Fast Adaptive Update Rate for Phased Array Radar Using IMM Target Tracking Algorithm

Benoudnine, H.; Keche, Mokhtar; Ouamri, Abdelaziz; Woolfson, M.S.

doi:10.1109/isspit.2006.270811

Cited by 12 publications

(8 citation statements)

References 5 publications

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“…In addition, many other RIS algorithms are based on similar principles of the algorithms mentioned above [5], [9]- [11]. However, the previously proposed RIS algorithms use target, radar, and environment models, making them vulnerable to modeling errors.…”

Section: Introductionmentioning

confidence: 99%

Time Budget Management in Multifunction Radars Using Reinforcement Learning

Pulkkinen

Aittomaki

Ström³

et al. 2021

2021 IEEE Radar Conference (RadarConf21)

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An adaptive revisit interval selection (RIS) in multifunction radars is an integral part of efficient time budget management (TBM). In this paper, the RIS problem is formulated as a Markov decision process (MDP) with unknown state transition probabilities and reward distributions. A reward function is proposed to minimize the tracking load (TL) while maintaining the track loss probability (TLP) at a tolerable level. The reinforcement learning (RL) problem is solved using the Qlearning algorithm with an epsilon-greedy policy. Compared to a baseline algorithm, the RL approach was capable of maintaining the tracks while reducing the tracking load significantly.

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Section: Introductionmentioning

confidence: 99%

Time Budget Management in Multifunction Radars Using Reinforcement Learning

Pulkkinen

Aittomaki

Ström³

et al. 2021

2021 IEEE Radar Conference (RadarConf21)

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“…Overall, the stability and accuracy of the tracking method must be met simultaneously; if the tracks are not updated adaptively-their update rates are not specific to their nature, then radar resources will be wasted when the target stops maneuvering but the update rate is still constantly high. The target trajectory is generally composed of segments corresponding to different motions; therefore, only an algorithm like the IMM which contains a filter bank of different motion models can realistically model the motion of the target [5][6][7][8].Here, we have made use of the IMM algorithm as the basis for resource management optimization through improving the tracking method; the IMM enjoys a bank of filters appropriate for particular kinematics of different targets, which reduces the tracking load; therefore, if we adaptively make use of such a filter bank, we can greatly minimize the use of radar resources. Choosing a revisit time according to the maneuvering of the target while keeping tracking error under a given threshold, we have proposed an adaptive version of the IMM algorithm, which has been shown through computer simulations to considerably reduce the use of radar resources.…”

Section: Introductionmentioning

confidence: 99%

Error-based Adaptive Update Rate in multi-function Phased-array Radar Using IMM Target-Tracking Algorithm

Zamani¹,

Hamadani²,

Mofrad³

2017

ijecs

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Abstract-An adaptive-update-rate target-tracking algorithm is indeed a developed version of constant-update-rate tracking algorithms. The update rate is closely related with the level of target maneuvering and the required quality of tracking performancethe shorter the time intervals between two successive updates, the better the radar resources are made use of. Considering the needs of the phased-array radar system and multi-function tracking, we here propose an algorithm which makes a better use of radar resources; therefore, many targets can be tracked with an improved accuracy, and more regions can be detected. The proposed algorithm is based upon the adaptive interactive tracking algorithm (AIMM); the revisit time has been calculated using Van Keuk method, and then the fast AIMM has been amended such that the tracking error is no more than that in Van Keuk method. Our proposed algorithm which has a revisit time different than Van Keuk's results in 62% saving of the radar resources.

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“…A typical motion model with Singer model, "current" statistical model, interacting multiple models (IMM). In the traditional IMM model [1,2], assuming that the target in different motion model between the transfer probabilities is fixed, this assumption is not sufficient to account for the movement model of selective, but the use of similar "hard decision "thinking model of transfer probability in a fixed value. In fact, when the target motion model is a trend, the traditional IMM algorithm only through the mediation of different observation vector under conditions of motion model the posterior probability weighting of motion model between the "comprehensive", and do not take into account the Markov transition probability matrix design is not reasonable.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Interacting Multiple Model for Vehicle Target Tracking Method

2013

AMR

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Abstract.In the field of traffic safety vehicle target tracking prediction as the background, this paper proposes an adaptive interacting multiple model tracking algorithm. According to the field of transportation vehicle movement state characteristics, based on the uniform(CV)and uniformly accelerated motion(CA)model, based on new information structure model of motion of the likelihood function, online adaptive adjustment model of the noise variance and the Markov matrix, realization of maneuvering target movement model and model set adaptation, not only improved IMM algorithm for tracking accuracy, and enhances the real-time performance of system, the simulation results show that, the algorithm for tracking precision compared to the traditional IMM method has bigger improvement.

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Fast Adaptive Update Rate for Phased Array Radar Using IMM Target Tracking Algorithm

Cited by 12 publications

References 5 publications

Time Budget Management in Multifunction Radars Using Reinforcement Learning

Time Budget Management in Multifunction Radars Using Reinforcement Learning

Error-based Adaptive Update Rate in multi-function Phased-array Radar Using IMM Target-Tracking Algorithm

An Adaptive Interacting Multiple Model for Vehicle Target Tracking Method

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