Cognitive Dwell Time Allocation for Distributed Radar Sensor Networks Tracking via Cone Programming

Liu, Xinghua; Xu, Zhiwei; Wang, Luoshengbin; Dong, Wei

doi:10.1109/jsen.2020.2970280

Cited by 16 publications

(3 citation statements)

References 29 publications

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“…Several works focus on power and dwell time allocation in CRNs [21], [22], [23]. These works consider CRNs sharing a single channel, which must allocate the limited observation time to the nodes of the networks.…”

Section: Organizationmentioning

confidence: 99%

Hybrid Cognition for Target Tracking in Cognitive Radar Networks

Howard¹,

Buehrer²

2023

Preprint

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This work investigates online learning techniques for a cognitive radar network utilizing feedback from a central coordinator. The available spectrum is divided into channels, and each radar node must transmit in one channel per time step. The network attempts to optimize radar tracking accuracy by learning the optimal channel selection for spectrum sharing and radar performance. We define optimal selection for such a network in relation to the radar observation quality obtainable in a given channel. This is a difficult problem since the network must seek the optimal assignment from nodes to channels, rather than just seek the best overall channel. Since the presence of primary users appears as interference, the approach also improves spectrum sharing performance. In other words, maximizing radar performance also minimizes interference to primary users. Each node is able to learn the quality of several available channels through repeated sensing. We model the independent radar nodes as cognitive agents as well as the central coordinator and assume that information can be exchanged between the radars and the coordinator. Importantly, each part of the network acts as an online learner, observing the environment to inform future actions. We show that in interference-limited spectrum, where the signal-to-interference-plus-noise ratio can vary across channels and nodes, a cognitive radar network is able to use information from the central coordinator in order to reduce the amount of time necessary to learn the optimal channel selection. We also show that even limited use of a central coordinator can eliminate collisions, which occur when two nodes select the same channel. We provide several reward functions which capture different aspects of the dynamic radar scenario and describe the online machine learning algorithms which are applicable to this structure. In addition, we study varying levels of feedback, where central coordinator update rates vary. We compare our algorithms against baselines and demonstrate dramatic improvements in convergence time over the prior art. A network using hybrid

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Section: Organizationmentioning

confidence: 99%

Hybrid Cognition for Target Tracking in Cognitive Radar Networks

Howard¹,

Buehrer²

2023

Preprint

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“…Due to increased maneuvering characteristics, electronic countermeasures and the complexity of electromagnetic environment [1], there are many problems such as strong maneuvering characteristics, high filtering, multiple interference and increasing problems due to inefficiencies in checking [2]. Single target tracking is no longer able to adapt to the growing tracking requirements [3], which can result in incomplete measurements from optoelectronic tracking systems [4]. This results in wild and missing values in the sensor measurements, which increases the difficulty of estimating the target state in the information [5].…”

Section: Introductionmentioning

confidence: 99%

A multi-sensor cooperative detection target tracking method based on radar-optical linkage control

Qian¹,

He²,

Li³

et al. 2023

J. meas. eng.

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Radar is a common means of tracking a target, and with active enemy interference, it often causes the target to lose its track, thus causing the radar to lose continuous tracking of the target. To improve the tracking effect, a multi-sensor cooperative detection target tracking method based on radar photoelectric linkage control was established. The study is based on radar photoelectric linkage, constant velocity (CV), constant acceleration (CA) and current statistical model (CSM) as the mathematical model of moving targets for this study. Improved interactive multi-model (IMM) and standard IMM were compared for targets in different motion states, as well as single sensor electronic support measures (ESM) and multi-sensor electronic support measures (ESM), infrared search and track (IRST). The research results show that in variable speed motion, the improved IMM algorithm and multiple sensors are used for target tracking. The azimuth and elevation tracking errors of the target are low, which can effectively solve the problem of model mismatch during the conversion of motion modes such as CV and CA. The azimuth and elevation image curves fluctuate smoothly, and have high stability. This method can achieve better tracking results.

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“…The time resource allocation is performed using kinematic estimation and prediction of target state parameters. Resource management solutions found in the literature mainly use the predicted states covariance matrix [3, 4], its expectation bound – Bayesian Cramer‐Rao lower bound (BCRLB) [5], the posterior CRLB (PCRLB) [6, 7] and predicted conditional CRLB (PC‐CRLB) [8].…”

Section: Introductionmentioning

confidence: 99%