Low-SNR Infrared Point Target Detection and Tracking via Saliency-Guided Double-Stage Particle Filter

Jia, Liangjie; Rao, Peng; Zhang, Yuke; Su, Yueqi; Chen, Xin

doi:10.3390/s22072791

Cited by 9 publications

(5 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…underwater acoustics [15], infrared point target detection and tracking [17], real-time remote sensing [18], seismic monitoring [19], target detection and tracking [20] and helicopter blade detection [21].…”

Section: Table III Comparison Of the Matched Filtermentioning

confidence: 99%

Performance of a Neural Network Classifier Based Demodulator in Communication System

Kumar

2024

IEEE Access

View full text Add to dashboard Cite

The success rate of a neural network (NN) classifier (rectified linear unit, 10 layers, softmax output layer activation)-based demodulator was proposed and evaluated for phase-shift keying (PSK) and quadrature amplitude modulation (QAM) modulated signals corrupted by additive white Gaussian, chisquared, uniform, and Rayleigh noise channels with signal-to-noise ratios (SNR) ranging from -20 dB to +20 dB. Low SNR are common in spectrum sensing, cognitive radio networks, underwater acoustics, target detection, remote sensing, seismic monitoring, and helicopter blade detection. This classifier-modulator performance was compared with that of the matched filter detector (MFD) for varying channel noise, constellation type (PSK or QAM), constellation size (M=2, 4, 8, 16), sample size (N), and training to test the data ratio. The classifier demodulator had a performance equal to or better than MFD in 98% of the scenarios.A training-to-test ratio of 70:30 or 80:20 is appropriate. The classifier performances of M-PSK and M-QAM are comparable. The superior performance of the NN classifier is more pronounced for M values greater than 2. N=5000 or higher is sufficient for most scenarios, and N=20000 is necessary for M=16. A higher success rate was obtained for additive chisquare and Rayleigh noise channels. The proposed demodulator performed significantly better than the matched filter for SNR values ≤ 0 dB. 16-QAM over an additive uniform noise channel had a better success rate for an SNR of 0 dB or less, whereas 16-QAM over an additive Rayleigh noise channel had a better success rate for an SNR of 5 dB or higher.

show abstract

Section: Table III Comparison Of the Matched Filtermentioning

confidence: 99%

Performance of a Neural Network Classifier Based Demodulator in Communication System

Kumar

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…While these stars may not significantly affect the performance of DBT methods, they can still hurt real-time performance and accuracy in TBD methods. Existing TBD methods, such as particle filter [38,39], dynamic programming [40,41], and Hough transform [42,43], have primarily focused on digging targets that are covered by heavy noise. Usually, these TBD methods need extra processes to identify real targets and stars, which can negatively impact real-time performance and accuracy.…”

Section: Motivationmentioning

confidence: 99%

Infrared Dim Star Background Suppression Method Based on Recursive Moving Target Indication

Zhang,

Rao,

Hong

et al. 2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Space-based infrared target detection can provide full-time and full-weather observation of targets, thus it is of significance in space security. However, the presence of stars in the background can severely affect the accuracy and real-time performance of infrared dim and small target detection, making star suppression a key technology and hot spot in the field of space target detection. The existing star suppression algorithms are all oriented towards the detection before track method and rely on the single image properties of the stars. They can only effectively suppress bright stars with a high signal-to-noise ratio (SNR). To address this problem, we propose a new method for infrared dim star background suppression based on recursive moving target indication (RMTI). Our proposed method is based on a more direct analysis of the image sequence itself, which will lead to more robust and accurate background suppression. The method first obtains the motion information of stars through satellite motion or key star registration. Then, the advanced RMTI algorithm is used to enhance the stars in the image. Finally, the mask of suppressing stars is generated by an accumulation frame adaptive threshold. The experimental results show that the algorithm has a less than 8.73% leakage suppression rate for stars with an SNR ≤ 2 and a false suppression rate of less than 2.3%. The validity of the proposed method is verified in real data. Compared with the existing methods, the method proposed in this paper can stably suppress stars with a lower SNR.

show abstract

“…Common filters used include the Top-Hat filter [19], the max-mean/max-median filter [20], and the median subtraction filter [21]. In addition, Jia et al [22] proposed a Saliency-Guided Double-Stage Particle Filter (SGDS-PF) consisting of a searching Particle Filter (PF) and tracking PF, which achieved high tracking precision even under intensive noise. Tang et al [23] used a dilate filter to increase the characteristic information of the suspected target, which was able to detect the suspected target easily, and the time complexity was very low.…”

Section: Introductionmentioning

confidence: 99%

An Effective Method of Infrared Maritime Target Enhancement and Detection with Multiple Maritime Scene

et al. 2023

View full text Add to dashboard Cite

Aiming at maritime infrared target detection with low contrast influenced by maritime clutter and illumination, this paper proposes a Modified Histogram Equalization with Edge Fusion (MHEEF) pre-processing algorithm in backlight maritime scenes and establishes Local-Contrast Saliency Models with Double Scale and Modes (LCMDSM) for detecting a target with the properties of positive and negative contrast. We propose a local-contrast saliency mathematical model with double modes in the extension of only one mode. Then, the big scale and small scale are combined into one Target Detection Unit (TDU), which can approach the “from bottom to up” mechanism of the Visual Attention Model (VAM) better and identify the target with a suitable size, approaching the target’s actual shape. In the experimental results and analysis, clutter, foggy, backlight, and dim maritime scenes are chosen to verify the effectiveness of the target detection algorithm. From the enhancement result, the LCMDSM algorithm can achieve a Detection Rate (DR) with a value of 98.26% under each maritime scene on the average level and can be used in real-time detection with low computational cost.

show abstract

Low-SNR Infrared Point Target Detection and Tracking via Saliency-Guided Double-Stage Particle Filter

Cited by 9 publications

References 41 publications

Performance of a Neural Network Classifier Based Demodulator in Communication System

Performance of a Neural Network Classifier Based Demodulator in Communication System

Infrared Dim Star Background Suppression Method Based on Recursive Moving Target Indication

An Effective Method of Infrared Maritime Target Enhancement and Detection with Multiple Maritime Scene

Contact Info

Product

Resources

About