Browsing by Author "Griffin, Benjamin"

Now showing 1 - 5 of 5
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    Advanced cognitive networked radar surveillance
    (IEEE, 2021-06-18) Jahangir, Mohammed; Baker, Chris J.; Antoniou, Michail; Griffin, Benjamin; Balleri, Alessio; Money, David; Harman, Stephen
    The concept of a traditional monostatic radar with co-located transmit and receive antennas naturally imposes performance limits that can adversely impact applications. Using a multiplicity of transmit and receive antennas and exploiting spatial diversity provides additional degrees of design freedom that can help overcome such limitations. Further, when coupled with cognitive signal processing, such advanced systems offer significant improvement in performance over their monostatic counterparts. This will also likely lead to new applications for radar sensing. In this paper we explore the fundamentals of multistatic network radar highlighting both potential and constraints whilst identifying future research needs and applications. Initial experimental results are presented for a 2-node networked staring radar.
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    Development of a passive dual channel receiver at L-band for the detection of drones
    (IEEE, 2022-06-02) Griffin, Benjamin; Balleri, Alessio; Baker, Chris; Jahangir, Mohammed; Harman, Stephen
    Staring radars use a transmitting static wide-beam antenna and a directive digital array to form multiple simultaneous beams on receive. Because beams are fixed, the radar can employ long integration times to detect slow low-RCS targets, such as drones, which present a challenge to traditional air surveillance radar. The use of multiple spatially separated receivers cooperating with the staring transmitters in a multistatic network allows multi-perspective target acquisitions that can help mitigate interference and ultimately enhance the detection of drones and reduce estimation errors. Here, the development and experimental results of a passive, dual-channel, L-band receiver are presented. The receiver has been used to take measurements of both moving vehicles of drones in flight using a bistatic staring transmitter. An analysis of the receiver is presented using GPS is used to quantify the estimation performance of the receiver.
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    Multistatic dual-channel detection of drones: effects of PNT errors
    (IEEE, 2023-12-28) Griffin, Benjamin; Balleri, Alessio; Catherall, Aled
    A radar network solution to detect drones is presented that consists of low-cost low-size dual-channel moving receivers, which can be deployed on UASs and operate within the coverage of an existing cooperative or non-cooperative monostatic staring radar. The receivers exploit the use of a dual-channel design and therefore use a reference and a surveillance channel to operate coherently without the requirement of a shared synchronisation reference signal between the network nodes, which is one of the key limitations of other traditional multistatic radar network solutions. Drone detection and parameter estimation is achieved by fusing the information collected at the network receivers and rely on accurate Position, Navigation and Timing (PNT) information. In this paper, we investigate the effects of PNT errors on estimation performance for such a radar network.
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    Optimal receiver placement in staring cooperative radar networks for detection of drones
    (IEEE, 2020-12-04) Griffin, Benjamin; Balleri, Alessio; Baker, Chris; Jahangir, Mohammed
    Staring radars use a transmitting static wide-beam antenna and a directive digital array to form multiple simultaneous beams on receive. Because beams are static, the radar can employ long integration times that facilitate the detection of slow low-RCS targets, such as drones, which present a challenge to traditional air surveillance radar. Typical low altitude trajectories employed by drones often result in low-grazing angle multipath effects which are difficult to mitigate with a monostatic radar alone. The use of multiple spatially separated receivers cooperating with the staring transmitters in a multistatic network allows multi-perspective target acquisitions that can help mitigate multipath and ultimately enhance the detection of drones. This paper investigates how varying the network geometry affects the estimation performance of a targets position and velocity in a multipath free scenario. The optimal geometry is found by minimising the trace of the Cramér-Rao Lower Bound (CRLB) of the Maximum Likelihood (ML) estimates of range and Doppler using the Coordinate Descent (CD) algorithm. The network estimation accuracy performance is verified using Monte Carlo simulations and an ML Estimator on the target parameter estimates.
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    Prototyping a dual-channel receiver for use in a staring cooperative radar network for the detection of drones
    (IEEE, 2021-07-02) Griffin, Benjamin; Balleri, Alessio; Baker, Chris; Jahangir, Mohammed
    Staring radars use a transmitting static wide-beam antenna and a directive digital array to form multiple simultaneous beams on receive. Because beams are fixed, the radar can employ long integration times to detect slow low-RCS targets, such as drones, which present a challenge to traditional air surveillance radar. The use of multiple spatially separated receivers cooperating with the staring transmitters in a multistatic network allows multi-perspective target acquisitions that can help mitigate interference, such as signal multipath, and ultimately enhance the detection of drones and reduce target parameter estimation errors. Here, the design of a dual-channel receiver prototype for use in a multistatic cooperative network is presented. Several measurements have been taken using the prototype receiver in a bistatic configuration to test and assess its performance.