A Novel Track -Before-Detect Algorithm for Airborne Target with Over-The-Horizon Radar

Range walk of the fourth generation fighters echo for traditional radar signal processing is a serious problem in the long coherent integration time. To solve the problem, a novel coherent Radon transform based weak target detection algorithm for metric-band phased array radar is proposed. First the echo signal model of supersonic weak target is provided, and the characteristic of range walk is analyzed. Based on this, the coherent Radon transform based long term coherent integration algorithm along the range walk line is presented. Finally, the performance of target detection and parameter estimation of the algorithm are analyzed by the simulation experiments, which suggest that the proposed algorithm has better detection performance compared with Hough transform method and MTD method. (5 pages)

In recent years, with the progress of society and the development of the economy, the number of cars in China has been increasing. In wireless communication networks, the choice of wireless nodes has a greater impact on the improvement of system performance (such as channel capacity, coverage area, etc.). The intelligent vehicle target detection system can perceive and recognize the surrounding objects such as pedestrians and vehicles through sensors, which is the basis for realizing the unmanned driving of intelligent vehicles. In a wireless environment where multiple wireless nodes coexist, current research focuses on how long it takes to re-plan the selection of wireless nodes (for mobile environments) and how to allocate and manage wireless nodes, such as where under the conditions of wireless, who will wireless with whom, and in what way (such as auto focus, digital fusion, or other) wireless, etc.; the dedicated centralized controller (such as the base station of the infrastructure-based wireless access network) determines the wireless partner (i.e., centralized type selection), or the wireless node decides by itself (i.e., distributed selection); select the appropriate number of wireless nodes to take into account the system performance gain and implementation complexity. The popularity of automobiles has brought great convenience to people's lives, but it has also brought greater traffic pressure. At present, the amount of domestic automobile traffic has increased exponentially, and urban traffic congestion has become more serious, and even caused serious traffic accidents, directly affecting people's quality of life. The emergence of intelligent transportation systems has effectively alleviated road traffic pressure and reduced the incidence of traffic accidents. As an important part of the intelligent transportation system, the research of intelligent vehicles has received extensive attention. Intelligent vehicles use a variety of sensors installed on the body to sense the environment, and realize intelligent driving functions such as lane line detection, obstacle detection, dynamic cruise control, and unmanned driving, which is conducive to reducing the incidence of traffic accidents and improving the safety of vehicle driving. At present, in the research of target recognition and tracking of intelligent vehicles, the traditional target detection method is mainly based on artificial feature extraction, which is difficult to describe more complex or higher-order image features, and the tracking effect is not good, which limits the target detection. An intelligent vehicle is an intelligent system with functions such as environment perception, planning decision-making, and operation control, and is an important part of the intelligent transportation system. Identify the effect. In response to these problems, we present a study of the target recognition and tracking of intelligent vehicles based on grid map and lidar sensor technology. To verify the proposed method in the actual scene, we identify and track the vehicle ahead on the road through a car equipped with a lidar sensor. The meaning of target detection: target detection, also called target extraction, is an image segmentation based on target geometric and statistical features. The results show that the proposed research method can accurately identify the vehicle in front and detect the moving targets such as pedestrians. The tracking trajectory is also consistent with the expected route. The results show that the laser radar-based vehicle target recognition and tracking in this study is effective. In the 10 frames of images, the average time taken for the recognition of one frame of image is 9.8 ms, while the traditional method is 12.9 ms. Technology can improve reliable road information for intelligent vehicle driving and improve the transportation efficiency and safety performance of intelligent vehicles. There are two main target detection methods for lidar: detection methods based on feature extraction and detection methods based on grid maps.

Auto ship detection for high frequency (HF) skywave over-the-horizon (OTH) radar, being a valuable application, has not been achieved virtually within short coherent integration times (CITs). Because the ships' lines in the spectrum can be covered by the ocean clutter's easily as a result of the poor resolution, which attenuates the validities of ocean clutter suppression algorithms. Besides, when the backscatter echo of ocean clutter becomes complicated, which can often be caused by the complex environmental factors, the performance of ocean clutter suppression algorithms also degrades. To achieve auto ship detection within short CITs, a scheme employing algorithms including Orthogonal Matching Pursuit (OMP), eigenvalue-decomposition (EVD) and constant false alarm rate (CFAR) processor, called OEC scheme which is the acronym for the methods applied, is proposed in this paper. Simulations approves the validation of the scheme.

