An improved particle filter for dim radar target detection and tracking

With the rapid development of information technology in inland waterway, radar was widely used to track the ship running, in order to ensure the safe and efficient shipping industry development, as well as to avoid a ship collision causing unnecessary economic losses. For the problem of radar target tracking data associated, a method of data association with radar data was proposed, which was based on radar target course and speed. By this method, the target error or loss problem was solved when the target density was large and used belief rule-based (BRB) methodology to verify the reliability of this method. Real target points obtained by this method had been filtered through particle filter. And then the radar target tracking was achieved. This method has a good target tracking results, verified by Monte Carlo simulations.

Vision-based collision avoidance system for air traffic management requires a excellent multiple target detection algorithm under low signal-to-noise ratio (SNR) levels. The track-before-detect (TBD) approaches have significant applications such as detection of small and dim targets from an image sequence. In this paper, two detection algorithms with the TBD approaches are proposed to satisfy the multiple target detection requirements. The first algorithm, based on a dynamic programming approach, is designed to classify multiple targets by using a k-means clustering algorithm. In the second approach, a hidden Markov model (HMM) is slightly modified for detecting multiple targets sequentially. Both of the proposed approaches are used in numerical simulations with variations in target appearance properties to provide satisfactory performance as multiple target detection methods.

This paper deals with the weak target detection and tracking for airborne radar in compound Gaussian with inverse Gaussian texture (IGCG) distribution sea clutter. Combine with the characteristics of IGCG distribution clutter and the airborne radar, a track-before-detect algorithm based on dynamic programming (DP-TBD) is proposed in this paper. In this algorithm, the multi-frame test statistic based on generalized likelihood ratio test (GLRT) under the track-before-detect (TB-D) framework is derived, and then the dynamic programming (DP) algorithm is used to give the specific realization method for detecting and tracking target in the range-azimuth-doppler domain. Compared with the traditional algorithms, simulation results show that the proposed algorithm can effectively improve the detection and tracking performance for airborne radar in IGCG distribution clutter.

This paper mainly addresses the maneuvering targets detection and tracking problem in pulse Doppler radar using Dynamic Programming (DP) based Track-Before-Detect (TBD). Generally, the DP-TBD algorithm implicitly embeds the tracking stage into its framework using a predefined target model to determine the admissible searching paths. However, its performance will suffer severe loss due to the mismatching of target model when maneuver occurs. In this paper, to handle the target maneuvers, an improved DP-TBD algorithm is proposed, wherein the hidden state variable of radial acceleration is recursively estimated to help scheme the DP searching paths in a more appropriate way. Meantime, by employing the Current Statistical (CS) model, the DP integration process is modified which can adaptively adjust the searching region to avoid the interference of many noisy states involved by some impractical paths. Simulation results show that the proposed algorithm can effectively improve the detection and tracking performance of DP-TBD.

An optimal nonlinear Bayesian TBD (track-before-detect) algorithm based on particle filter for dim targets detection and tracking in cluttered background is proposed. To make full use of the targets a priori information, a table of possible target movements and their transition probabilities among these base states are introduced. Furthermore, the TBD technology is exploited for the target detection in cluttered image sequences with low SNR. Here particle filter is provided to implement the Bayesian regression and estimate the target's state model at each step.

A novel particle filter for multiple target tracking with track-before-detect measurement models is proposed. Particle filters efficiently perform target tracking under nonlinear or non-Gaussian models. However, their application to multiple target tracking suffers from the curse of dimensionality. We introduce an efficient particle filter for multiple target tracking which deals with the curse of dimensionality better than previously developed methods. The proposed algorithm is tested and compared to other multiple target tracking particle filters.

“Track-before-detection” (TBD) is well suitable for radar detection of dim targets because it takes advantage of the gains of time integration. Dynamic programming algorithm (DPA) is one of the traditional TBD techniques, and has been applied to sea dim target detection. In this paper, the expression of DPA applied to air dim target detection is given firstly. Important differences between air dim target and sea dim target is introduced in the application of DPA. In the end, numerical simulations show that air dim target detection based on DPA is limited in radar performance enhancement. (3 pages)

The performance of ballistic target interception is critically dependent on the performance of the target state estimation. The estimation performance then strongly depends on the accuracy of the measurement model. The Gaussian uncertainty distribution has commonly been used for representing the statistical properties of sensor noise, due to its mathematical simplicity and effectiveness. However, seeker sensor measurements are often corrupted by glint noise which is highly non-Gaussian, and conventional Gaussian filtering algorithms are known to show unsatisfactory performance in the presence of glint noise. This research proposes the use of a particle filter for ballistic target tracking in a glint noise environment. The target tracking performance of the particle filter is compared with that of the extended Kalman filter.

Adaptive data processing in modern radar systems is standard approach. The classic kalman filter, extended kalman filter and many other applications are usually used for state estimate and radar target tracking. Signal and data processing technology improvement allows the more effective target tracking in real time. In this paper, we approach the structure and key features of the optimal particle filtering (PF). The optimization criteria for PF are proposed and analyzed. The dependence of mean square error on the number of particles during track prediction by particle filter is discussed. Using this dependence, we were able to determine the minimum required number of particles to correctly predict the trajectory of the target depending on its dynamics (trajectory complexity). The results of off-line real radar data processing are analyzed.

