An Improved IMM Algorithm Based on STSRCKF for Maneuvering Target Tracking

Han, Bo; Huang, Hanqiao; Lei, Lei; Huang, Changqiang; Zhang, Zhuoran

doi:10.1109/access.2019.2912983

Cited by 52 publications

(21 citation statements)

References 33 publications

(36 reference statements)

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“…Due to the unique structure of the antenna array, the resource management of OAR is flexible. On the other hand, the maneuvering target tracking (MTT) plays a vital part for various commercial and military applications and receives plenty of attention [6][7][8][9]. For example, the application areas include battlefield surveillance, air traffic control, air defense, and fire control.…”

Section: Background and Motivationmentioning

confidence: 99%

Joint Adaptive Sampling Interval and Power Allocation for Maneuvering Target Tracking in a Multiple Opportunistic Array Radar System

Han

Pan

Long

et al. 2020

Sensors

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In this paper, a joint adaptive sampling interval and power allocation (JASIPA) scheme based on chance-constraint programming (CCP) is proposed for maneuvering target tracking (MTT) in a multiple opportunistic array radar (OAR) system. In order to conveniently predict the maneuvering target state of the next sampling instant, the best-fitting Gaussian (BFG) approximation is introduced and used to replace the multimodal prior target probability density function (PDF) at each time step. Since the mean and covariance of the BFG approximation can be computed by a recursive formula, we can utilize an existing Riccati-like recursion to accomplish effective resource allocation. The prior Cramér-Rao lower boundary (prior CRLB-like) is compared with the upper boundary of the desired tracking error range to determine the adaptive sampling interval, and the Bayesian CRLB-like (BCRLB-like) gives a criterion used for measuring power allocation. In addition, considering the randomness of target radar cross section (RCS), we adopt the CCP to package the deterministic resource management model, which minimizes the total transmitted power by effective resource allocation. Lastly, the stochastic simulation is embedded into a genetic algorithm (GA) to produce a hybrid intelligent optimization algorithm (HIOA) to solve the CCP optimization problem. Simulation results show that the global performance of the radar system can be improved effectively by the resource allocation scheme.

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Section: Background and Motivationmentioning

confidence: 99%

Joint Adaptive Sampling Interval and Power Allocation for Maneuvering Target Tracking in a Multiple Opportunistic Array Radar System

Han

Pan

Long

et al. 2020

Sensors

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“…The framework of IMM with three filters is used. One of these filters uses the CV model and the other two use the CT model, one of which is to track the target for the right turn and the other for the left turn [24]. In this algorithm, the turn rate of the target is adaptively estimated at each time step without any prior knowledge about the target maneuverability or the range rate.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Turn Rate Estimation for Tracking a Maneuvering Target

2020

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Tracking maneuvering targets accurately is one of the most challenging tasks in the design of aircraft tracking systems. For efficient tracking performance, the target motion predicted by the target motion model needs to match the target's actual motion during the maneuver. For tracking a maneuvering target, a combination of the constant velocity (CV) model and the coordinated turn (CT) model with a known turn rate are incorporated in the interacting multiple model (IMM) algorithm. However, in such a scheme when a target performs an unexpected maneuver, the tracking performance deteriorates, or the scheme may even fail to track the target. To overcome this problem, there exists a scheme in the literature, in which instead of using an a priori knowledge of the target turn rate, it is estimated adaptively using the target acceleration and speed. However, this algorithm uses a three-dimensional model to estimate the turn rate in two-dimensional space, which may result in an inaccurate estimation of the target acceleration, and thus may lead to in inaccurate turn rate value. In this paper, an adaptive algorithm to track a maneuvering target in an IMM framework is proposed. Estimating the turn rate is based on the speed of the target and the radius of the turn, where the latter is computed by a simple method using the previous three successive measurements. Further, a detailed study to select an appropriate transition probability matrix for the proposed algorithm is carried out. Simulation results demonstrate that the proposed tracking algorithm outperforms the other algorithms in terms of its tracking accuracy and consistency, particularly in the realistic situation when neither an a priori knowledge about the target turn rate nor about the range rate information is available to the tracking algorithm. INDEX TERMS Interacting multiple model for radar tracking, target tracking, target turn rate estimation, maneuvering targets.

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“…The transfer between models is determined by Markov probability transfer matrix, which can effectively adjust the probability of each model. [5].…”

Section: Introductionmentioning

confidence: 99%

Combination of IMM Algorithm and ASTRWCKF for Maneuvering Target Tracking

Jin

Guo

2020

IEEE Access

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In this paper, an improved interactive multiple model adaptive strong tracking random weighted cubature Kalman filter (IIMM-ASTRWCKF) algorithm is developed to overcome the low tracking accuracy and easy divergence when dealing with complex maneuvering situations. The algorithm is improved in two aspects: On the one hand, ASTRWCKF is used as the sub filter of IMM algorithm to filter different motion models. By introducing the random weight factor to replace the original weight factor, the accuracy of the algorithm is improved. At the same time, the adaptive strong tracking filter is added to update the prediction covariance matrix and noise covariance matrix for the stability of the algorithm. On the other hand, this algorithm proposes a new method to improve the probability conversion accuracy of IMM by adding time-varying factor to adjust Markov probability transfer matrix. Compared with the performance of IMM-CKF and in dealing with maneuvering problems, IIMM-ASTRWCKF algorithm has better tracking accuracy in solving maneuvering problem.

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An Improved IMM Algorithm Based on STSRCKF for Maneuvering Target Tracking

Cited by 52 publications

References 33 publications

Joint Adaptive Sampling Interval and Power Allocation for Maneuvering Target Tracking in a Multiple Opportunistic Array Radar System

Joint Adaptive Sampling Interval and Power Allocation for Maneuvering Target Tracking in a Multiple Opportunistic Array Radar System

An Adaptive Turn Rate Estimation for Tracking a Maneuvering Target

Combination of IMM Algorithm and ASTRWCKF for Maneuvering Target Tracking

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