Receive-Beam Resource Allocation for Multiple Target Tracking With Distributed MIMO Radars

Xie, Mingchi; Yi, Wei; Kong, Lingjiang; Kirubarajan, T.

doi:10.1109/taes.2018.2818579

Cited by 81 publications

(14 citation statements)

References 43 publications

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“…where a ø 1 is the relaxation factor, and recall that 15) is the same as equation ( 14). Next, we adapt the Saturation algorithm in Krause et al 23 to solve equation (15), and propose a subset selection algorithm for transmitters as in Algorithm 1.…”

Section: A Balanced Subset Selection Policymentioning

confidence: 99%

“…30 To ensure an improved convergence of particles, we adopt the binary PSO algorithm in Nezamabadi-pour et al 31 to Algorithm 1. Algorithm pseudo code for equation (15). (14).…”

Section: Numerical Simulation and Analysismentioning

confidence: 99%

“…A two-step semidefinite programming-based approach was proposed to derive an approximate solution. Xie et al 15 considered the problem of receive-beam allocation when an MIMO radar works on ''defocused transmit-focused receive'' mode and proposed a convex relaxation-based approximate algorithm to solve the problem. Zhang et al 16 considered the problem of joint subarray selection and power allocation for multitarget tracking with clutter in an MIMO radar, where the weighted sum of predicted conditional CRLB was used as the optimization criterion.…”

Section: Introductionmentioning

confidence: 99%

“…Yuan et al 19 considered the power allocation problem in colocated MIMO radar systems based on the quality of service framework and proposed an iterative parallel search algorithm. While extensive work on selection of transmitters/receivers for an MIMO radar has been done, 6,7,[12][13][14][15][16][17][18][19] the properties of the objective functions optimized were seldom considered in their work. The algorithms developed there were shown being effective as demonstrated by simulations but with no performance guarantee.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A balanced sensor scheduling for multitarget localization in a distributed multiple-input multiple-output radar network

Wang¹,

Wang

Xu³

et al. 2021

International Journal of Distributed Sensor Networks

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In this article, we consider the problem of optimally selecting a subset of transmitters from a transmitter set available to a multiple-input and multiple-output radar network. The aim is to minimize the location estimation error of underlying targets under a power constraint. We formulate it as a minimum-variance estimation problem and show that the underlying variance reduction function is submodular. From the properties of submodularity, we present a balanced selection policy which minimizes the worst-case error value using a minimax strategy. A greedy algorithm with guaranteed performance with respect to optimal solutions is given to efficiently implement the scheduling policy. The effectiveness and the efficiency of the proposed algorithm are demonstrated in simulated examples.

show abstract

Section: A Balanced Subset Selection Policymentioning

confidence: 99%

“…30 To ensure an improved convergence of particles, we adopt the binary PSO algorithm in Nezamabadi-pour et al 31 to Algorithm 1. Algorithm pseudo code for equation (15). (14).…”

Section: Numerical Simulation and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A balanced sensor scheduling for multitarget localization in a distributed multiple-input multiple-output radar network

Wang¹,

Wang

Xu³

et al. 2021

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…Recently, radar network systems, such as multiple-input multiple-output (MIMO) radar, have attracted great attention from academic researchers [1][2][3][4][5]. It has been shown that a radar network system has numerous potential advantages over traditional monostatic and bistatic radar, such as waveform diversity [1], multiplexing gain [2], enhanced target tracking, localization performance [6,7], etc.…”

Section: Introductionmentioning

confidence: 99%

Joint Dwell Time and Bandwidth Optimization for Multi-Target Tracking in Radar Network Based on Low Probability of Intercept

Ding

Shi

Qiu

et al. 2020

Sensors

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Radar network systems have been demonstrated to offer numerous advantages for target tracking. In this paper, a low probability of intercept (LPI)-based joint dwell time and bandwidth optimization strategy is proposed for multi-target tracking in a radar network. Since the Bayesian Cramer–Rao lower bound (BCRLB) provides a lower bound on parameter estimation, it can be utilized as the accuracy metric for target tracking. In this strategy, in order to improve the LPI performance of the radar network, the total dwell time consumption of the underlying system is minimized, while guaranteeing a predetermined tracking accuracy. There are two adaptable parameters in the optimization problem: one for dwell time, and the other for bandwidth allocation. Since the nonlinear programming-based genetic algorithm (NPGA) can solve the nonlinear problem well, we develop a method based upon NPGA to solve the resulting problem. The simulation results demonstrate that the proposed strategy has superiority over traditional algorithms, and can achieve a better LPI performance of this radar network.

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Control and utilization of range-dependent beampattern with waveform diverse array radars

Lan

Liao

et al. 2022

Chinese Journal of Aeronautics

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Receive-Beam Resource Allocation for Multiple Target Tracking With Distributed MIMO Radars

Cited by 81 publications

References 43 publications

A balanced sensor scheduling for multitarget localization in a distributed multiple-input multiple-output radar network

A balanced sensor scheduling for multitarget localization in a distributed multiple-input multiple-output radar network

Joint Dwell Time and Bandwidth Optimization for Multi-Target Tracking in Radar Network Based on Low Probability of Intercept

Control and utilization of range-dependent beampattern with waveform diverse array radars

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