IEICE TRANSACTIONS on Communications
Thresholding Process Based Dynamic Programming Track-Before-Detect Algorithm
Summary :
Dynamic Programming (DP) based Track-Before-Detect (TBD) algorithm is effective in detecting low signal-to-noise ratio (SNR) targets. However, its complexity increases exponentially as the dimension of the target state space increases, so the exact implementation of DP-TBD will become computationally prohibitive if the state dimension is more than two or three, which greatly prevents its applications to many realistic problems. In order to improve the computational efficiency of DP-TBD, a thresholding process based DP-TBD (TP-DP-TBD) is proposed in this paper. In TP-DP-TBD, a low threshold is first used to eliminate the noise-like (with low-amplitude) measurements. Then the DP integration process is modified to only focuses on the thresholded higher-amplitude measurements, thus huge amounts of computation devoted to the less meaningful low-amplitude measurements are saved. Additionally, a merit function transfer process is integrated into DP recursion to guarantee the inheritance and utilization of the target merits. The performance of TP-DP-TBD is investigated under both optical style Cartesian model and surveillance radar model. The results show that substantial computation reduction is achieved with limited performance loss, consequently TP-DP-TBD provides a cost-efficient tradeoff between computational cost and performance. The effect of the merit function transfer on performance is also studied.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E96-B No.1 pp.291-300
- Publication Date
- 2013/01/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E96.B.291
- Type of Manuscript
- PAPER
- Category
- Sensing
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Wei YI, Lingjiang KONG, Jianyu YANG, "Thresholding Process Based Dynamic Programming Track-Before-Detect Algorithm" in IEICE TRANSACTIONS on Communications,
vol. E96-B, no. 1, pp. 291-300, January 2013, doi: 10.1587/transcom.E96.B.291.
Abstract: Dynamic Programming (DP) based Track-Before-Detect (TBD) algorithm is effective in detecting low signal-to-noise ratio (SNR) targets. However, its complexity increases exponentially as the dimension of the target state space increases, so the exact implementation of DP-TBD will become computationally prohibitive if the state dimension is more than two or three, which greatly prevents its applications to many realistic problems. In order to improve the computational efficiency of DP-TBD, a thresholding process based DP-TBD (TP-DP-TBD) is proposed in this paper. In TP-DP-TBD, a low threshold is first used to eliminate the noise-like (with low-amplitude) measurements. Then the DP integration process is modified to only focuses on the thresholded higher-amplitude measurements, thus huge amounts of computation devoted to the less meaningful low-amplitude measurements are saved. Additionally, a merit function transfer process is integrated into DP recursion to guarantee the inheritance and utilization of the target merits. The performance of TP-DP-TBD is investigated under both optical style Cartesian model and surveillance radar model. The results show that substantial computation reduction is achieved with limited performance loss, consequently TP-DP-TBD provides a cost-efficient tradeoff between computational cost and performance. The effect of the merit function transfer on performance is also studied.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E96.B.291/_p
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@ARTICLE{e96-b_1_291,
author={Wei YI, Lingjiang KONG, Jianyu YANG, },
journal={IEICE TRANSACTIONS on Communications},
title={Thresholding Process Based Dynamic Programming Track-Before-Detect Algorithm},
year={2013},
volume={E96-B},
number={1},
pages={291-300},
abstract={Dynamic Programming (DP) based Track-Before-Detect (TBD) algorithm is effective in detecting low signal-to-noise ratio (SNR) targets. However, its complexity increases exponentially as the dimension of the target state space increases, so the exact implementation of DP-TBD will become computationally prohibitive if the state dimension is more than two or three, which greatly prevents its applications to many realistic problems. In order to improve the computational efficiency of DP-TBD, a thresholding process based DP-TBD (TP-DP-TBD) is proposed in this paper. In TP-DP-TBD, a low threshold is first used to eliminate the noise-like (with low-amplitude) measurements. Then the DP integration process is modified to only focuses on the thresholded higher-amplitude measurements, thus huge amounts of computation devoted to the less meaningful low-amplitude measurements are saved. Additionally, a merit function transfer process is integrated into DP recursion to guarantee the inheritance and utilization of the target merits. The performance of TP-DP-TBD is investigated under both optical style Cartesian model and surveillance radar model. The results show that substantial computation reduction is achieved with limited performance loss, consequently TP-DP-TBD provides a cost-efficient tradeoff between computational cost and performance. The effect of the merit function transfer on performance is also studied.},
keywords={},
doi={10.1587/transcom.E96.B.291},
ISSN={1745-1345},
month={January},}
Copy
TY - JOUR
TI - Thresholding Process Based Dynamic Programming Track-Before-Detect Algorithm
T2 - IEICE TRANSACTIONS on Communications
SP - 291
EP - 300
AU - Wei YI
AU - Lingjiang KONG
AU - Jianyu YANG
PY - 2013
DO - 10.1587/transcom.E96.B.291
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E96-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2013
AB - Dynamic Programming (DP) based Track-Before-Detect (TBD) algorithm is effective in detecting low signal-to-noise ratio (SNR) targets. However, its complexity increases exponentially as the dimension of the target state space increases, so the exact implementation of DP-TBD will become computationally prohibitive if the state dimension is more than two or three, which greatly prevents its applications to many realistic problems. In order to improve the computational efficiency of DP-TBD, a thresholding process based DP-TBD (TP-DP-TBD) is proposed in this paper. In TP-DP-TBD, a low threshold is first used to eliminate the noise-like (with low-amplitude) measurements. Then the DP integration process is modified to only focuses on the thresholded higher-amplitude measurements, thus huge amounts of computation devoted to the less meaningful low-amplitude measurements are saved. Additionally, a merit function transfer process is integrated into DP recursion to guarantee the inheritance and utilization of the target merits. The performance of TP-DP-TBD is investigated under both optical style Cartesian model and surveillance radar model. The results show that substantial computation reduction is achieved with limited performance loss, consequently TP-DP-TBD provides a cost-efficient tradeoff between computational cost and performance. The effect of the merit function transfer on performance is also studied.
ER -
English
