Name
Abstract | ||
|---|---|---|
This paper deals with multi-target localization in statistical MIMO radar. An improved target locating algorithm is proposed which combines Kalman filtering with fuzzy C clustering. The Kalman filter is utilized to acquire the information of target location and fuzzy C clustering is used for data fusion as there are multiple receivers in radar. For target locating in MIMO radar, we first utilize the maximum likelihood estimation algorithm to estimate the parameters of targets. To eliminate the influence of noise on the parameter estimation, we take advantage of the gliding property of Kalman filter to process the result of parameter estimation. All these processing data from different receivers is fused by fuzzy C cluster to obtain the parameters estimation of all targets. We give scenarios including MIMO radar and targets to analyze the performance of this target location algorithm. With considering the effects of noise, the position of receivers and transmitters and the moving of targets, the analysis is carried out by evaluating the location accuracy of the algorithm. The simulation result shows that the proposed method can locate multiply targets effectively and improves the location accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-19156-6_34 | WIRELESS AND SATELLITE SYSTEMS, PT II |
Keywords | DocType | Volume |
Statistical MIMO radar, Multi-target localization, Kalman filtering, Fuzzy C clustering | Conference | 281 |
ISSN | Citations | PageRank |
1867-8211 | 0 | 0.34 |
References | Authors | |
0 | 5 | |