Advanced cognitive networked radar surveillance

Jahangir, Mohammed; Baker, Chris; Antoniou, Michail; Griffin, Benjamin; Balleri, Alessio; Money, David; Harman, Stephen

doi:10.1109/radarconf2147009.2021.9455245

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…In addition, multistatic cognitive radar networks have been studied [25], where each radar in a network is able to receive and process the pulses transmitted by the other nodes. Multistatic radar operation allows for greater tracking accuracy, at a cost of greater amounts of coordination and processing.…”

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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“…Raw I/Q data can be recorded for all channels to build a vast database of target signatures in realistic clutter conditions (Figure 7& [10]) which is furthering work on machine learning classifier [11] and drone signature modelling [12]. The network configuration will provide a vital facility for multi-static measurements [13]. In due course, both radars will be connected to ultra-low phase noise, ultra-fine stability, Quantum oscillators to establish both the performance limits of monostatic radar in strong clutter and the coherence in the radar network [14].…”

Section: Figure 3 Qinetiq Ribi Hardware and Flown On <20kg Quadcoptermentioning

confidence: 99%

Try living in the real world: the importance of experimental radar systems and data collection trials

Greig¹,

Clemente²,

Antoniou³

et al. 2023

IET Conf. Proc.

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While simulations of increasingly high fidelity are an important tool in radar science, experimentation is still needed as a source of validation for simulation, to explore complex phenomena which cannot be accurately simulated and ultimately in turning theory and simulation into a real world system with real world applications. Experimental systems can range from laboratory based, installations on the ground with limited fields of view all the way up to flying demonstrators which may be prototypes for radar products. In this paper we will discuss the importance of experimentation in the development of radar science and radar products with examples of systems used by a sub-set of the members of the UK EMSIG.

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