In the field of infrared remote sensing, the problem of IR small target detection is still an important component part. Concerning infrared dim small target (IRDST) detection, firstly the infrared image is processed with DWT method to get the wavelet coefficients image, but the distribution characteristics, such as scales, frequencies, orientations of wavelet coefficients in different sub-bands are various, so wavelet image denoised by a single threshold criterion can not give a satisfying estimation. Based on this motivation, a spatially adaptive multi-model de-noising strategy (SAMMDS) based IRDST detection method is proposed in this paper, which can adjust thresholding strategy according to the distribution of noise in different scales and directions. Spatially adaptive BayesShrink (SABS) thresholding, traditional BayesShrink (BS) thresholding and generalized cross validation (GCV) thresholding are all adopted here to process each sub-band separately. After reconstructing the denoised wavelet image, a simple global thresholding is used to separate the background and target finally. Experimental results demonstrate that the proposed algorithm performs better than other typical wavelet methods for small target detection with various complex backgrounds.

Camera arrays can enhance the signal-to-noise ratio (SNR) between dim targets and backgrounds through multi-view synthesis. This is crucial for the detection of dim targets. To this end, we design and develop an infrared camera array system with a large baseline. The multi-view synthesis of camera arrays relies heavily on the calibration accuracy of relative poses in the sub-cameras. However, the sub-cameras within a camera array lack strict geometric constraints. Therefore, most current calibration methods still consider the camera array as multiple pinhole cameras for calibration. Moreover, when detecting distant targets, the camera array usually needs to adjust the focal length to maintain a larger depth of field (DoF), so that the distant targets are located on the camera’s focal plane. This means that the calibration scene should be selected within this DoF range to obtain clear images. Nevertheless, the small parallax between the distant sub-aperture views limits the calibration. To address these issues, we propose a calibration model for camera arrays in distant scenes. In this model, we first extend the parallax by employing dual-array frames (i.e., recording a scene at two spatial locations). Secondly, we investigate the linear constraints between the dual-array frames, to maintain the minimum degrees of freedom of the model. We develop a real-world light field dataset called NUDT-Dual-Array using an infrared camera array to evaluate our method. Experimental results on our self-developed datasets demonstrate the effectiveness of our method. Using the calibrated model, we improve the SNR of distant dim targets, which ultimately enhances the detection and perception of dim targets.

Improving the reliability and performance of target detection and tracking in multi-field environments is one of the key areas for moving target detection and tracking. At present, the detection and tracking of moving targets are one of the difficult research hotspots in the field of image processing. Due to the limited detection capabilities of object detectors such as vehicles, they often fail to alert in the event of an accident, especially in open areas. Aiming at the problem of target detection and tracking in a large space environment, several target detections and tracking algorithms combined with multiple image features are proposed. This paper discusses several methods for detecting moving targets. The algorithm is based on a variety of visual features of the target in the image, extracting a small number of features to detect the existence of the target. Kalman filtering and particle filtering are tools for accurately estimating data values. Simulation results show the effectiveness of these methods.

Research on Infrared Dim and Small Target Detection Algorithm Based on Low-Rank Tensor Recovery

RADAR plays a crucial role in target tracking. A faulty tracking is vulnerable to fatal errors. Since RADAR signals contain noise in them, filters are used to remove the noise. This paper implements Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Cubature Kalman Filter (CKF) for radar target tracking. Each filter is exposed to the same environment and the results are observed. A radar target tracking model is proposed with an input chirp signal of 77 GHz. The turn rate is varied between 0.2 and 150. The simulations are carried out in MATLAB. Since every simulation generates random motions of the target, the filter can be gauged only on the basis of how well the estimated trajectory follows the true trajectory. The findings have shown that EKF works best for large turn rate and high noise, in comparison to UKF and CKF, thus making it an appropriate choice for such applications.

Data fusion algorithms are inevitable in improving the accuracy and reliability of radar target tracking. Considering the shortcomings of target tracking in Cartesian coordinates, all the fusion process are carried out in spherical coordinate system. To fuse data from bidimensional radar and Infrared Search and Track (IRST), five data fusion methods, Bayesian fusion, Signal variance based fusion, Optimal fusion, Error variance based fusion and Extended Kalman Filter (EKF) based fusion are proposed and analyzed. The performances of the methods are analyzed in terms of Root Mean Square Error in Position (RMSPE) and Mean Absolute Error in Position (MAPE) for a wide range of measurement noise variances. It is concluded that EKF based fusion has the best error performance. Kalman Filter State-Vector Fusion (KF-SVF) which is a hierarchical fusion architecture, having first level of fusion with Kalman filter and second level with error variance based fusion method, is available in the literature. In this work it has been expanded to Extended Kalman Filter—State-Vector Fusion (EKF-SVF) with either error variance based fusion, signal variance based fusion or optimal information fusion as second level. All the three EKF-SVF methods have similar error characteristics, but EKF-SVF with signal variance based fusion, unlike the other two, provides less computational cost since it does not involve matrix inversion. While comparing EKF based fusion and EKF-SVF, EKF-SVF offers increased error minimization.