Dynamic programming track-before-detect algorithm for radar target detection based on polynomial time series prediction

An improved dynamic programming track-before-detect (DP-TBD) algorithm is proposed in this study. A new relaxed DP-TBD test statistic containing a term of state transition probability is derived. The state transition probability is designed according to the one-step prediction of the target state. A...

Full description

Saved in:
Bibliographic Details
Published in:IET radar, sonar & navigation Vol. 10; no. 8; pp. 1327 - 1336
Main Authors: Zheng, Daikun, Wang, Shouyong, Meng, Qingwen
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 01.10.2016
Subjects:
Algorithms
Dynamic programming
energy back‐scattered integration
improved dynamic programming track‐before‐detect algorithm
Mathematical models
object detection
polynomial time series prediction
Polynomials
Radar detection
radar target detection
regression analysis
relaxed DP‐TBD test statistic
Research Article
state transition probability
Statistical tests
Time series
Transition probabilities
weighted least squares
radar detection
improved dynamic programming track-before-detect algorithm
polynomials
regression analysis
weighted least squares
dynamic programming
time series
object detection
state transition probability
radar target detection
relaxed DP-TBD test statistic
polynomial time series prediction
energy back-scattered integration
ISSN:1751-8784, 1751-8792
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:An improved dynamic programming track-before-detect (DP-TBD) algorithm is proposed in this study. A new relaxed DP-TBD test statistic containing a term of state transition probability is derived. The state transition probability is designed according to the one-step prediction of the target state. An asymptotic and recursive solution is developed to obtain the state prediction by the polynomial time series model under the framework of weighted least squares. The impact of the weight parameter on the performance of the proposed algorithm is also investigated. The proposed algorithm can efficiently integrate the energy back-scattered along the admissible target trajectory in that the designed state transition probability enables the relaxed test statistic to distinguish real targets from the false ones more effectively. The prediction needs no priori information of target state space model and can be embedded in the recursion of the DP-TBD. Numerical simulations are provided to assess and compare the performance of the proposed algorithm. It turns out that the proposed algorithm has better detection and tracking performance than the basic one and is resilient to various target motion forms.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1751-8784
1751-8792
DOI:10.1049/iet-rsn.2015.0332