Detection scene analysis for high frequency radar

Ground penetrating radar (GPR) uses electromagnetic waves to image, locate, and identify changes in electric and magnetic properties in the ground. The received signal comprises not only the target echoes but also strong reflections from the rough, uneven ground surface, which impair subsurface inspections and visualization of buried objects. In this paper, a background clutter mitigation and target detection method using low-rank and sparse priors is proposed for GPR data. The radar signal is decomposed into the sum of a low-rank component and a sparse component, plus noise. The low-rank component captures the ground surface reflections and background clutter, whereas the sparse component contains the target reflections. The effectiveness of the proposed method is evaluated on real radar signals collected from buried landmines and improvised explosive devices. The experimental results show that the proposed method successfully removes the background clutter and estimates the target signals.

Traditionally, radar signals have been analysed in either the time or the frequency domain. Joint time–frequency representations characterise signals over a time–frequency plane. They thus combine time-domain and frequency-domain analyses to yield a potentially more revealing picture of the temporal localisation of a signal's spectral components. Therefore, for air target returns with time-varying frequency content, joint time–frequency representations offer a powerful analysis tool. A concise review of time–frequency transforms is provided as background and is needed to appreciate how time–frequency processing methods can improve conventional time or frequency processing methods. The authors use time–frequency analysis techniques for the detection of a manoeuvring aircraft using high frequency (HF) radar in heavily cluttered regions. They compare the ability of different time–frequency transforms to resolve several experimental aircraft returns. The relative speeds of the different transforms are also quantitatively studied. The results clearly demonstrate that time–frequency analysis techniques can significantly improve the detection performance of the HF radar and add considerable physical insight over what can be achieved by conventional Fourier transform methods currently used by HF radars.

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.

Northern Radar Systems Limited has built a high frequency ground wave radar at Cape Race, Newfoundland, for over-the-horizon (OTH) detection of targets. Operational experience with the radar has demonstrated that this technology has significant potential for all weather, cost-effective surveillance of large coastal areas. The radar uses a frequency modulated interrupted continuous wave (FMICW) signal, specifically developed for long range target detection. For the detection of fast targets, conventional processing is constrained by the limited time that a target is present in a range cell. This problem can be eliminated by using velocity matched filters to process the FMICW signals. For the case of manoeuvring targets, the signal processing must also take the target acceleration into account. Results of OTH detection and tracking of low-flying aircraft, including flight segments with manoeuvres, using the HF-GWR system are presented.

Recently, the usage of high frequency surface wave radars has been expanded to monitoring of ships within observable region because it can always observe wide target region with low power consumption. However, ship monitoring systems using high frequency radars suffer from the fact that a detection position is far away from true position. To resolve this problem, the proposed method determines final location by applying Bayesian estimator to detection results from each ship monitoring system in high frequency radar network. According to Bayesian theory, a posterior distribution is factorized into likelihood and prior distributions, and both distributions are modeled by using each detection results and auto-identification system data, respectively. Effectiveness of the proposed method is demonstrated through appropriate synthetic and real data. From the results, location accuracy can be improved when the proposed method is applied to location estimation.

Sustained research and development at Memorial University of Newfoundland has led to an operational High Frequency Ground Wave Radar (HF-GWR) system for coastal surveillance. This radar system has demonstrated over-the-horizon detection of targets such as vessels, ice hazards and low-flying aircraft, and performed ocean parameter measurements over a large area. The industrial developers of offshore hydrocarbon reserves in ice infested regions have an urgent requirement for the long range detection and tracking of icebergs from their production platforms. However, due to space restrictions, a rig- or ship-based system can only accommodate a compact antenna array. The uniform trajectory and low velocity of icebergs is ideal for Reciprocal Synthetic Aperture Radar (RSAR) processing with long target dwell times. The proven ice detection capability of HF-GWR systems, coupled with the compact antennas suggested by the RSAR technique, can be used to develop a rig- or ship-based all-weather surveillance device for ice hazards. It is also anticipated that the results of this research will allow the use of shorter antenna arrays for many other applications. Preliminary results using real data from the operational HF-GWR system are presented.

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.

In the process of constructing the target appearance model, the traditional HOG feature does not take into account the relationship between adjacent regions when the image is segmented in the feature extraction process, resulting in the regional aliasing effect, and the single feature is inefficient in target detection and tracking. An improved HOG-color feature fusion method for target detection and tracking is proposed. Firstly, HOG feature and color feature were extracted from the target samples respectively. During the HOG feature extraction process, trilinear interpolation was used to eliminate regional aliasing effect. Secondly, Bhattacharyya distance is used to select the HOG feature after interpolation, and the appropriate feature is selected as the HOG feature. Then combine the HOG feature after selection with the color feature; Finally, the filter is obtained by learning the kernel correlation filter, and the image is correlated detected with the filter, and the response output is obtained. The experimental results show that this method is superior to the traditional HOG-color feature fusion target detection and tracking method in both tracking speed and tracking efficiency.

Infrared (IR) point target detection and tracking is challenging due to lack of texture and detailed information of small dim targets. The problem becomes even more challenging when there is a real-time requirement too. A key goal in robust detection is to reduce missed detections (MD) and false alarms (FA). Complex traditional state-of-the-art point target detection algorithms may give accurate results but typically incur larger execution times which renders them unsuitable for real-time applications. On the other hand, deep learning methods for point target detection do not generalize satisfactorily with domain shifts. In this work, we address the problem of real-time detection and tracking of multiple point targets in diverse background conditions. We apply a ’top-hat’ operator as the first stage of our detection algorithm and this is followed by a systematic thresholding scheme to yield a good balance between MD and FA. This is followed by a methodology to find the exact target position from the detections, and a track association scheme in conjunction with Kalman filter for state estimation. Based on the proposed approach for target detection and track association, we perform 2D tracking of image coordinates as well as 3D tracking of azimuth and elevation angles. We verify the effectiveness of our method on 12 different IR image sequences over existing state-of-the-art methods in terms of accuracy as well as speed.

