compared with the conventional current statistical model (CS) combined with maximum entropy fuzzy joint probabilistic data association, the proposed algorithm has higher tracking accuracy and good engineering application prospect. Key words: Multiple maneuvering target tracking, data association, strong tracking filter, modified input estimation, multiple fading factor.

A Maneuvering Target Tracking Algorithm Based on Gaussian Filter for Multiple Passive Sensors

When tracking a maneuvering target by multiple passive sensors, two problems need to be considered, one is the nonlinear problem, another is the maneuvering problem. Taking these into account, a Gaussian filter (GF) for nonlinear Bayesian estimation is introduced based on a deterministic sample selection scheme, which can solve the nonlinear problem better than the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Then, a new maneuvering target tracking algorithm is proposed based on the GF and Interacting Multiple Mode (IMM), called IMM-GF method in this paper. Simulation results show that the proposed method has better performance than the IMM-EKF and IMM-UKF in tracking a maneuvering target for multiple passive sensors.

Maneuvering Target Tracking Algorithm with Multiple Passive Sensors in Clutter Environment

To maneuvering target tracking with multiple passive sensors in clutter environment, a novel algorithm based on particle filter is proposed. In this algorithm, the likelihood function of particle filter in clutter is derived. Then the particle filter and Interactive Multiple Model (IMM) method are integrated. The former solves the proplem of passive target manoeuvre and the latter deals with the nonlinear problem of the measurement equation. In order to avoid the unobservability problem of passive target tracking, a nonlinear measurement model of multiple passive sensors is founded, and the algorithm can deal with the case of non-gaussian noise. Finally, the simulation results show that the proposed algorithm is effective, and its performance is superiority over the interacting multiple model-probabilistic data association filter (IMM-PDAF).

A tracking algorithm for maneuvering targets

A methodology for the tracking of maneuvering targets is presented. A quickest-detection scheme based on the innovation sequence is developed for a prompt detection of target maneuvers. The optimal length of a sliding window that minimizes the maneuver detection delay for a given false-alarm rate is determined. After maneuver detection, the system model is modified by adding a maneuver term. A recursive algorithm is proposed to estimate the maneuver magnitude. With this estimate, a modified Kalman filter is used for tracking. Simulation results demonstrate the superior performance of the algorithm, especially during target maneuvers.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>