Name
Title | ||
|---|---|---|
Feature-Level and Decision-Level Fusion of Noncoincidently Sampled Sensors for Land Mine Detection |
Abstract | ||
|---|---|---|
We present and compare methods for feature-level (predetection) and decision-level (postdetection) fusion of multisensor data. This study emphasizes fusion techniques that are suitable for noncommensurate data sampled at noncoincident points. Decision-level fusion is most convenient for such data, but it is suboptimal in principle, since targets not detected by all sensors will not obtain the full benefits of fusion. A novel algorithm for feature-level fusion of noncommensurate, noncoincidently sampled data is described, in which a model is fitted to the sensor data and the model parameters are used as features. Formulations for both feature-level and decision-level fusion are described, along with some practical simplifications. A closed-form expression is available for feature-level fusion of normally distributed data and this expression is used with simulated data to study requirements for sample position accuracy in multisensor data. The performance of feature-level and decision-level fusion algorithms are compared for experimental data acquired by a metal detector, a ground-penetrating radar, and an infrared camera at a challenging test site containing surrogate mines. It is found that fusion of binary decisions does not perform significantly better than the best available sensor. The performance of feature-level fusion is significantly better than the individual sensors, as is decision-level fusion when detection confidence information is also available (驴soft-decision驴 fusion). |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/34.927459 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
decision-level fusion,noncommensurate data,multisensor data,available sensor,simulated data,feature-level fusion,decision-level fusion algorithm,noncoincidently sampled sensors,sensor data,experimental data,fusion technique,comparative method,normal distribution,ground penetrating radar,image sensors,sensor fusion,closed form expression,infrared | Radar,Computer vision,Ground-penetrating radar,Pattern recognition,Experimental data,Decision level,Computer science,Fusion,Sensor fusion,Artificial intelligence,Detector,Binary number | Journal |
Volume | Issue | ISSN |
23 | 6 | 0162-8828 |
Citations | PageRank | References |
45 | 2.80 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ajith H. Gunatilaka | 1 | 45 | 2.80 |
| Brian A. Baertlein | 2 | 58 | 5.88 |