Bayesian multiple target tracking, 2nd edition [Book review]

Stone, Larry; Streit, Roy L.; Corwin, Tom; Bell, Kristine L.; Daum, Fred

doi:10.1109/maes.2014.140049

Cited by 47 publications

(59 citation statements)

References 9 publications

(9 reference statements)

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“…This principle is a specific version of the conditionality principle which states that inference should be solely based upon those events that actually occurred in the experiment [11]. Thus, only the observable data collected by the observer about the position of the vehicle should be used in the development of the inference as to its future position.…”

Section: Likelihood Principlementioning

confidence: 99%

“…Thus, only the observable data collected by the observer about the position of the vehicle should be used in the development of the inference as to its future position. According to Stone, the likelihood principle is only slightly more specific in that the principle holds that the likelihood function evaluated for all the observations is the entire picture of the information [11]. The likelihood function is therefore used to describe the observations of the position of the vehicle and is relied upon as the sole source of information for the prediction of the future position of the vehicle.…”

Section: Likelihood Principlementioning

confidence: 99%

“…Although it bears his name, the interpretation of Bayes' theorem used in this paper was actually developed by the work of Laplace [11]. The theorem describes the method by which previous data can be resolved with current data to produce a future prediction.…”

Section: Bayes' Theoremmentioning

confidence: 99%

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Randomized path optimization for the mitigated counter-detection of UAVs

Heaton

DeVries²,

Kutzer³

2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Washington The views expressed in this document are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. IRB Protocol Number: N/A. 12a. DISTRIBUTION / AVAILABILITY STATEMENTApproved for public release. Distribution is unlimited.12b. DISTRIBUTION CODE ABSTRACT (maximum 200 words)UAVs provide exceptional capabilities and a myriad of potential mission sets, but the ability to disguise where the aircraft takes off and lands would expansively advance the abilities of UAVs. This thesis describes the development of a nonlinear estimation algorithm to predict the terminal location of an aircraft and a trajectory optimization strategy to mitigate the algorithm's success. Vehicle paths are generated using a matrix-based quadratic trajectory computation method. The paths are then tracked by recursively updating time-based observations of vehicle position using Bayesian filtering. The KL divergence is used to compare the probability density of aircraft termination to a normal distribution around the true terminal location. Results show that the optimal conditions to obfuscate path include waypoints at or beyond the vehicle terminal location, variations in velocity throughout the time of flight, and the minimal use of an aircraft's maximum potential time of flight. ABSTRACTUAVs provide exceptional capabilities and a myriad of potential mission sets, but the ability to disguise where the aircraft takes off and lands would expansively advance the abilities of UAVs. This thesis describes the development of a nonlinear estimation algorithm to predict the terminal location of an aircraft and a trajectory optimization strategy to mitigate the algorithm's success. Vehicle paths are generated using a matrix-based quadratic trajectory computation method. The paths are then tracked by recursively updating timebased observations of vehicle position using Bayesian filtering. The KL divergence is used to compare the probability density of aircraft termination to a normal distribution around the true terminal location. Results show that the optimal conditions to obfuscate path include waypoints at or beyond the vehicle terminal location, variations in velocity throughout the time of flight, and the minimal use of an aircraft's maximum potential time of flight.

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Section: Likelihood Principlementioning

confidence: 99%

Section: Likelihood Principlementioning

confidence: 99%

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Randomized path optimization for the mitigated counter-detection of UAVs

Heaton

DeVries²,

Kutzer³

2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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“…For application in aerospace surveillance, target detection and tracking are thus performed in the backend. Target tracking algorithms, including the Kalman filter and its variants, predict future target speeds and positions by using Bayesian estimation [3], whose computational requirements vary according to the format and content of input data. Accordingly, detection and tracking functions require processors to be more capable of logic and data manipulation as well as complex program flow control.…”

Section: Real-time Large-scale Phased Array Radar Systemsmentioning

confidence: 99%

An Implementation of Real-Time Phased Array Radar Fundamental Functions on DSP-Focused, High-Performance Embedded Computing Platform

