Small Target Detection Performance Research Articles

Sparse representation for infrared Dim target detection via a discriminative over-complete dictionary learned online.

It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn't be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively.

Influence on the resolution based on defective NR-PC double flat lens group with Ag for target detection and imaging

Abstract In this paper, we employed the dynamic scanning scheme to study the influence on target detection and imaging based on the defective NR-PC double flat lens group with Ag by finite-difference-time-domain method. We firstly deduced the focusing imaging circuit diagram of NR-PC flat lens with the Snell extension law and geometrical optics principle. Numerical simulations indicate that significant enhancement of the scattering signal can be obtained while the NR-PC flat lens is used for target detection and imaging. Then the imaging principle of NR-PC flat lens group is studied. We find that the distance between the two lenses can be adjusted flexibly. What is important, the resolution of target detection is greatly improved when incorporating Ag into the NR-PC flat lens group. In conclusion, our investigation makes the target detection and imaging by NR-PC flat lens come true, and optimizes the performance of small target detection and imaging system.

Predicting Small Target Detection Performance of Low-SNR Airborne Lidar

Recent technological advances in the performance of small micro-lasers and multi-channel multi-event photo-detectors have enabled the development of experimental airborne lidar (light detection and ranging) systems based on a low-SNR (LSNR) paradigm. Due to dense point spacing (tens of points per square meter) and sub-decimeter range resolution, LSNR lidar can likely enable detection of meter-scale targets that would go unnoticed by traditional lidar technology. Small vehicle obstructions and other similar targets in the beach and littoral zones are of particular interest, because of LSNR lidar's applicability to the near-shore environment and the general desire to improve detection of antivehicle and antipersonnel obstacles in the coastal zone. A target detection procedure is presented that exploits the detailed information available from LSNR lidar data while diminishing the effect of spurious noise events. Consideration is given to detection in both topographic and bathymetric scenarios. Data sets for target detection analysis are supplied by a numerical sensor simulator developed at the University of Florida. Target detection performance is evaluated as a function of environmental characteristics, such as water clarity and depth, and system parameters, specifically transmitted pulse energy and laser pulse repetition frequency. Analysis of results with regards to consideration for future system design is discussed.