A joint beam and dwell time allocation strategy for multiple target tracking based on phase array radar system

Wang, Xiangli; Yi, Wei; Xie, Mingchi; Kong, Lingjiang

doi:10.23919/icif.2017.8009856

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…Based on the application of distributed MIMO radar target detection, Jebali et al proposed a power allocation method that jointly optimizes the total transmit power and intercept probability [9]. In terms of airborne radar radiation time control, Wang et al studied the joint assignment of beam pointing and dwell time in the multi-target tracking process of phased array radar [10]. Ghazal M et al studied infrared sensors and electronic support measures to assist airborne radar in target tracking, which can effectively reduce the number of external radiations of airborne radar [11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Tracking of High-Maneuvering Targets Based on Multi-Feature Fusion Trajectory Clustering: LPI’s Purpose

Lei

Chen

Ding

et al. 2022

Sensors

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Since the passive sensor has the property that it does not radiate signals, the use of passive sensors for target tracking is beneficial to improve the low probability of intercept (LPI) performance of the combat platform. However, for the high-maneuvering targets, its motion mode is unknown in advance, so the passive target tracking algorithm using a fixed motion model or interactive multi-model cannot match the actual motion mode of the maneuvering target. In order to solve the problem of low tracking accuracy caused by the unknown motion model of high-maneuvering targets, this paper firstly proposes a state transition matrix update-based extended Kalman filter (STMU-EKF) passive tracking algorithm. In this algorithm, the multi-feature fusion-based trajectory clustering is proposed to estimate the target state, and the state transition matrix is updated according to the estimated value of the motion model and the observation value of multi-station passive sensors. On this basis, considering that only using passive sensors for target tracking cannot often meet the requirements of high target tracking accuracy, this paper introduces active radar for indirect radiation and proposes a multi-sensor collaborative management model based on trajectory clustering. The model performs the optimal allocation of active radar and passive sensors by judging the accumulated errors of the eigenvalue of the error covariance matrix and makes the decision to update the state transition matrix according to the magnitude of the fluctuation parameter of the error difference between the prediction value and the observation value. The simulation results verify that the proposed multi-sensor collaborative target tracking algorithm can effectively improve the high-maneuvering target tracking accuracy to satisfy the radar’s LPI performance.

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Section: Introductionmentioning

confidence: 99%

Adaptive Tracking of High-Maneuvering Targets Based on Multi-Feature Fusion Trajectory Clustering: LPI’s Purpose

Lei

Chen

Ding

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Shi et al designed a scheme to allocate power based on LPI radar in a complex electromagnetic background [6]. Wang et al studied the joint allocation of beam pointing and dwell time of phased array radar in multitarget tracking [7]. Shi et al proposed an optimized method of parameters including radar's illumination interval, dwell time, and radiated power in target tracking, which showed the achievement of minimum interception probability [8].…”

Section: Introductionmentioning

confidence: 99%

Netted Radar Tracking with Multiple Simultaneous Transmissions against Combined PDS Interception

Wang

Zhou

2020

Journal of Sensors

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One of the advantages of a netted airborne radar system (NARS) is escaping interception of the passive detection system (PDS) while tracking a target. A significant tactic to realize tracking without PDS interception is to study the low probability of interception (LPI) time of NARS. Firstly, this paper analyses the power, frequency, and platform interception probabilities of a combined PDS consisting of a radar-warning receiver (RWR) and an electronic support measurement (ESM). Secondly, this paper takes interactive multiple models (IMM) to describe the target tracking process and introduces a binary hypothesis test for chi square as well as noncentralized chi square distributions as a detection criterion of NARS during target tracking after the design of adaptive dwell time and the maximum illumination interval algorithm. Finally, based on experiential moving platform interception probabilities of a RWR and an ESM, a simplified math model is presented to estimate LPI time of NARS when the parameters are partially known. Simulations illustrate that the simultaneous management of radiation power and time is crucial for NARS against combined PDS interception.

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“…In ref. [ 2 ], a joint allocation method for beam pointing and dwell time was proposed for multiple target tracking. The expression of Bayesian Cramér-Rao Lower Bound (BCRLB) with regard to the dwell time was derived, from which the total dwell time can be minimized given the tracking accuracy of each target.…”

Section: Introductionmentioning

confidence: 99%

Dwell Time Allocation Algorithm for Multiple Target Tracking in LPI Radar Network Based on Cooperative Game

Xue

Wang

Zhu

2020

Sensors

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To solve the problem of dwell time management for multiple target tracking in Low Probability of Intercept (LPI) radar network, a Nash bargaining solution (NBS) dwell time allocation algorithm based on cooperative game theory is proposed. This algorithm can achieve the desired low interception performance by optimizing the allocation of the dwell time of each radar under the constraints of the given target detection performance, minimizing the total dwell time of radar network. By introducing two variables, dwell time and target allocation indicators, we decompose the dwell time and target allocation into two subproblems. Firstly, combining the Lagrange relaxation algorithm with the Newton iteration method, we derive the iterative formula for the dwell time of each radar. The dwell time allocation of the radars corresponding to each target is obtained. Secondly, we use the fixed Hungarian algorithm to determine the target allocation scheme based on the dwell time allocation results. Simulation results show that the proposed algorithm can effectively reduce the total dwell time of the radar network, and hence, improve the LPI performance.

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A joint beam and dwell time allocation strategy for multiple target tracking based on phase array radar system

Cited by 7 publications

References 20 publications

Adaptive Tracking of High-Maneuvering Targets Based on Multi-Feature Fusion Trajectory Clustering: LPI’s Purpose

Adaptive Tracking of High-Maneuvering Targets Based on Multi-Feature Fusion Trajectory Clustering: LPI’s Purpose

Netted Radar Tracking with Multiple Simultaneous Transmissions against Combined PDS Interception

Dwell Time Allocation Algorithm for Multiple Target Tracking in LPI Radar Network Based on Cooperative Game

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