Joint time-space resource allocation and waveform selection for the collocated MIMO radar in multiple targets tracking

Li, Xi; Cheng, Ting; Su, Yang; Peng, Han

doi:10.1016/j.sigpro.2020.107650

Cited by 27 publications

(14 citation statements)

References 19 publications

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“…(24) Similar to the optimization problem in (22), the PSO algorithm can also be adopted to tackle the problem of multi-dimensional resource management in (24), where the difference is that the illumination power, dwell time, waveform bandwidth, and pulse length of each airborne radar are mapped to the positions of the particles.…”

Section: ) Multi-dimensional Transmit Resource Managementmentioning

confidence: 99%

“…It is worth to mention that not only transmit resources but also waveform parameters have effects on the target tracking performance. Thus, Cheng et al in [24] formulate the optimization model for joint timespace resource allocation and waveform selection for colocated MIMO radar in multi-target tracking, where the objective function in terms of system resource consumption and target tracking accuracy is minimized by adaptively coordinating the variables of interest, i.e., revisit interval, sub-array number, transmit energy, and waveform parameters. More recently, reference [25] extends the work of [24] to netted colocated MIMO radar case in order to achieve better trade-off between illumination resource burden and target tracking precision.…”

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confidence: 99%

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Joint Route Optimization and Multidimensional Resource Management Scheme for Airborne Radar Network in Target Tracking Application

Shi

Dai

Wang

et al. 2022

IEEE Systems Journal

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In this paper, we investigate the problem of joint route optimization and multi-dimensional resource management for airborne radar network in target tracking application. The mechanism of the proposed joint route optimization and multidimensional resource management (JRO-MDRM) scheme is to adopt the optimization technique to collaboratively design the flight route, transmit power, dwell time, waveform bandwidth, and pulse length of each airborne radar node subject to the system kinematic limitations and several resource budgets, with the aim of simultaneously enhancing the target tracking accuracy and low probability of intercept (LPI) performance of the overall system. The predicted Bayesian Cramér-Rao lower bound (BCRLB) and probability of intercept are calculated and employed as the metrics to gauge the target tracking performance and LPI performance, respectively. It is shown that the resulting optimization problem is non-linear and non-convex, and the corresponding working parameters are coupled in both objective functions, which is generally intractable. By incorporating the particle swarm optimization (PSO) and cyclic minimization approaches, an efficient four-step solution algorithm is proposed to deal with the above problem. Extensive numerical results are provided to demonstrate the correctness and advantages of our developed scheme compared with other existing benchmarks.Index Terms-Joint route optimization and multi-dimensional resource management (JRO-MDRM), airborne radar network, target tracking, Bayesian Cramér-Rao lower bound (BCRLB), low probability of intercept (LPI), particle swarm optimization (PSO). I. INTRODUCTION A. Background and Literature ReviewWith the significant superiorities over the static radar systems, the airborne radar network has received considerable interests in various applications, such as air defence, missile guidance, cooperative navigation, target localization, and so

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Section: ) Multi-dimensional Transmit Resource Managementmentioning

confidence: 99%

mentioning

confidence: 99%

Joint Route Optimization and Multidimensional Resource Management Scheme for Airborne Radar Network in Target Tracking Application

Shi

Dai

Wang

et al. 2022

IEEE Systems Journal

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“…Citation information: DOI 10.1109/ACCESS.2020.3043338, IEEE Access VOLUME XX, 2020 1 to the number of task requests in the scheduling interval [22]. In (12), s i N is the number of tasks successfully scheduled in the i-th scheduling interval, i N is the number of task requests in the i-th scheduling interval, and i ss R is the success rate of the i-th scheduling interval.…”

Section: Simulation Verification and Analysis A Evaluation Indexmentioning

confidence: 99%

“…Reference [11] pointed out that in the process of multitarget tracking, there is a linear relationship between the emission energy of the radar and the dwell time, and the nonconvex optimization problem of energy and time distribution can be transformed into a standard convex optimization problem. Reference [12] studied the resource management problem of multiple-input and multiple-output (MIMO) radar for multitarget tracking and pointed out that the optimal sampling period, transmission power, and subarray selection are all resource allocations in the time domain, which reduces the number of variables for problem solving. Reference [13] is based on the pulse interleaving scheduling method by setting the objective function and constraint conditions, using a hybrid particle swarm genetic algorithm to solve the optimal scheduling plan and successfully transforms the radar scheduling problem into an optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Phased-Array Radar Task Scheduling Method for Hypersonic-Glide Vehicles

Meng¹,

Tian²

2020

IEEE Access

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“…As a new radar system recently proposed, the FDA-MIMO radar has great advantages in parameter estimation, especially in target angle and range estimation [9]. According to the spatial distribution of radar antennas, there are two main types of the MIMO radar: statistical MIMO radar with widely separated antennas [10,11] and collocated MIMO radar with close spacing antennas [12,13]. The research object of this paper is monostatic MIMO radar, which belongs to collocated MIMO radar.…”

Section: Introductionmentioning

confidence: 99%

Tensor-Based Target Parameter Estimation Algorithm for FDA-MIMO Radar with Array Gain-Phase Error

Guo

Wang

Shi³

et al. 2022

Remote Sensing

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As a new radar system, FDA-MIMO radar has recently developed rapidly, as it has broad prospects in angle-range estimation. Unfortunately, the performance of existing algorithms for FDA-MIMO radar is greatly degrading or even failing under the condition of array gain-phase error. This paper proposes an innovative solution to the joint angle and range estimation of FDA-MIMO radar under the condition of array gain-phase error and an estimation algorithm is developed. Moreover, the corresponding Cramér-Rao bound (CRB) is derived to evaluate the algorithm. The parallel factor (PARAFAC) decomposition technique can be utilized to calculate transmitter and receiver direction matrices. Taking advantage of receiver direction matrix, the angle estimation can be obtained. The range estimation can be estimated by transmitter direction matrix and angle estimation. To eliminate the error accumulation effect of array gain-phase error, the gain error and phase error are obtained separately. In this algorithm, the impact of gain-phase error on parameter estimation is removed and so is the error accumulation effect. Therefore, the proposed algorithm can provide excellent performance of angle-range and gain-phase error estimation. Numerical experiments prove the validity and advantages of the proposed method.

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Joint time-space resource allocation and waveform selection for the collocated MIMO radar in multiple targets tracking

Cited by 27 publications

References 19 publications

Joint Route Optimization and Multidimensional Resource Management Scheme for Airborne Radar Network in Target Tracking Application

Joint Route Optimization and Multidimensional Resource Management Scheme for Airborne Radar Network in Target Tracking Application

Phased-Array Radar Task Scheduling Method for Hypersonic-Glide Vehicles

Tensor-Based Target Parameter Estimation Algorithm for FDA-MIMO Radar with Array Gain-Phase Error

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