Sensor Selection in Distributed Multiple-Radar Architectures for Localization: A Knapsack Problem Formulation

Godrich, Hana; Petropulu, Athina P.; Poor, H. Vincent

doi:10.1109/tsp.2011.2170170

Cited by 200 publications

(150 citation statements)

References 29 publications

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“…In Algorithm 2, the proposed MFMLS_OAT algorithm is presented to improve the resource utilization for problem (17). Similar to MFMLS algorithm, the MFMLS_OAT algorithm allows the radar system to ensure the demands of hot targets firstly and then improve the tracking accuracy for suspicious targets with the remaining resources.…”

Section: Mfmls_oat Algorithm For Low Location Estimation Msementioning

confidence: 99%

“…Some antennas may have greater impacts on system performance than others [16], affected by the different propagation paths, target reflection coefficients, and topologies of system with respect to targets. Reasonable antenna selection can maximize antenna utilization and minimize computational complexity and communication cost among stations.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with an exhaustive search, the existing heuristic algorithm can offer considerable reduction in computational complexity. Considering the data transmission and computational complexity, a single target localization scheme based on antenna selection is proposed in [17] where two optimization models are included. The first idea achieves the minimum antenna subset out of available transmitters and receivers within a given MSE threshold.…”

Section: Introductionmentioning

confidence: 99%

“…Antenna selection problem for moving targets is considered in this paper. Antenna utilization is ignored in [16]. For the improvement of the antenna utilization and the demands of other tasks, a constraint on tracking antenna utilization is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Resource Allocation Schemes for Multiple Targets Tracking in Distributed MIMO Radar Systems

Song

Zheng

Bai

2017

International Journal of Antennas and Propagation

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Considering the demands of different location accuracy for multiple targets tracking, performance-driven resource allocation schemes in distributed MIMO radar system are proposed. Restricted by the tracking antenna number, location estimation meansquare error (MSE), and target priorities, an optimization problem of the minimal antenna subsets selection is modeled as a knapsack problem. Then, two operational schemes, modified fair multistart local search (MFMLS) algorithm and modified fair multistart local search with one antenna to all targets (MFMLS_OAT) algorithm, are presented and evaluated. Simulation results indicate that the proposed MFMLS and MFMLS_OAT algorithm outperform the existing algorithms. Moreover, the MFMLS algorithm can distinguish targets with different priorities, while the MFMLS_OAT algorithm can perform the tracking tasks with higher accuracy.

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Section: Mfmls_oat Algorithm For Low Location Estimation Msementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Resource Allocation Schemes for Multiple Targets Tracking in Distributed MIMO Radar Systems

Song

Zheng

Bai

2017

International Journal of Antennas and Propagation

View full text Add to dashboard Cite

show abstract

“…Similar greedy techniques have been applied to radar arrays [17,18] where the change of the MMSE is used for target detection. It should also be noted that the utility proposed in our framework differs from other definitions such as [11,19,20] which rely on the concept of submodularity.…”

Section: Introductionmentioning

confidence: 99%

Greedy distributed node selection for node-specific signal estimation in wireless sensor networks

et al. 2014

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A wireless sensor network is envisaged that performs signal estimation by means of the distributed adaptive nodespecific signal estimation (DANSE) algorithm. This wireless sensor network has constraints such that only a subset of the nodes are used for the estimation of a signal. While an optimal node selection strategy is NP-hard due to its combinatorial nature, we propose a greedy procedure that can add or remove nodes in an iterative fashion until the constraints are satisfied based on their utility. With the proposed definition of utility, a centralized algorithm can efficiently compute each nodes's utility at hardly any additional computational cost. Unfortunately, in a distributed scenario this approach becomes intractable. However by using the convergence and optimality properties of the DANSE algorithm, it is shown that for node removal, each node can efficiently compute a utility upper bound such that the MMSE increase after removal will never exceed this value. In the case of node addition, each node can determine a utility lower bound such that the MMSE decrease will always exceed this value once added. The greedy node selection procedure can then use these upper and lower bounds to facilitate distributed node selection.

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Modified Cramér‐Rao lower bounds for joint position and velocity estimation of a Rician target in OFDM‐based passive radar networks

et al. 2017

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Owing to the increased deployment and the favorable range and Doppler resolutions, orthogonal frequency‐division multiplexing (OFDM)‐based L band digital aeronautical communication system type 1 (LDACS1) stations have become attractive systems for target surveillance in passive radar applications. This paper investigates the problem of joint parameter (position and velocity) estimation of a Rician target in OFDM‐based passive radar network systems with multichannel receivers placed on moving platforms, which are composed of multiple OFDM‐based LDACS1 transmitters of opportunity and multiple radar receivers. The modified Cramér‐Rao lower bounds (MCRLBs) on the Cartesian coordinates of target position and velocity are computed, where the received signal from the target is composed of dominant scatterer (DS) component and weak isotropic scatterers (WIS) component. Simulation results are provided to demonstrate that the target parameter estimation accuracy can be improved by exploiting the DS component. It also shows that the joint MCRLB is not only a function of the transmitted waveform parameters, target radar cross section, and signal‐to‐noise ratio but also a function of the relative geometry between the target and the passive radar networks. The analytical expressions of MCRLB can be utilized as a performance metric to access the target parameter estimation in OFDM‐based passive radar networks in that they enable the selection of optimal transmitter‐receiver pairs for target estimation.

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Sensor Selection in Distributed Multiple-Radar Architectures for Localization: A Knapsack Problem Formulation

Cited by 200 publications

References 29 publications

Resource Allocation Schemes for Multiple Targets Tracking in Distributed MIMO Radar Systems

Resource Allocation Schemes for Multiple Targets Tracking in Distributed MIMO Radar Systems

Greedy distributed node selection for node-specific signal estimation in wireless sensor networks

Modified Cramér‐Rao lower bounds for joint position and velocity estimation of a Rician target in OFDM‐based passive radar networks

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