Optical moving target detection with 3-D matched filtering

Reed, I. S.; Gagliardi, R.; Stotts, Larry B.

doi:10.1109/7.7174

Cited by 233 publications

(111 citation statements)

References 10 publications

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“…It is generally considered to be an optimal detection method in idealized environments with known noise characterization and a priori knowledge of the spatial-temporal signal behavior. Published variants and enhancements of this general approach have been reported in the literature by Kelly (1986), Barniv (1985), Barniv & Kella (1987), Lampropoulos & Boulter (1987), Reed et al (1988), Pohlig (1989Pohlig ( , 1992, Chen (1989), Porat & Friedlander (1990), Auerbach et al (1996), Ralston et al (1996), Watson & Watson (1997), and Sanders- Reed (1998). The basic methodology involves a two-step algorithm in which the first stage performs mean removal and background clutter suppression, followed by a second stage incorporating an adaptive MF and detector to enhance the signal component.…”

Section: Matched Filter Formulationmentioning

confidence: 99%

Matched Filter Processing for Asteroid Detection

Gural¹,

Larsen²,

Gleason³

2005

Astron J

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Matched filter (MF) processing has been shown to provide significant performance gains when processing stellar imagery used for asteroid detection, recovery, and tracking. This includes extending detection ranges to fainter magnitudes at the noise limit of the imagery and operating in dense cluttered star fields as encountered at low Galactic latitudes. The MF software has been shown to detect 40% more asteroids in high-quality Spacewatch imagery relative to the currently implemented approaches, which are based on moving target indicator (MTI) algorithms. In addition, MF detections were made in dense star fields and in situations in which the asteroid was collocated with a star in an image frame, cases in which the MTI algorithms failed. Thus, using legacy sensors and optics, improved detection sensitivity is achievable by simply upgrading the image-processing stream. This in turn permits surveys of the nearEarth asteroid (NEA) population farther from opposition, for smaller sizes, and in directions previously inaccessible to current NEA search programs. A software package has been developed and made available on the NASA data services Web site that can be used for asteroid detection and recovery operations utilizing the enhanced performance capabilities of MF processing.

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Section: Matched Filter Formulationmentioning

confidence: 99%

Matched Filter Processing for Asteroid Detection

Gural¹,

Larsen²,

Gleason³

2005

Astron J

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“…A localization technique using 3-D matched filtering (3-DMF) has been developed by Reed [165][166][167] for detecting multiple weak, moving targets in clutter. Such methods synthesize the 2-D matched filter (2-DMF) and the 1-D time energy, where the 2-DMF is used to depress the clutter, and the 1-D of time energy is cumulated based on the velocity feature.…”

Section: ) 3-d Matched Filtersmentioning

confidence: 99%

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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Abstract-Multitarget tracking (MTT) in surveillance system is extremely challenging, due to uncertain data association, maneuverable target motion, dense clutter disturbance, and real-time processing requirements. A good many methods have been proposed to cope with these challenges. However, no up-to-date survey is available in the literature that can help to select suitable tracking algorithm for practical problem. This paper provides a comprehensive review of the state-of-the-art motion-based MTT techniques, classifies existing methods into two groups, i.e., the detect-beforetrack (DBT) scheme and the track-before-detect (TBD) scheme. The DBT scheme is employed to achieve robust and tractable tracking performance when the signal-noise-ratio (SNR) is strong. The TBD scheme is used in the scenarios of low SNR, and it aims to cumulate target energy by multiple sensor frames. Furthermore, depending on the data association mechanism, the DBT methods can be classified into two categories, data association based approaches and finite set statistics (FISST) based approaches. And the TBD methods can be classified into non-Bayesian approaches and Bayesian approaches depending on the basis theory used for tracking. For each category, this paper provides the detailed descriptions of the representative algorithms, and examines their pros and cons. Finally, new trends for future research directions are offered.

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“…7 shows some samples used in this experiment. Here, we select two classic target detection algorithms: Victor algorithm [27] and Reed algorithm [28].…”

Section: Experiments For Pftn and Brimentioning

confidence: 99%

Metrics for Performance Evaluation of Preprocessing Algorithms in Infrared Small Target Images

Diao¹,

Mao²,

Gui³

2011

PIER

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Abstract-Image preprocessing is commonly used in infrared (IR) small target detection to suppress background clutter and enhance target signature. To evaluate the performance of preprocessing algorithms, two performance metrics, namely PFTN (potential false targets number) decline ratio and BRI (background relative intensity) decline ratio are developed in this paper. The proposed metrics evaluate the performance of given preprocessing algorithm by comparing the qualities of input and output images. The new performance metrics are based on the theories of PFTN and BRI, which describe the quality of IR small target image, by representing the difficulty degree of target detection. Theoretical analysis and experimental results show that the proposed performance metrics can accurately reflect the effect of the image preprocessing stage on reducing false alarms and target shielding. Compared to the traditional metrics, such as signal-to-noise ratio gain and background suppression factor, the new ones are more intuitive and valid.

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Optical moving target detection with 3-D matched filtering

Cited by 233 publications

References 10 publications

Matched Filter Processing for Asteroid Detection

Matched Filter Processing for Asteroid Detection

A Survey of Motion-Based Multitarget Tracking Methods

Metrics for Performance Evaluation of Preprocessing Algorithms in Infrared Small Target Images

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