A Bi-Parametric Clutter-Map CFAR Detection Method in Non-Gaussian Environment for Foreign Objects Debris on Runways

2013 ◽  
Vol 401-403 ◽  
pp. 1173-1176 ◽  
Author(s):  
Jing Wu ◽  
Hong Wang ◽  
Xue Lian Yu
Keyword(s):  
Detection Method ◽  
Parameters Estimation ◽  
Time Varying ◽  
Recursive Filter ◽  
Cfar Detection ◽  
Foreign Objects ◽  
Primary Interference ◽  
Non Gaussian

Time-varying land clutter is primary interference for Foreign Objects Debris (FOD) detection on airport runways. Traditional clutter-map CFAR (CM-CFAR) algorithms were ineffect-ive to detect targets in non-Gaussian clutter. In this paper, a Bi-parametric CM-CFAR (Bi-CM-CFAR) algorithm based on bi-parameters estimation is proposed. Clutter-level estimation is obtained with video integrator, parameters estimator and recursive filter; and updated in each scanning period. Moreover, simulations verify effectiveness of this method for FOD detection.

Robust monoparametric multiradar CFAR detection against non-Gaussian spiky clutter

1997 ◽  
Vol 144 (3) ◽  
pp. 131 ◽  
Author(s):  
F. Gini ◽  
F. Lombardini ◽  
L. Verrazzani
Keyword(s):  
Cfar Detection ◽  
Non Gaussian

scholarly journals Adjacent Infrared Multitarget Detection Using Robust Background Estimation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Sungho Kim ◽  
Kyung-Tae Kim
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Detection Method ◽  
Detection Performance ◽  
Detection Methods ◽  
Constant False Alarm Rate ◽  
Cluttered Environments ◽  
Background Estimation ◽  
Cfar Detection

Small target detection is very important for infrared search and track (IRST) problems. Grouped targets are difficult to detect using the conventional constant false alarm rate (CFAR) detection method. In this study, a novel multitarget detection method was developed to identify adjacent or closely spaced small infrared targets. The neighboring targets decrease the signal-to-clutter ratio in hysteresis threshold-based constant false alarm rate (H-CFAR) detection, which leads to poor detection performance in cluttered environments. The proposed adjacent target rejection-based robust background estimation can reduce the effects of the neighboring targets and enhance the small multitarget detection performance in infrared images by increasing the signal-to-clutter ratio. The experimental results of the synthetic and real adjacent target sequences showed that the proposed method produces an upgraded detection rate with the same false alarm rate compared to the recent target detection methods (H-CFAR, Top-hat, and TDLMS).

scholarly journals A novel design of algorithm and framework for decentralized CFAR signal detection without prior information

2018 ◽  
Vol 189 ◽  
pp. 04006
Author(s):  
Nan Wang ◽  
Yunshan Xu ◽  
Haibao Xia ◽  
Jundi Wang
Keyword(s):  
Signal Detection ◽  
Prior Information ◽  
Detection Method ◽  
Detection Algorithm ◽  
Constant False Alarm Rate ◽  
Test Statistic ◽  
Fusion Algorithm ◽  
Cfar Detection ◽  
Novel Design ◽  
Fusion Detection

In this paper, a fusion detection algorithm that focuses on decentralized CFAR (Constant False Alarm Rate) signal detection problem without prior information is proposed. In the algorithm, the threshold and test statistic of the detection fusion algorithm derive from the conventional CFAR detection method. At last a framework for decentralized CFAR signal detection is designed corresponding to the fusion algorithm. Simulation results illustrate that an almost optimal detection performance is obtained by the proposed algorithm.

scholarly journals Windowed Least Square Algorithm Based PMSM Parameters Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Song Wang
Keyword(s):  
Embedded System ◽  
Window Size ◽  
Parameters Estimation ◽  
Least Square ◽  
Time Varying ◽  
Recursive Least Square ◽  
Permanent Magnet Synchronous Motors ◽  
Practical Applications ◽  
Controlling System ◽  
System Resource

Stator resistance and inductances ind-axis andq-axis of permanent magnet synchronous motors (PMSMs) are important parameters. Acquiring these accurate parameters is usually the fundamental part in driving and controlling system design, to guarantee the performance of driver and controller. In this paper, we adopt a novel windowed least algorithm (WLS) to estimate the parameters with fixed value or the parameter with time varying characteristic. The simulation results indicate that the WLS algorithm has a better performance in fixed parameters estimation and parameters with time varying characteristic identification than the recursive least square (RLS) and extended Kalman filter (EKF). It is suitable for engineering realization in embedded system due to its rapidity, less system resource possession, less computation, and flexibility to adjust the window size according to the practical applications.

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