| Revista: | Journal of applied research and technology |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000373892 |
| ISSN: | 1665-6423 |
| Autores: | Zhang, Ji1 Liu, Yu2 |
| Instituciones: | 1North China Electric Power University Baoding, Department of Computer, Hebei. China 2University of New Orleans, Department of Electrical Engineering, Nueva Orleans, Luisiana. Estados Unidos de América |
| Año: | 2013 |
| Periodo: | Oct |
| Volumen: | 11 |
| Número: | 5 |
| Paginación: | 641-652 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | The measurement origin uncertainty and target (dynamic or/and measurement) model uncertainty are two fundamental problems in maneuvering target tracking in clutter. The multiple hypothesis tracker (MHT) and multiple model (MM) algorithm are two well-known methods dealing with these two problems, respectively. In this work, we address the problem of single maneuvering target tracking in clutter by combing MHT and MM based on the Gaussian mixture reduction (GMR). Different ways of combinations of MHT and MM for this purpose were available in previous studies, but in heuristic manners. The GMR is adopted because it provides a theoretically appealing way to reduce the exponentially increasing numbers of measurement association possibilities and target model trajectories. The superior performance of our method, comparing with the existing IMM+PDA and IMM+MHT algorithms, is demonstrated by the results of Monte Carlo simulation |
| Disciplinas: | Ingeniería |
| Palabras clave: | Ingeniería eléctrica, Seguimiento de objetivos, Modelos múltiples |
| Keyword: | Engineering, Electrical engineering, Target tracking, Multiple models |
| Texto completo: | Texto completo (Ver HTML) |
