Ms. Yongli Yu Profile

Yongli Yu

at Duke Univ

SPIE Involvement:

Author

Publications (3)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 10 June 2005 Paper

Multi-modal iterative adaptive processing (MIAP) performance in the discrimination mode for landmine detection

Yongli Yu, Leslie Collins

Proceedings Volume 5794, (2005) https://doi.org/10.1117/12.603848

KEYWORDS: Sensors, Electromagnetic coupling, General packet radio service, Land mines, Detection and tracking algorithms, Mining, Sensor fusion, Target detection, Palladium, Data fusion

Read Abstract +

Due to the nature of landmine detection, a high detection probability (Pd) is required to avoid casualties and injuries. However, high Pd is often obtained at the price of extremely high false alarm rates. It is widely accepted that no single sensor technology has the ability to achieve the required detection rate while keeping acceptably low false alarm rates for all types of mines in all types of soil and with all types of false targets. Remarkable advances in sensor technology for landmine detection have made multi-sensor fusion an attractive alternative to single sensor detection techniques. Hence, multi-sensor fusion mine detection systems, which use complementary sensor technologies, are proposed. Previously we proposed a new multi-sensor fusion algorithm called Multi-modal Iterative Adaptive Processing (MIAP), which incorporates information from multiple sensors in an adaptive Bayesian decision framework and the identification capabilities of multiple sensors are utilized to modify the statistical models utilized by the mine detector. Simulation results demonstrate the improvement in performance obtained using the MIAP algorithm. In this paper, we assume a hand-held mine detection system utilizing both an electromagnetic induction sensor (EMI) and a ground-penetrating radar (GPR). The hand-held mine detection sensors are designed to have two modes of operations: search mode and discrimination mode. Search mode generates an initial causal detection on the suspected location; and discrimination mode confirms whether there is a mine. The MIAP algorithm is applied in the discrimination mode for hand-held mine detection. The performance of the detector is evaluated on a data set collected by the government, and the performance is compared with the other traditional fusion results.

Proceedings Article | 21 September 2004 Paper

Landmine discrimination via Bayesian adaptive multimodal processing: results for handheld and vehicular sensors

Yongli Yu, Peter Torrione, Leslie Collins

Proceedings Volume 5415, (2004) https://doi.org/10.1117/12.542276

KEYWORDS: Sensors, General packet radio service, Metals, Land mines, Detection and tracking algorithms, Electromagnetic coupling, Mining, Sensor fusion, Algorithm development, Fusion energy

Read Abstract +

The recent development of high quality sensors paired with development of advanced statistical signal processing algorithms has shown that there are sensors that can not only discriminate targets from clutter, but can also identify subsurface or obscured targets. In a previous theoretical and simulation study, we utilized this identification capability in addition to contextual information in a multi-modal adaptive algorithm where the identification capabilities of multiple sensors are utilized to modify the prior probability density functions associated with statistical models being utilized by other sensors. We assumed that the statistics describing the features associated with each sensor modality follow a Gaussian mixture density, where in many cases the individual Gaussian distributions that make up the mixture result from different target types or target classes. We utilized identification information from one sensor to modify the weights associated with the probability density functions being utilized by algorithms associated with other sensor modalities. In our simulations, this approach is shown to be improve sensor performance by reducing the overall false alarm rate. In this talk, we transition the approach from a simulation study to consider real field data collected by both handheld and vehicular based systems. We show that by appropriate modification of our statistical models to accurately match field data, improved performance can be obtained over traditional sensor fusion algorithms.

Proceedings Article | 11 September 2003 Paper

Adaptive multimodality processing for the discrimination of landmines

Yongli Yu, Leslie Collins

Proceedings Volume 5089, (2003) https://doi.org/10.1117/12.487216

KEYWORDS: Sensors, Electromagnetic coupling, General packet radio service, Land mines, Detection and tracking algorithms, Metals, Mining, Lawrencium, Sensor performance, Data fusion

Read Abstract +

As in many application areas, performance of landmine detection algorithms is judged in terms of detection and false alarm rates. It is widely accepted that single sensors cannot simultaneously achieve both high detection rates and low false alarm rates, since every sensor has its advantages and disadvantages when dealing with a large variety of landmines, from large metal-cased mines to small plastic-cased mines. The recent development of high quality sensors in conjunction with statistical signal processing algorithms has shown that there are sensors that can not only discriminate targets from clutter, but can also identify subsurface or obscured targets. Here, we utilize this identification capability in addition to contextual information in a multi-modal adaptive algorithm where the identification capabilities of multiple sensors are utilized to modify the prior probability density functions associated with statistical models being utilized by other sensors. In general, every sensor modality is associated with a specific physics-based feature set that is extracted from the sensor data. Often, the statistics describing these features are assumed to follow a Gaussian mixture density, where in many cases the individual Gaussian distributions that make up the mixture result from different target types or target classes. We utilize identification information from one sensor to modify the weights associated with the probability density functions being utilized by algorithms associated with other sensor modalities. Using both simulated and real data, this approach is shown to be improve sensor performance by reducing the overall false alarm rate.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years