The existing target detection and tracking methods, as an underlying design of service robot, are highly dependent on the computing ability, which will limit the transplantation to the robot platform. To solve the problem, this paper proposes a detection and tracking system suitable for mobile robot. For the target detection, an optimization method for feature extraction is proposed, by reducing the dimensionality of feature vectors to improve detection speed. For the target tracking, a multi-scale optimization method is proposed, which can accurately obtain all the characteristics of the upper body of the target. The last section is the experiment of the integrated system, which will resist the interference and robustly dynamically tracking the target.

We propose a method for automatic target detection and tracking in forward-looking infrared (FLIR) image sequences. We use morphological connected operators to extract and track targets of interest and remove undesirable clutter. The design of these operators is based on general size, connectivity, and motion criteria, using spatial intraframe and temporal interframe information. In a first step, an image sequence is filtered on a frame-by-frame basis to remove background and residual clutter and to enhance the presence of targets. Detections extracted from the first step are passed to a second step for motion-based analysis. This step exploits the spatiotemporal correlation of the data, stated in terms of a connectivity criterion along the time dimension. The proposed method is suitable for piplined implementation or time progressive coding/transmission, since only a few frames are considered at a time. Experimental results, obtained with real FLIR image sequences, illustrating a wide variety of target and clutter variability, demonstrate the effectiveness and robustness of the proposed method.

Given the prior information of the target, hyperspectral target detection focuses on exploiting spectral differences to separate objects of interest from the background, which can be treated as information retrieval (IR) task in machine learning (ML). Most traditional detection methods work in the original feature space and rely heavily on specific assumptions, which cannot guarantee effective extraction of features for the target and background in hyperspectral images (HSIs). Mass estimation (ME) is a base modeling mechanism that has been proven to effectively solve problems in IR and is not restricted by specific assumptions. In this article, we propose a novel target detection method through ensemble-based IR with ME (EIRME). By directly deriving the ordering from a sample set to rank data points, ME provides a simple and straightforward ranking measure to ensure that points similar to the given target are far away from dissimilar points. For the estimation of mass distribution, the proposed method utilizes a tree-structured mapping to generate a feature space, in which the separability of the target and background is further improved. In particular, to break through the technical difficulty that the direct migration of IR methods with mass measure cannot specifically meet the high-precision requirements of target detection in HSIs, we develop a specialized measurement, topological mass, which innovatively combines the mass measure with tree topology to quantify the spectral difference for detection output. Moreover, the IR with ME based on parallel measurements through ensemble trees provides a robust solution with better generalization capacity and higher precision for hyperspectral target detection, facilitating practical applications. Experimental results on benchmark HSI datasets prove that the specialized measurement that we developed successfully overcomes the drawbacks of the direct migration of IR methods with ME and exhibits unique advantages. In addition, comparisons with the most classic and advanced detection algorithms demonstrate the superiority of the proposed method.

With the development of radio technology, passive bistatic radar (PBR) will suffer from interferences not only from the base station that is used as the illuminator of opportunity (BS-IoO), but also from the base station with co-frequency or adjacent frequency (BS-CF/AF). It is difficult for clutter cancellation algorithm to suppress all the interferences, especially the interferences from BS-CF/AF. The residual interferences will seriously affect target detection and DOA estimation. To solve this problem, a novel target detection and DOA estimation method for PBR based on compressed sensing sparse reconstruction is proposed. Firstly, clutter cancellation algorithm is used to suppress the interferences from BS-IoO. Secondly, the residual interferences and target echo are separated in spatial domain based on the azimuth sparse reconstruction. Finally, target detection and DOA estimation method are given. The proposed method can achieve not only target detection and DOA estimation in the presence of residual interferences, but also better anti-mainlobe interferences and high-resolution DOA estimation performance. Numerical simulation and experimental results verify the effectiveness of the proposed algorithm.

High Frequency (HF) ground wave radar is widely used in maritime target detection and marine environment observation. Its performance is often degraded by co-frequency interference of the HF band. It is necessary to detect the environmental radio frequency for a HF radar to select the proper operation frequency. In this paper, a radio frequency detecting module is designed and equipped on a double-frequency all-digital multi-input and multi-output (MIMO) HF radar. The operational frequency bands are scanned in the operation of the radar to help select an unoccupied frequency in the band. The application of this module reduced the influence of RF interference on the HF radar. The improvement in the signal quality is obvious.

A target detection method from low contrast forward looking infrared (FLIR) images is proposed. It is known that detecting small targets in remotely sensed image is difficult and challenging work. The goal is to identify target areas with small number of false alarms in a thermal infrared scene of battlefield. The proposed method consists of following three stages. First, center-surround difference with local adaptive threshold is proposed in order to find salient areas in an input image. Second, local thresholding is proposed to the local region of interest (ROf) based on the result of first step. The second step is needed to segment target silhouettes precisely. Third, the extracted binary target silhouettes are compared with target template using size and affinity to remove clutters. In the experiments, many natural infrared images with high variability are used to prove performance the proposed method. It is compared with a morphological method using receiver operating characteristic (ROC) curve and execution time. The result shows that our method is superior to the morphological method and it can be applied to automatic target recognition (ATR) system.