Name
Abstract | ||
|---|---|---|
In the statistical target recognition based on radar high-resolution range profile (HRRP), two challenging tasks are how to deal with the target-aspect, time-shift, and amplitude-scale sensitivity of HRRP and how to accurately describe HRRPs statistical characteristics. In this paper, based on the scattering center model, range cells are classified, in accordance with the number of predominant scatterers in each cell, into three statistical types. After resolving the three sensitivity problems, this paper develops a statistical model comprising two distribution forms, i.e., Gamma distribution and Gaussian mixture distribution, to model echoes of different types of range cells as the corresponding distribution forms. Determination of the type of a range cell is achieved by using the rival penalized competitive learning (RPCL) algorithm, while estimation for the parameters of Gamma distribution and Gaussian mixture distribution by the maximum likelihood (ML) method and the expectation-maximization (EM) algorithm, respectively. Experimental results for measured data show that the proposed statistical model not only has better recognition performance but also is more robust to noises than the two existing statistical models, i.e., Gaussian model and Gamma model. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/TSP.2006.873534 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
gamma distribution,distribution form,radar hrrp target recognition,hrrps statistical characteristic,range cell,model echo,corresponding distribution form,gaussian model,gaussian mixture distribution,existing statistical model,gamma model,em algorithm,gaussian distribution,intelligent sensors,maximum likelihood estimation,maximum likelihood,probability,high resolution,automatic target recognition,expectation maximization,expectation maximization algorithm,statistical model,hidden markov models,parameter estimation | Radar,Pattern recognition,Expectation–maximization algorithm,Gaussian process,Gaussian network model,Statistical model,Artificial intelligence,Gamma distribution,Estimation theory,Mixture theory,Mathematics | Journal |
Volume | Issue | ISSN |
54 | 6 | 1053-587X |
Citations | PageRank | References |
39 | 2.15 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lan Du | 1 | 272 | 34.83 |
| Hongwei Liu | 2 | 416 | 66.06 |
| Zheng Bao | 3 | 1985 | 155.03 |
| Junying Zhang | 4 | 86 | 7.59 |