scholarly journals Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets

2019 ◽  
Vol 9 (19) ◽  
pp. 4187 ◽  
Author(s):  
Rang Liu ◽  
Hongqi Fan ◽  
Huaitie Xiao
Keyword(s):  
Likelihood Function ◽  
Signal To Noise Ratio ◽  
Mutual Interference ◽  
Joint Detection ◽  
Detection And Tracking ◽  
Point Spread ◽  
Spread Function ◽  
Bernoulli Filter ◽  
Effectiveness And Efficiency ◽  
Radar Targets

A labeled multi-Bernoulli (LMB) filter is presented to jointly detect and track radar targets. A relevant LMB filter is recently proposed by Rathnayake which assumes that the measurements of different targets do not overlap, leading to the favorable separable likelihood assumption. However, new or close tracks often violate the assumption and lead to a bias in the cardinality estimate. To address this problem, a one-to-one association method between measurements and tracks is proposed. In our method, any target only corresponds to its associated measurements and different tracks have little mutual interference. In addition, an approximate method for calculating the point spread function of radar is developed to improve the computational efficiency of likelihood function. The simulation under low signal-to-noise ratio scenario with closely spaced targets have demonstrated the effectiveness and efficiency of the proposed algorithm.

scholarly journals Joint Detection and DOA Tracking with a Bernoulli Filter Based on Information Theoretic Criteria

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3473 ◽  
Author(s):  
Guangpu Zhang ◽  
Ce Zheng ◽  
Sibo Sun ◽  
Guolong Liang ◽  
Yifeng Zhang
Keyword(s):  
Likelihood Function ◽  
Signal To Noise Ratio ◽  
Joint Detection ◽  
Information Theoretic ◽  
Practical Applications ◽  
Stochastic Signal ◽  
State Process ◽  
Finite Set ◽  
Bernoulli Filter ◽  
Information Theoretic Criteria

In this paper, we study the problem of the joint detection and direction-of-arrival (DOA) tracking of a single moving source which can randomly appear or disappear from the surveillance volume. Firstly, the Bernoulli random finite set (RFS) is employed to characterize the randomness of the state process, i.e., the dynamics of the source motion and the source appearance. To increase the performance of the detection and DOA tracking in low signal-to-noise ratio (SNR) scenarios, the measurements are obtained directly from an array of sensors and allow multiple snapshots. A track-before-detect (TBD) Bernoulli filter is proposed for tracking a randomly on/off switching single dynamic system. Secondly, since the variances of the stochastic signal and measurement noise are unknown in practical applications, these nuisance parameters are marginalized by defining an uninformative prior, and the likelihood function is compensated by using the information theoretic criteria. The simulation results demonstrate the performance of the filter.

An Approach to Measuring the Point Spread Function of the Confocal Raman Microscope

2020 ◽  
Vol 74 (10) ◽  
pp. 1230-1237
Author(s):  
Xiang Ding ◽  
Yanzhe Fu ◽  
Jiyan Zhang ◽  
Yao Hu ◽  
Shihang Fu
Keyword(s):  
Image Quality ◽  
Size Effect ◽  
Point Spread Function ◽  
Performance Indicator ◽  
Signal To Noise Ratio ◽  
Polystyrene Microspheres ◽  
Point Spread ◽  
Spread Function ◽  
Confocal Raman

The confocal Raman microscope (CRM) is a powerful tool in analytical science. Image quality is the most important performance indicator of CRM systems. The point spread function (PSF) is one of the most useful tools to evaluate the image quality of microscopic systems. A method based on a point-like object is proposed to measure the PSF of CRM, and the size effect of spherical objects is discussed. A series of phantoms are fabricated by embedding different sizes of polystyrene microspheres into polydimethylsiloxane matrix. The diameters of microspheres are from 0.2 µm to 5 µm. The phantoms are tested by measuring the PSF of a commercial CRM whose nominal lateral resolution is about 1 µm. Results of the PSF are obtained and the accuracy of resolution is used to evaluate the size effect of the microspheres. Experimental results are well consistent with theoretical analysis. The error of the PSF can be decreased by reducing the diameter of the microsphere but meanwhile the signal-to-noise ratio (S/N) will be lowered as well. The proper diameter of microspheres is proposed in consideration of the trade-off between the S/N and the measurement error of the PSF. Results indicate that the method provides a useful approach to measurement of the PSF and the resolution of the CRM.

