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

Abstract

<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; mso-layout-grid-align: none;" align="left"><span style="color: black; mso-bidi-font-weight: bold;"><span style="font-size: x-small;"><span style="font-family: Times New Roman;">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.</span></span></span></p

Punctured Difference Set Pair and Perfect Punctured Binary Sequence Pair

“…Pseudo random sequences with optimal correlation properties have been widely applied in modern communication, radar, signal processing and source coding fields [1][2][3][4][5]. The traditional sequence correlation properties are characterized by the inner product between itself and its delayed sequence.…”