Both visible and infrared images are important sources of intelligence information on the battlefield, and air‐to‐ground reconnaissance by UAV is an important means to obtain intelligence. However, there are great challenges in ground target detection and tracking, especially in complex battlefield environments. Aiming at the problem of insufficient accuracy of target detection by a single type of sensor in the battlefield environment at this stage, a target detection method by fusion of visible and infrared images is proposed in this paper, which is called ReconnaissanceFusion‐YOLO (RF‐YOLO), and with the help of infrared imagery, it can effectively improve the accuracy of target detection in the case of insufficient light. The performance of target detection in the battlefield is significantly improved by introducing two key innovative modules: dual feature fusion (DFF) module and feature fusion corrector (FFC) module. The DFF module enhances multi‐channel feature fusion through a novel concatenation and channel‐wise attention mechanism, while the FFC module performs feature correction between parallel streams using spatial and channel‐wise weights, addressing noise and uncertainty in different modalities. These modules are integrated on top of a dual‐stream YOLO architecture, allowing for effective fusion of visible and infrared information. RF‐YOLO was trained and evaluated using the FLIR data set, containing 5142 pairs of strictly aligned visible and infrared images. Results demonstrate that RF‐YOLO significantly outperforms benchmark networks in terms of robustness requirements. Specifically, the large model of RF‐YOLO achieves an mAP of 0.831, which is a significant improvement compared to the YOLOv5l inf benchmark's 0.739. This represents an improvement of over 12% in detection accuracy. Additionally, RF‐YOLO offers a Nano version that balances accuracy and speed. The Nano version achieves an mAP of 0.765, while maintaining a model size of only 11.5 MB, making it suitable for deployment on UAV edge computing devices with limited resources. To validate the practical applicability of our approach, this paper successfully implements target detection and tracking on a real UAV's edge computing device using the ROS system and SiameseRPN, combined with the proposed RF‐YOLO. Real‐world flight tests were conducted on an internal playground, demonstrating the effectiveness of our method in actual UAV applications. The system achieved a processing rate of approximately 10 fps at 640 × 640 resolution on an NVIDIA TX2 edge computing device, showcasing its real‐time performance capability in practical scenarios. This study contributes to enhancing UAV‐based battlefield reconnaissance capabilities by improving the accuracy and robustness of target detection and tracking in complex environments. The proposed RF‐YOLO method, along with its successful implementation on a real UAV platform, provides a promising solution for advanced military intelligence gathering and decision‐making support.

Multiple pulse repetition frequency (multi-PRF) radar is widely utilized due to its advantages in range-Doppler ambiguity suppression and anti-jamming capabilities. Most existing research focuses on intra-frame multi-PRF mode, which lacks flexibility in PRF scheduling and robustness against clutter and dynamic targets. In contrast, inter-frame multi-PRF mode allows PRF variation across frames, enhancing ambiguity resolution and target detection performance in complex environments. However, research on detecting and tracking ambiguous targets under the inter-frame multi-PRF mode remains limited. To address this gap, we propose a multi-frame track-before-detect (MF-TBD) and fusion disambiguation method based on inter-frame multiPRF radar systems, referred to as IF-MF-TBD. First, an efficient method for constructing a pseudo-measurement plane is proposed to mitigate the computational complexity of ambiguity resolution. Then, to estimate the target's ambiguous state across different PRFs, intra-PRF multi-frame energy accumulation is performed within the pseudo-measurement plane, generating multiple sets of ambiguous plot-sequences. Finally, ambiguity resolution is achieved through plot-sequences association and fusion method, yielding unambiguous target tracking results. Simulation results demonstrate that the proposed IF-MF-TBD algorithm effectively enhances target detection and tracking performance in interframe multi-PRF radar systems.

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.

As a key technology of detection and tracking system, moving target detection and tracking has attracted a great deal of attention. To achieve the goal of tracking target, conventional methods have to complete the detection of targets firstly, and then track the target. However, such kind of approaches require using the method of constant false-alarm rate to detect the whole frame image received at that time, which not only reduces the detection efficiency but also degrades the performance of target tracking. In order to implement real-time monitoring divers and other small moving targets under the conditions of strong underwater noise and clutter interference, an interactive algorithm for moving target detection and tracking is proposed based on acoustic image, which jointly analyzes the target detection system and the target tracking system. In the target tracking unit, the interactive algorithm takes advantage of data interconnection to remove the false targets produced by target detection; extrapolates the target's track, and predicts the location that the target may appear at the next moment by employing the Kalman filter. These messages will be used as priori information in the detection unit. In the detection unit, first of all, a gate is established whose center is the position that the target tracking unit predicted. For the image received at the next moment, detection in the predicted gate in implemented by using the method of constant false-alarm rate, followed by feeding back the detection results to the tracking unit. Compared with existed methods, the proposed algorithm can effectively improve the target detection efficiency and tracking performance, providing a better approach to monitor underwater small targets.