scholarly journals Image Deblurring using Wiener Filtering and Siamese Neural Network

2021 ◽  
Vol 9 (3) ◽  
pp. 96
Author(s):  
Vaibhav Setia ◽  
Shreya Kumar
Keyword(s):  
Neural Network ◽  
Image Matching ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Function Estimation ◽  
Point Spread ◽  
Spread Function ◽  
Blurred Image ◽  
Blurred Images ◽  
Local Image

Blurred images are difficult to avoid in situations when minor Atmospheric turbulence or camera movement results in low-quality images. We propose a system that takes a blurred image as input and produces a deblurred image by utilizing various filtering techniques. Additionally, we utilize the Siamese Network to match local image segments. A Siamese Neural Network model is used that is trained to account for image matching in the spatial domain. The best-matched image returned by the model is then further used for Signal-to-Noise ratio and Point Spread Function estimation. The Wiener filter is then used to deblur the image. Finally, the results of the deblurring techniques with existing algorithms are compared and it is shown that the error in deblurring an image using the techniques presented in this paper is considerably lesser than other techniques.

scholarly journals Point-spread Function Deconvolution of the IFU Data and Restoration of Galaxy Stellar Kinematics

2021 ◽  
Vol 257 (2) ◽  
pp. 66
Author(s):  
Haeun Chung ◽  
Changbom Park ◽  
Yong-Sun Park
Keyword(s):  
Point Spread Function ◽  
Performance Test ◽  
Spatial Information ◽  
Signal To Noise Ratio ◽  
Line Of Sight ◽  
Stellar Kinematics ◽  
Point Spread ◽  
Deconvolution Algorithm ◽  
Spread Function ◽  
Field Unit

Abstract We present a performance test of the point-spread function (PSF) deconvolution algorithm applied to astronomical integral field unit (IFU) spectroscopy data for restoration of galaxy kinematics. We deconvolve the IFU data by applying the Lucy–Richardson algorithm to the 2D image slice at each wavelength. We demonstrate that the algorithm can effectively recover the true stellar kinematics of the galaxy, by using mock IFU data with a diverse combination of surface brightness profile, signal-to-noise ratio, line-of-sight geometry, and line-of-sight velocity distribution (LOSVD). In addition, we show that the proxy of the spin parameter λ R e can be accurately measured from the deconvolved IFU data. We apply the deconvolution algorithm to the actual SDSS-IV MaNGA IFU survey data. The 2D LOSVD, geometry, and λ R e measured from the deconvolved MaNGA IFU data exhibit noticeable differences compared to the ones measured from the original IFU data. The method can be applied to any other regular-grid IFU data to extract the PSF-deconvolved spatial information.

scholarly journals Accurate Photometry of Saturated Stars Using the Point-spread-function Wing Technique with Spitzer

2022 ◽  
Vol 163 (2) ◽  
pp. 46
Author(s):  
Kate Y. L. Su ◽  
G. H. Rieke ◽  
M. Marengo ◽  
Everett Schlawin
Keyword(s):  
Point Spread Function ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Large Radius ◽  
Point Spread ◽  
Primary Sample ◽  
Spread Function ◽  
Optical Stability ◽  
Flux Calibration ◽  
Better Than

Abstract We report Spitzer 3.6 and 4.5 μm photometry of 11 bright stars relative to Sirius, exploiting the unique optical stability of the Spitzer Space Telescope point-spread function (PSF). Spitzer's extremely stable beryllium optics in its isothermal environment enables precise comparisons in the wings of the PSF from heavily saturated stars. These bright stars stand as the primary sample to improve stellar models, and to transfer the absolute flux calibration of bright standard stars to a sample of fainter standards useful for missions like JWST and for large ground-based telescopes. We demonstrate that better than 1% relative photometry can be achieved using the PSF wing technique in the radial range of 20″–100″ for stars that are fainter than Sirius by 8 mag (from outside the saturated core to a large radius where a high signal-to-noise ratio profile can still be obtained). We test our results by (1) comparing the [3.6]−[4.5] color with that expected between the WISE W1 and W2 bands, (2) comparing with stars where there is accurate K S photometry, and (3) also comparing with relative fluxes obtained with the DIRBE instrument on COBE. These tests confirm that relative photometry is achieved to better than 1%.

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