Zhang

Patel

et al. 2016

Preprint

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This paper investigates the feasibility of a backend design for real-time, multiple-channel processing digital phased array system, particularly for high-performance embedded computing platforms constructed of using general purpose digital signal processors. Frist, we obtained the labscale backend performance benchmark from simulating beamforming, pulse compression, and Doppler filtering based on MicroTCA chassis using Serial RapidIO protocol in backplane communication. Next, a field-scale demonstrator of a multifunctional phased array radar is emulated by using the similar configuration. Interestingly, the performance of a barebone design is compared to that of emerging tools that systematically take advantage of parallelism and multicore capabilities, including Open Computing Language.

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“…Probabilistic grids apply the Bayesian data fusion technique to solve mapping (Elfes, 1987) and tracking (Stone et al, 1999) problems. Practically, a grid of likelihoods on the states x ij is produced in the form of ( | ) ( )…”

Section: Probabilistic Gridsmentioning

confidence: 99%

Factory Sensors and RFID Networks

Nof

Ceroni

Jeong

et al. 2015

Revolutionizing Collaboration Through E-Work, E-Business, and E-Service

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Abstract. In line with the emergence of decentralized e-Systems and the everincreasing needs for effective interaction and collaboration, a rising challenge is how to enable communication and gather information on the real-time state of distributed agents in an e-Work network. That is, each agent (e.g., machine agent; resource agent; product agent), as an autonomous and intelligent element of a collaborative e-System, must be able to detect and process useful information from its environment and share it with other peers. The supporting technologies for this critical need are sensor and RFID (Radio Frequency Identification) networks. In this chapter, fundamentals of each technology are discussed and some real-life applications are reviewed. SensorsA sensor is an instrument that responds to a specific physical stimulus and produces a measurable electrical signal. Sensors have different types ranging from mechanical to electrical, electromechanical, magnetic, optical, chemical or even biological. Indeed, any device that is directly altered in a predictable and measurable manner by changes in a real-life parameter is known as a sensor for that parameter. Sensors play a significant role in any e-System due to the need to gather, process, and share information relevant to specific tasks. Recent advances in micro-device technology, micro-fabrication, chemical processes, and digital signal processing have enabled the development of economically affordable micro-/nano-sensors. Micro-sensors have a variety of applications in many practical areas such as medical and space devices, military equipment, telecommunication, and manufacturing (Luo, 1996;Luo et al., 2002). Compared to conventional sensors, micro-sensors provide some unique advantages such as interfering less with the environment they measure, requiring less manufacturing cost, being used in narrow spaces and harsh environments, etc. Successful design and implementation of micro-sensors depends highly on sensors' capability, cost, and reliability. Sensing PrinciplesAccording to their working principles, sensors are technically classified into different types (Table 8.1). Sensors may measure physical phenomena by capturing resistance change, capacitance change, inductance change, thermoelectric effect, piezoelectric effect, photoelectric effect, Hall effect, and so on (Wilson, 2004;Fraden, 2003). Among these effects, majority of sensors utilize the resistance change or resistivity of a conductor. Considering uniform current density in the insulator, the resistance R of a conductor of cross-sectional area A can be computed as , ρ ⋅ = l R A ( 8 . 1 ) where l denotes the length of the conductor, and ρ is the electrical resistivity of the material. Resistivity is a measure of the material's ability to oppose electric current. Therefore, the change of resistance can be measured by detecting physical deformation (l or A) of conductive materials or by sensing resistivity (ρ) of conductor. A strain gage, for example, is a sensor that measures resistance by deformation ...

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Bayesian multiple target tracking, 2nd edition [Book review]

Cited by 47 publications

References 9 publications

Randomized path optimization for the mitigated counter-detection of UAVs

Randomized path optimization for the mitigated counter-detection of UAVs

An Implementation of Real-Time Phased Array Radar Fundamental Functions on DSP-Focused, High-Performance Embedded Computing Platform

Factory Sensors and RFID Networks

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