摘要
作为红外自寻的制导、搜索跟踪和预警等领域的一项关键技术,红外弱小目标检测成为了红外图像处理领域中一项历史悠久且又充满活力的研究课题。对于实际的武器系统而言,如何充分发挥红外目标检测技术的优势,提高目标的检测能力,尽早获取来袭目标的相关信息对于提高武器系统的性能具有重要的意义。对于远距离目标,其成像面积小,辐射能量弱,且易遭受强噪声和背景杂波的影响。因此,如何实现低信噪比条件下红外弱小目标的可靠检测成为了一项更具实际意义和挑战性的研究课题。
本文针对复杂背景下的红外弱小目标检测问题,以模糊推理、高阶统计量分析、瞬态信号检测和检测前跟踪等理论为基础对红外弱小目标检测技术进行了深入地研究,取得的主要研究成果如下:
(1)为了获得更好的检测性能,本文在图像差分检测法的基础上提出了一种基于模糊推理的检测新算法,该算法利用模糊推理融合多帧图像中的目标信息,通过设定的推理规则实现目标的“软判决”。该算法克服了差分法检测存在的门限难确定和“硬”判决带来的检测概率低的缺点,有效地改善了弱小目标的检测性能。此外,将该算法推广到红外双波段情况,提出了一种红外双波段模糊融合检测算法。
(2)通过将序列图像中目标经过时像素点灰度值的起伏变化表示为一维非高斯瞬态信号,利用高阶统计量对高斯噪声良好的抑制特性,提出了一种基于三阶累积量的弱小目标检测算法。该算法在时间维通过构造三阶累积量实现对瞬态信号的检测,且在三阶累积量估计中的去均值处理具有抑制背景杂波的作用,有效地改善了信噪比(SNR)。实验表明,该算法能够在SNR≥1时可靠检测目标。同时,将该方法推广到双谱域,得到了双谱域的似然比检测算法。
(3)依据图像序列中弱小运动目标像素点灰度值可等效为一维瞬态信号,将瞬态信号检测方法引入到红外弱小目标检测领域中,提出了一种基于DFT的Power-Law运动目标检测算法,实现了强杂波中的弱小目标检测,同时利用高阶累积量对Power-Law检测器的统计检测量进行改进,提出了一种基于双谱切片的检测算法,有效地改善了检测性能。
(4)检测前跟踪算法是近年来低信噪比情况下目标检测的有效方法之一,其关键是跟踪算法。本文针对粒子滤波及其改进算法采用蒙特卡罗随机采样引起的估计性能降低的问题,提出了一种基于拟蒙特卡罗(QMC)采样的高斯粒子滤波算法(QMC-GPF),基于此提出了一种红外弱小目标检测前跟踪算法。该算法将红外弱小目标幅度也作为待估计的状态分量,利用所提出的QMC-GPF算法进行状态跟踪,最后通过迭代更新的协方差矩阵的收敛特性构建判断逻辑,实现目标的“软判决”检测。由于算法利用了QMC采样能产生较少但分布更均匀的点集取代了蒙特卡罗(MC)采样产生的随机点集,更均匀地表示了采样空间,因此提高了目标的跟踪检测性能。
As key techniques in infrared (IR) homing guidance, target search and tracking, warning and so on, IR small dim target detection and tracking have been regarded as old-line and attractive research topics in the field of IR image processing. As for the real weapon systems, how to make the best of IR target detection techniques to improve the ability of target detection and to obtain the related information about the invading targets, have important significance for improvement of the real weapon systems. The longer the distance of targets, the less the imaging area of targets and the larger the probabilities of targets influenced by backrounds and clutter will be. Therefore, how small dim targets can be reliablely detected under low signal-to-noise ratio(SNR) have become the more realistic and challenging research topics.
Aiming at the problem of IR small dim target detection under a complex background, this thesis mainly deals this problem on the basis of the theories of fuzzy reason, high order statistic, transient signal detection and track-before-detect and so on. And the main achievements of this dissertation are as follows:
(1) For obtaining better detection performance, based on the classic image difference, a new detection algorithm based on fuzzy reason was proposed, which fused target information from adjacent frames by fuzzy reason theory, and implements 'soft decision'of target following the designing rules. Our algorithm avoids the shortcoming of low detection probability caused by difficult threshold determination and 'hard' decision, and improves the detection performance effectively. In addition, we extends the algorithm to be applied in the infrared dual-band detection and the corresponding algorithm is proposed.
(2) Considering the fluctuation of grey-scale values of pixels of the corresponding images, caused by a passing target, as a non-Gaussian transient signal, and combining excellent characteristics of suppressing Gaussian noise, a novel detection algorithm based on third-order cumulant was proposed. The transient signal can be detected by calculating its third-order cumulant. The average value subtraction in the estimation of the third-order cumulant also suppress the background clutter, leading to a great improvement of the signal to clutter ratio (SNR). The experimental results show that the algorithm can effectively suppress heavy infrared background and reliably detect dim targets with SNR larger than 1. Meanwhile, by calculating the Bispectrum likelihood ratio in frequency domain used for target detection, another algorithm was put forward in Bispectrum domain.
(3) Duo to the characteristic of the grey-scale value of a pixel in an image occurs fluctuation when a target passes by, the transient signal detection methods was applied in the small dim target detection, and a new detection algorithm based on the Power-Law(P-L) detector was proposed. The higher-order statistics have the advantage of suppressing Gaussian-noise. With the advantage, the method based on Bispectrum slice was proposed, this method can overcome the influence of the background noise and improve the detection performance obviously.
(4)Recently, the track-before-detection (TBD) algorithms are effective for the small dim target detection with low SNR. As the tracking part of TBD, the particle filter (PF) is attracting attention. According to the performance descent caused by Monte Carlo random sampling used in the PF and its some improved versions, a new Quasi-Monte-Carlo sampling based Gaussian Particle Filter(QMC-GPF) is proposed. Basing on that, a new TBD algorithm was proposed, which estimates on-line the standard kinematic parameters of the target, including position and velocity, as well as the amplitude of the target. The convergence characteristic of the covariance matrix of the posterior densities propagated in the QMC-GPF is used to determine whether it is the true target. The TBD algorithm, based on the idea of using more regularly distributed point set called low-discrepancy (LD) points to construct the approximation than the random point set associated with MC, would provide the best-possible spread in the sample space and improve the target tracking and detection performance obviously.
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