Target detection and tracking is a key technology in intelligent monitoring system. However, it is still difficult to design a robust, accurate and real-time target detection and tracking algorithm. Taking target Tracking as the research focus, this paper focuses on one of the current mainstream algorithms, namely, TLD (tracking-learning-detection) target Tracking algorithm. Based on the theory of TLD target tracking and corner detection, a new target tracking method was proposed to solve the existing problems. A classifier is built online to track and learn the target in real time, and a target contour detector is added in the system to complement the random forest detector to solve the problem of tracking failure. Experimental results show that the proposed algorithm can effectively overcome the influence of fast change or depth occlusion, and improve the tracking efficiency of moving targets.

Differential Coherent Integration method is an effective method for weak GPS acquisition. But the traditional differential method has a problem that the coherent integration time could not be too long and can hardly acquire very weak GPS signal. This paper introduces an improved differential coherent integration algorithm based on optimum path method, which can find the path of the maximum sum of differential blocks by estimating every possible product of the adjacent navigation data bits. So it could overcome the influence of navigation data bit transition and the long time coherent integration can be used in the differential method. Another advantage of this new method is that the loss of the square is reduced, because of the differential operation. Simulations show that, in the weak signal environment, if the same C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> and processing data length are provided, the performance of the new method is much better than the legacy half-bits method and full-bits method. With the increasing of the processing data length, the advantage of the new method on signal to noise ratio improvement is more significant.

A dynamic programming (DP) technique has been developed for the detection of subpixel-sized, low-SNR targets observed by mosaic imaging sensors. The primary advantages of DP are its sensitivity to weak targets along with its robustness to target maneuvers and sensor instabilities. Such enhancements are achieved by performing target data association and detection in a single optimization procedure. To the extent that the target motion and noise behavior can be accurately modeled, the DP algorithm is shown to be optimal for this task. A prototype IR tracking system based on the DP approach was developed and tested for a step-staring IR camera application. Performance analysis indicates a sensitivity improvement of several dB over conventional sequential detection tracking approaches.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An elliptical Hough transform (EHT) algorithm is proposed in the framework of track-before-detect (TBD) for joint detection and tracking of weak exoatmospheric targets. The new approach exploits the fact that when restricted to a two-body problem, the exoatmospheric target often follows an elliptical orbit, and thus the Hough transform integrated with orbital geometry information would have better detection performance. The relationship between the original radar measurements in data space and the elliptical parameters in parameter space is explicitly derived with multiple steps of coordinate transformation. It is found that the data points mapping into the parameter space essentially represent a quartic curve. An EHT-based algorithm is then designed, and orbit planarity is also taken into account to reduce the effect of noise accumulation. The influences of primary and secondary thresholds and the signal-to-noise ratio (SNR) on the detection performance are compared by simulations. Additionally, a real radar tracking dataset from a scientific satellite on 28 May 2017 is used to investigate the efficiency of the method. By adding some imaginary clutter to the raw orbit, the results indicate that it is very effective in detecting the real satellite trajectory in a low signal-to-noise ratio (SNR) environment. The advantage of the new method lies in it can not only simultaneously detect and track weak exoatmospheric targets but also can predict the trajectory by using these available detected parameters.

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.

In our previous papers, the FPGA-based processing package and the co-processor board have been introduced for numerous commercial and military applications including motion detection, optical flow, background velocimetry, and target tracking. The processing package is being continually upgraded by new point- and area-applied algorithms for a variety of real-time digital video camera systems including foveal sensors based on Nova's <b>V</b>ariable <b>A</b>cuity <b>S</b>uperpixel <b>I</b>mager (VASI^TM) and <b>L</b>arge <b>F</b>ormat VASI^TM (LVASI^TM) technologies. This paper demonstrates the FPGA-based processor for high frame-rate target detection in a cluttered background using variable acuity sensors. For the 1024 x 1024 pixel LVASI^TM Focal Plane Array (FPA), the proposed target-detection algorithm increases the frame rate from 4 Hz for the full resolution mode up to 450 Hz for the foveal mode while maintaining full field of view and target-detection performances on cluttered backgrounds that are comparable with detection performances at the full resolution mode.

The detection and tracking of dim targets in complex environments show great importance in radar systems. The track-before-detect (TBD) technology has been widely researched in the scenario where the target signal-to-noise ratio (SNR) is low, however, there has been no suitable solution to the trade-off between tracking accuracy and computational complexity during the process of multi-frame joint detection in radar systems. In this paper, we propose an efficient two-stage based multi-frame detection and tracking algorithm in radar systems. The proposed algorithm consists of a low threshold pre-processing stage, and a TBD processor, which searches possible target tracks from multiple scans and declares the final estimated tracks. The proposed algorithm provides an accurate evolution of target states over time in polar coordinates to avoid the performance loss and model mismatch due to the nonlinear conversion in mixed coordinates. In addition, we further propose a greedy-based recursive algorithm to implement fast track formation from the over-threshold multi-frame measurement points. Simulation results show that the proposed method achieves a better detection and tracking performance with a low computational complexity.