Sidelobe Control in Collaborative Beamforming via Node Selection

Ahmed, Mohammed; Vorobyov, Sergiy A.

doi:10.1109/tsp.2010.2077631

Cited by 59 publications

(58 citation statements)

References 28 publications

(42 reference statements)

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“…All sensor nodes within a cluster have access to the data/measurements to be transmitted to the UAV. This information sharing is simply performed by broadcasting the data from one sensor node to all other nodes in its cluster [5], [13], [14]. There is a line-of-sight (LOS) between the sensor nodes and the UAV and the environment does not have any obstacles.…”

Section: Introductionmentioning

confidence: 99%

WSN-UAV Monitoring System with Collaborative Beamforming and ADS-B Based Multilateration

Nijsure¹,

Ahmed²,

Kaddoum³

et al. 2016

2016 IEEE 83rd Vehicular Technology Conference (VTC Spring)

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Abstract-This paper presents wireless sensor networkunmanned aerial vehicle (WSN-UAV) system for military remote monitoring and surveillance. Large scale WSN is deployed in a battlefield or wide hostile region to collect information of interest and send it to a UAV. Collaborative beamforming (CB) is used to achieve the ground-to-air transmissions. An automatic dependent surveillance-broadcast (ADS-B) based multilateration is used to obtain the UAV location and tracking information. It is found that a minimum distance between the UAV and the WSN is required for proper operation of the CB due to the precision of the multilateration and the movement of the UAV.Index Terms-Monitoring and surveillance, Geolocation, ADS-B based multilateration, Large scale wireless sensor networks, Collaborative beamforming. I. INTRODUCTIONWireless sensor networks (WSNs) is an attractive tool for monitoring and surveillance applications due to its flexible deployment and ability of unattended operation [1], [2]. Sensor nodes are deployed in an ad-hoc fashion over large and remote area to collect information from the surrounding neighbourhood and send it to a fusion center (FC) for processing and decision making. In order to have reasonable cost, sensor nodes are fabricated with off-the-shelf microcontrollers and, consequently, have limited processing capability. Moreover, energy is typically supplied in sensor nodes from batteries or energy harvesting devices [3].Sensor nodes are deployed close to the event or phenomena under surveillance which is typically far from the FC. Data gathering can be implemented using unmanned aerial vehicle (UAV) as a FC. The UAV flies over the sensing field while sensor nodes, which are deployed on the ground, transmit the collected data directly to the UAV. UAVs have the advantage to explore vast geographical region.In this paper, we focus on military tactical monitoring and surveillance to remotely monitor the battlefield or wide hostile regions [4]. In such scenarios, a UAV is utilized as the FC which is expected to fly at high altitudes. The distances between the sensor nodes and the UAV can be larger than the transmission range of individual sensor node. In this case, collaborative beamforming (CB) [5], [6] is utilized for ground-to-air transmission in the proposed system to increase the transmission range and achieve directional gain. In order to implement CB, the location and tracking information of the UAV should be available at the sensor nodes. Typically, global positioning system (GPS) can be used to obtain the UAV location information and broadcast it to the sensor nodes. However, we are interested in military and tactical applications where there is a threat of GPS jamming, spoofing and message infringement type attacks. In this case, other navigation and guidance subsystems can be used to obtain this information [4]. Recently, automatic dependent surveillancebroadcast (ADS-B) has been introduced to support avionics navigation. The ADS-B signal can carry location information relative to loca...

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Section: Introductionmentioning

confidence: 99%

WSN-UAV Monitoring System with Collaborative Beamforming and ADS-B Based Multilateration

Nijsure¹,

Ahmed²,

Kaddoum³

et al. 2016

2016 IEEE 83rd Vehicular Technology Conference (VTC Spring)

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“…A node selection algorithm to control the sidelobe in CB has been proposed in [20] but the proposed algorithm is feasible only when large number of nodes is available for collaboration. A collaborative null steering beamformer is presented in [21].…”

Section: Introductionmentioning

confidence: 99%

PSOGSA-Explore: A new hybrid metaheuristic approach for beampattern optimization in collaborative beamforming

Jayaprakasam

Rahim

Leow

2015

Applied Soft Computing

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“…Although it has been proved that the mainlobe of the beampattern in distributed beamforming is not affected the location of the individual sensor nodes, the sidelobes' position and magnitude vary according to the sensor nodes' locations [6,[9][10]. Moreover, in contrast to the conventional uniform array, the secondary maxima of the array factor is not necessarily the highest peak sidelobe (PSL) in distributed beamforming [11].…”

Section: Introductionmentioning

confidence: 99%

Beampatten optimization in distributed beamforming using multiobjective and metaheuristic method

Jayaprakasam

Rahim

Leow

et al. 2014

2014 IEEE Symposium on Wireless Technology and Applications (ISWTA)

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Distributed beamforming is a communication method in wireless sensor networks (WSNs) where the sensor nodes collaboratively create a virtual antenna to direct their radiating power towards the direction of an intended destination. This method could increase the transmission range of the network and save the sensors' energy. However, due to the random locations of the sensor nodes, the beampattern for a finite number of nodes usually has asymmetrical sidelobes with high sidelobe levels. Higher sidelobe levels cause undesirable interferences at directions other than the intended destination. Conventional sidelobe reduction methods proposed for centralized antenna array cannot be used for distributed beamforming networks. This paper proposes a distributed network compliant, multi-objective weight optimization technique to produce a beampattern with lower sidelobe levels, higher directivity and minimal energy. Exhaustive search for the most favorable weight solutions is time-consuming when the number of sensor nodes is large. Therefore, this paper analyses the use of nature-inspired metaheuristic algorithms to solve for the best weight values at each sensor node. Three algorithms were analysed, namely, genetic algorithm (GA), particle swarm optimization (PSO) and gravitational search algorithm (GSA). Simulation results show that the proposed multi-objective weight optimization using nature inspired algorithm can provide improved beampattern with lower sidelobes, higher directivity and better energy savings.

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Sidelobe Control in Collaborative Beamforming via Node Selection

Cited by 59 publications

References 28 publications

WSN-UAV Monitoring System with Collaborative Beamforming and ADS-B Based Multilateration

WSN-UAV Monitoring System with Collaborative Beamforming and ADS-B Based Multilateration

PSOGSA-Explore: A new hybrid metaheuristic approach for beampattern optimization in collaborative beamforming

Beampatten optimization in distributed beamforming using multiobjective and metaheuristic method

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