Antenna Polarization Optimization for Target Detection in Non-Gaussian Clutter

Adaptive polarization design of radar antenna has recently become the focus of attention in radar polarization community. A polarimetric detector against non-Gaussian clutter with transmitter polarization optimization has been proposed in this paper. First, the radar data model including the realistic dependence of the clutter on the transmitted polarization is introduced. Then the polarimetric detector with transmitter polarization optimization is developed. By employing the simulation, we demonstrate that the polarization waveform optimization can bring the significant performance gain on target detection as compared to the conventional full-polarization approach. Besides, jointly optimizing transmitter and receiver polarization to form a scalar measurement is confirmed not to achieve a better detection performance than vector measurement with only transmitter polarization optimization.

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Robust Weighted l1,2 Norm Filtering in Passive Radar Systems

Sensors ◽

10.3390/s20113270 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3270 ◽

Cited By ~ 1

Author(s):

Baris Satar ◽

Gokhan Soysal ◽

Xue Jiang ◽

Murat Efe ◽

Thiagalingam Kirubarajan

Keyword(s):

Target Detection ◽

Impulsive Noise ◽

Simulated Data ◽

Real Data ◽

Detection Performance ◽

Passive Radar ◽

Mobile Telecommunication ◽

Telecommunication System ◽

Conventional Methods ◽

Non Gaussian

Conventional methods such as matched filtering, fractional lower order statistics cross ambiguity function, and recent methods such as compressed sensing and track-before-detect are used for target detection by passive radars. Target detection using these algorithms usually assumes that the background noise is Gaussian. However, non-Gaussian impulsive noise is inherent in real world radar problems. In this paper, a new optimization based algorithm that uses weighted l 1 and l 2 norms is proposed as an alternative to the existing algorithms whose performance degrades in the presence of impulsive noise. To determine the weights of these norms, the parameter that quantifies the impulsiveness level of the noise is estimated. In the proposed algorithm, the aim is to increase the target detection performance of a universal mobile telecommunication system (UMTS) based passive radars by facilitating higher resolution with better suppression of the sidelobes in both range and Doppler. The results obtained from both simulated data with α stable distribution, and real data recorded by a UMTS based passive radar platform are presented to demonstrate the superiority of the proposed algorithm. The results show that the proposed algorithm provides more robust and accurate detection performance for noise models with different impulsiveness levels compared to the conventional methods.

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A robust Bayesian approach to target detection applied to explosive threat detection in handheld ground penetrating radar data

10.1117/12.2050514 ◽

2014 ◽

Author(s):

Kenneth D. Morton ◽

Leslie M. Collins ◽

Peter A. Torrione

Keyword(s):

Target Detection ◽

Bayesian Approach ◽

Ground Penetrating Radar ◽

Radar Data ◽

Threat Detection ◽

Ground Penetrating

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Radar target detection method based on particle filter theory under correlated non-Gaussian clutter backgrounds

IET International Radar Conference 2015 ◽

10.1049/cp.2015.1026 ◽

2015 ◽

Author(s):

X. Feng ◽

J. Jiang ◽

P. Lin ◽

W.H. Wu

Keyword(s):

Particle Filter ◽

Target Detection ◽

Detection Method ◽

Radar Target ◽

Filter Theory ◽

Non Gaussian

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Sensitivity of the General Linear Model to Assumptions

Regular Issue - International Journal of Innovative Science and Modern Engineering ◽

10.35940/ijisme.e1210.036520 ◽

2020 ◽

Vol 6 (5) ◽

pp. 21-25

Keyword(s):

Linear Model ◽

Gaussian Noise ◽

Linear Models ◽

General Linear Model ◽

White Gaussian Noise ◽

General Linear ◽

Optimal Estimator ◽

Significant Performance ◽

Non Gaussian ◽

Noise Models

Non-Gaussian noise often causes in significant performance abatement for systems which are designed using Gaussian assumption. This report challenges the question of General Linear Model with White Gaussian Noise assumption in order to define the sensitivity of the performance of an optimal estimator. Gaussian noise models provide an important role in many signal processing applications. The Laplacian and Uniform signal are two worthy examples of noise that can be compared to the White Gaussian Noise, though the sensitivity which can be compared with any non-Gaussian. White Gaussian Noise has been considered for General Linear Models and deviation from whiteness would affect on our estimates under different circumstances. Moreover, new assumptions have been considered to generate different type of signals in order to evaluate the sensitivity of the General Linear Model.

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