Target tracking with two passive infrared non-imaging sensors

Djurović, Željko; Kovačevíc, Branko; Dikić, Goran

doi:10.1049/iet-spr.2008.0068

Cited by 13 publications

(27 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is also a group of researches analyzing particular problems like counting people inside the office [9], tracking human movement in 2D [2] or in space [3]. Special apertures limiting Field of View to improve detection quality are analyzed in [3].…”

Section: Related Studiesmentioning

confidence: 99%

Smart passive infrared sensor - Hardware platform

Frankiewicz

Cupek

2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

View full text Add to dashboard Cite

Passive Infrared Sensors (PIR) are inexpensive devices widely used as motion detectors. Standard sensor provides just trigger signal on movement detection. This paper aims to extend standard usage of the sensor from motion detection to motion recognition and activity classification. In applications where motion detection based on video surveillance is not possible due regulations or high costs smart PIR sensor could be the only solution. This publication starts the topic of PIR based motion classification with analyses of hardware requirements and hardware selection for the smart sensor.

show abstract

Section: Related Studiesmentioning

confidence: 99%

Smart passive infrared sensor - Hardware platform

Frankiewicz

Cupek

2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

View full text Add to dashboard Cite

show abstract

“…with n k describing the background noise irradiance as in Ref. 7. This noise term appears because the target is viewed against a background which also emits IR energy, e.g., clouds or trees.…”

Section: Target Measurement Modelsmentioning

confidence: 99%

“…(9) and 10is one of them. 7 The position of the target calculated from the baseline length and angle information is used in this paper to initialize the triangulation process. In order to estimate the target position, angles from both sensors separated by a fixed baseline are augmented together and the estimation process is accomplished using the EKF.…”

Section: Ranging Systemsmentioning

confidence: 99%

“…Considerable research and development have been done to develop algorithms for tracking and position estimation based on radar with laser illumination or infrared (IR) sensors. 3,4 However, standalone use of multiple IR sensors not only proves to be useful in target tracking and estimation [5][6][7][8] but also has an added benefit of stealth due to the passive nature.…”

Section: Introductionmentioning

confidence: 99%

“…While effective with large baseline and sensor-target distances, the technique presented in Ref. 7 is not suitable for use in AVP systems due to short-range high velocity incoming threats and limitations on the short baseline distance between sensors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Active vehicle protection using angle and time-to-go information from high-resolution infrared sensors

2015

View full text Add to dashboard Cite

An algorithm for an active protection system with two or more passive infrared sensors based on the principle of triangulation and an extended measurement vector consisting of time-to-go (TTG) estimation is proposed to address high-speed short-range threats. The proposed algorithm exploits the full potential of passive sensors enabling the use of target irradiance in a manner to estimate the TTG along with angle information. The approach here uses target irradiance measured at successive scans to initialize a fixed interval iteration scheme for estimating the TTG. The Newton's method is used for estimating the TTG, the estimate is then added to the measurement vector and position estimation is performed using an extended Kalman filter. Computer simulations demonstrate the resulting improvement in estimation and feasibility for real-time application of the proposed algorithm.

show abstract

Expectation‐maximization‐based infrared target tracking with time‐varying extinction coefficient identification

Liu

Liang

et al. 2020

Adaptive Control & Signal

View full text Add to dashboard Cite

Summary Extinction coefficient (EC), as the key parameter of target intensity model, is assumed constant in classical infrared target tracking (IRTT) methods. However, it is a time‐varying and state‐coupled parameter related to complex atmosphere environment. To this end, this article proposes the problem of IRTT with time‐varying EC identification. Different from the constant EC case whose solution is the measurement augmentation, the time‐varying EC case brings out the new challenge: deep coupling between state estimation and parameter identification. In the expectation‐maximization framework, this article derives the joint identification and estimation optimization scheme, where the Taylor expansion variance error of intensity model is also identified to adaptively compensate the nonlinear approximation. Simulation examples show that the proposed scheme has better estimation accuracy than the existing augmented extended Kalman filter.

show abstract

Target tracking with two passive infrared non-imaging sensors

Cited by 13 publications

References 3 publications

Smart passive infrared sensor - Hardware platform

Smart passive infrared sensor - Hardware platform

Active vehicle protection using angle and time-to-go information from high-resolution infrared sensors

Expectation‐maximization‐based infrared target tracking with time‐varying extinction coefficient identification

Contact Info

Product

Resources

About