Dim Moving Point Target Research Articles

Dim Moving Point Target Detection in Cloud Clutter Scenes Based on Temporal Profile Learning

This letter presents an approach for detecting dim moving point targets in cloud clutter scenes based on temporal profile learning. The main idea is that a weak transient disturbance will appear in the temporal profiles of target-present pixels, changing the temporal profile’s characteristics. We propose a novel signal-to-signal network to learn the temporal characteristics of the background and the clutter, in which the transient disturbance is extracted by the residual between the input signal and the reconstructed background and clutter signal. The structure of the ConvBlock-1D is designed to enhance the flow and propagation of features in layers. A loss function is proposed to solve the imbalance problem. We provided a comparison to other widely used methods by using simulated datasets and real-world image sequences. The experimental results demonstrate that our method has the best performance in terms of the qualitative and quantitative assessments.

A Particle Filter Based Algorithm for State Estimation of Dim Moving Point Targets in IR Image Sequence

Under the condition of the targets’ initial information is already estimated successfully, This paper presents a real-time target tracking method based on particle filter (PF) update algorithm. According to the particles' transmission characteristics and the measurements from a single frame detection, the algorithm estimate a target’s following moving state. In this way, the dim moving point target could be tracked successfully under the low signal-noise-ratio (SNR) in IR image sequence. But it comes to the multiple targets’ tracking; we should take the data integration into account. In order to reduce the amount of calculation, we use the data fusion method to divide the measurements in the overlapped parts of windows, appoints the measurements for the corresponding target. That is the Nearest Neighbor Standard Filter (NNSF) theory is used to choose the target for a measurement. The paper introduced the related theory and the concrete steps for accomplish the algorithm and also simulated the proposed tracking algorithm on the MATLAB platform. Experimental analysis and results showed that the algorithm achieved real-time, dynamic stability and robustness while track the random moving targets in high clutter environment.

Parameter Adjustment for a Dynamic Programming Track-before-Detect-Based Target Detection Algorithm

This paper addresses the problem of tracking a dim moving point target in a sequence of IR images. The proposed tracking system, based on the track-before-detect (TBD) approach, is designed to track and detect dim maneuvering targets froman image sequence under low SNR conditions; a dynamic programming algorithm (DPA) is used to process the frames in the sequence. This paper deals with the practical issues of setting the parameters of our algorithm to maximize tracking capability. In our flexible approach, we are able to accommodate different levels of noise and different target velocities and mobilities. Our algorithm is tested on real data to determine its efficacy.

Detecting and tracking dim moving point target in IR image sequence

This paper presents a novel algorithm for detecting and tracking dim moving point target in IR image sequence with low SNR. Original images are preprocessed using temperature non-linear elimination and Top-hat operator, and then a composite frame is obtained by reducing the three-dimensional (3D) spatio-temporal scanning for target to 2D spatial hunting. Finally the target trajectory is tracked under the condition of constant false-alarm probability (CFAR). Based on the experimental results, the algorithm can successfully detect dim moving point target and accurately estimate its trajectory. The algorithm, insensitive to the velocity mismatch and the changes of statistical distribution of background or noise, is adaptable to real-time target detection and tracking.