Bayesian Tracking in Underwater Wireless Sensor Networks With Port-Starboard Ambiguity

Braca, Paolo; Willett, Peter; LePage, Kevin D.; Maranò, Stefano; Matta, Vincenzo

doi:10.1109/tsp.2014.2305640

Cited by 75 publications

(34 citation statements)

References 33 publications

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“…They are assumed to know their locations through GPS or other techniques. They are also locally time synchronized and have the ability to detect target's position (as in [24]). There is no sink in the sensor network.…”

Section: System Modelmentioning

confidence: 99%

Efficient Sleep Scheduling Algorithm for Target Tracking in Double-Storage Energy Harvesting Sensor Networks

Chen

Zeng

Zhao

2016

International Journal of Distributed Sensor Networks

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Target tracking is a typical application in wireless sensor networks. Both energy efficiency and tracking performance are important issues that need to be considered. They are a pair of contradictions most of the time. Saving energy often sacrifices tracking performance, while enhancing tracking performance needs to consume more energy. In this paper, an efficient sleep scheduling algorithm is put forward to tackle the above problem in energy harvesting sensor networks. At first, we modify the probabilitybased prediction and sleep scheduling (PPSS) algorithm to track the target and further use another sleep scheduling algorithm we proposed to wake tracking nodes when the target is likely to be missed (i.e., it is unsuccessful to wake next-moment tracking nodes). Secondly, a double-storage energy harvesting architecture is employed to increase residual energy of sensor nodes and to extend network lifetime. Simulation results reveal that the proposed sleep scheduling algorithm can improve tracking performance and prolong network lifetime compared with the PPSS algorithm and the proposed algorithm without energy harvesting.

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Section: System Modelmentioning

confidence: 99%

Efficient Sleep Scheduling Algorithm for Target Tracking in Double-Storage Energy Harvesting Sensor Networks

Chen

Zeng

Zhao

2016

International Journal of Distributed Sensor Networks

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“…In addition, in ASW systems based employing AUVs, receiving sensors have limited on board computational capabilities and therefore linear arrays with a conventional (rather than adaptive) beamformer are usually employed. In addition, horizontal line array receivers are cylindrically symmetric; they cannot discriminate if a detected echo comes from the port or from the starboard, i.e., they suffer from Port-Starboard (PS) ambiguity [13]- [15]. Thus ambiguous tracks may be produced by the on board tracker.…”

Section: Introductionmentioning

confidence: 99%

“…The nodes can also share locally collected information. For instance, solutions for the PS ambiguity in combination with the data association problem are provided in [13]- [15] where the assumption, that the target of interest is always assumed present, is made.…”

Section: Introductionmentioning

confidence: 99%

Distributed Information Fusion in Multistatic Sensor Networks for Underwater Surveillance

et al. 2016

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Surveillance in Anti-Submarine Warfare (ASW) has traditionally been carried out by means of submarines or frigates with towed arrays. These techniques are manpower intensive. Alternative approaches have recently been suggested using distributed stationary and mobile sensors, such as Autonomous Underwater Vehicles (AUVs). In contrast with the use of standard assets, these small, low-power and mobile devices have limited processing and wireless communication capabilities. However, when deployed in a spatially separated network, these sensors can form an intelligent network achieving high performance with significant features of scalability, robustness and reliability. The Distributed InFormation FUSION (DIFFUSION) strategy, in which the local information is shared among sensors, is one of the key aspects of this intelligent network.In this paper we propose two DIFFUSION schemes, in which the information shared among sensors consists of i) contacts, generated by the local detection stage; and ii) tracks generated by the local tracking stage. In the first DIFFUSION scheme contacts are combined at each nodes using the optimal Bayesian tracking based on the random finite set (RFS) formulation. In the second DIFFUSION scheme tracks are combined using the track-to-track association/fusion (T2T) procedure, then a sequential decision based on the association events is exploited. A full validation of the DIFFUSION schemes is conducted by the NATO Science and Technology Organization -Centre for Maritime Research and Experimentation during the sea trials Exercise Proud Manta 2012-2013 using real data. Performance metrics of DIFFUSION and of local tracking/detection strategies are also evaluated in terms of Time-on-Target (TOT) and False Alarm Rate (FAR).We demonstrate the benefit of using DIFFUSION against the local non-cooperative strategies. In particular DIFFUSION improves the level of TOT (FAR) with respect to the local tracking/detection strategies. Specifically, the TOT is increased over 90 − 95% while the FAR is reduced of two order of magnitude. The problem of communication failures, data not available from the collaborative AUV during certain periods of time, is also investigated. The robustness of DIFFUSION with respect to these communication failures is demonstrated, and the related performance results are reported here. Specifically, with 75% of communication failures the TOT is over 90 − 95% with a relatively small increase of the FAR with respect to the case of perfect communication.Index Terms-Collaborative data fusion, antisubmarine warfare, multistatic active sonar, target tracking, underwater sensor networks, autonomous underwater vehicles, real-world experimentation 1530-437X (c)

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“…There have been a number of other applications of Bayesian approaches throughout various industries. The application of Bayesian methods include estimation of storage reliability on pyrotechnic mechanical devices [8], underwater wireless sensor networks analysis with portstarboard ambiguity [9], single-cell differential expression analysis [10], and subway vibration power analysis on electromotive force [11]. However, to our knowledge, the application of a Bayesian method to the virtual metrology area has not been tried yet.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Virtual Metrology for Quality Inspection of Mobile Repeater Systems

Kim

Mun

et al. 2016

Management and Production Engineering Review

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Accepted: 28 July 2016The technology of wideband code division multiple access (WCDMA) has been applied to band selective interference cancellation system (ICS) repeaters. To inspect the telecommunication quality of the systems, quality engineers must check the shape of the signals at the corresponding frequency band of the repeaters. However, measuring the signal quality is a repetitive manual task which requires much inspection time and high costs. In the case of small-sized samples, such as the example of an ICS repeater system, Bayesian approaches have been employed to improve the estimation accuracy by incorporating prior information on the parameters of the model in consideration. This research proposes a virtual method of quality inspection for products using a correlation structure of measurement data, mainly in a Bayesian regression framework. The Bayesian regression model derives prior information from historical measurement data to predict measurements of other frequency bandwidths by exploiting the correlation structure of each measurement data. Empirical results show the potential for reducing inspection costs and time by predicting the values of adjoining frequency bandwidths through measured data of a frequency bandwidth in the course of quality inspections of ICS repeater systems.

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Bayesian Tracking in Underwater Wireless Sensor Networks With Port-Starboard Ambiguity

Cited by 75 publications

References 33 publications

Efficient Sleep Scheduling Algorithm for Target Tracking in Double-Storage Energy Harvesting Sensor Networks

Efficient Sleep Scheduling Algorithm for Target Tracking in Double-Storage Energy Harvesting Sensor Networks

Distributed Information Fusion in Multistatic Sensor Networks for Underwater Surveillance

A Bayesian Virtual Metrology for Quality Inspection of Mobile Repeater Systems

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