Improved mean clustering algorithm of target detection with GNSS forward-scattering radar

This paper explores the concept of a Forward Scattering Radar (FSR) system with a rotational transmitter for target detection and localization. Most of the research and development in FSR used a fixed dedicated transmitter; therefore, the detection of stationary and slow moving target is very difficult. By rotating the transmitter, the received signals at the receiver contain extra information carried by the Doppler due to the relative movement of the transmitter-target-receiver. Hence, rotating the transmitter enhances the detection capability especially for a stationary and slow-moving target. In addition, it increases the flexibility of the transmitter to control the signal direction, which broadens the coverage of FSR networks. In this paper, a novel signal processing for the new mode of FSR system based on the signal’s joint time-frequency is proposed and discussed. Additionally, the concept of the FSR system with the rotational transmitter is analyzed experimentally for the detection and localization of a stationary target, at very low speed and a low profile target crossing the FSR baseline. The system acts as a virtual fencing of a remote sensor for area monitoring. The experimental results show that the proposed mode with the new signal processing scheme can detect a human intruder. The potential applications for this system could be used for security and border surveillance, debris detection on an airport runway, ground aerial monitoring, intruder detection, etc.

Radar detection of small maritime targets is of great interest in the context of coastal and port security for prevention of activities such as smuggling and piracy. Multistatic radar and forward scatter radar offer detection advantages compared with conventional monostatic systems, such as advantageous multi-perspective target view for the former and target radar cross-section enhancement for the latter. In this study, experimental results are presented to characterise simultaneous measurements investigating the detection of a small inflatable boat by a multistatic radar and a forward scatter radar. These results are believed to be the first example of simultaneous experimental comparison of such systems.

Forward scatter radar (FSR) can achieve target detection by capturing the signal disturbance caused by the target crossing the baseline. Firstly, the FSR network based on the global navigation satellite system (GNSS) is constructed, and the basic conditions that constitute forward scattering are analyzed. Then, considering the satellite motion, a dynamic echo model is given. Finally, the influence of target parameters on the echo characteristics is analyzed by simulation. The feasibility of target detection based on GNSS-FSR is proved, and the work can provide a foundation for target detection and recognition of the FSR network.

Models of signal processing in human hearing

In this paper we consider a modified sub-optimal signal processing algorithm for marine target detection in a Forward Scatter Radar (FSR) system. The algorithm is based on a matched filtering approach where the filter parameters should be previously estimated. In contrast to the previously developed algorithms, the estimation of target parameters (time, velocity, length) is carried out by means of maximisation of the cross-correlation coefficient between frequencies of the input signal and the reference signal. The algorithm presented in this paper is verified by experimental data, and it can be also applied in some other bistatic radars. (5 pages)

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.

Parameters affecting target detection in VNIR and SWIR range

After GPS receivers being shot down for a long time, the time of booting may be too long due to the difficulty in obtaining the satellite's position. In order to solve this problem and reduce the booting time, we use the GPS satellites positions prediction in this paper to extend the useable time of GPS ephemeris such that the GPS receivers can be maintained in the condition of hot start. Instead of the curve-fitting method, Newton's equations of motion for the satellite is integrated to predict the position of the GPS satellites in a few days. Various effects, including that of the non-spherical Earth, the Solar-Lunar gravitational force, Solar radiation pressure, and attitude motion effect, are taken into account. The ephemeris data in the GPS receivers is in the frame of WGS-84, but the Newton's equation is calculated in an inertial frame. Therefore, we have to adopt the appropriate coordinate transformation between the two frames by including the effect of the precession, the nutation, and the polor motion of the Earth. The forth-order Runge Kutta method is used to solve the Newton's equation. From the numerical result, it is shown that the prediction error can be significantly reduced after those perturbations being included.

Target detection and segmentation algorithms have long been one of the main research directions in the field of computer vision, especially in the study of sea surface image understanding, these two tasks often need to consider the collaborative work at the same time, which is very high for the computing processor performance requirements. This article aims to study the deep learning sea target detection and segmentation algorithm. This paper uses wavelet transform-based filtering method for speckle noise suppression, deep learning-based method for land masking, and the target detection part uses an improved CFAR cascade algorithm. Finally, the best separable features are selected to eliminate false alarms. In order to further illustrate the feasibility of the scheme, this paper uses measured data and simulation data to verify the scheme and discusses the effect of different signal-to-noise ratio, sea target type, and attitude on the algorithm performance. The research data show that the deep learning sea target detection and segmentation algorithm has good detection performance and is generally applicable to ship target detection of different types and different attitudes. The results show that the deep learning sea target detection and segmentation algorithm fully takes into account the irregular shape and texture of the interfering target detected in the optical remote sensing image so that the accuracy rate is 32.7% higher and the efficiency is increased by about 1.3 times. The deep learning sea target detection is compared with segmentation algorithm, and it has strong target characterization ability and can be applied to ship targets of different scales.

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.

Aiming at the problem of low target detection accuracy caused by the close arrangement of targets, complex feature information, and numerous small targets in remote sensing images, this paper proposes a target detection algorithm based on the improved Yolov5 algorithm for remote sensing images. Firstly, this paper designs a remote sensing image target detection algorithm C²-YOLO based on multi-level feature fusion. Combined with the attention mechanism module to focus on the spatial location information of the target, which improves the extraction effect of the features of the target of interest. The network uses the CARAFE upsampling operator with a larger sensing field to better utilize the surrounding information. The proposal is to incorporate a predictive header hl that is generated from a low-level, high-resolution feature map to improve the recognition of tiny objects. The problem of large target scales in remote sensing images and poor detection of small targets has been improved. Angular loss is also added to the loss function to improve the detection of rotating targets. Finally, the accuracy comparison and experimental effect visualization are carried out on the dataset, and the experimental results show that the improvement work in this paper achieves better results no matter comparing the original YOLOv5 detection algorithm or other types of detection algorithms. Specifically, the accuracy of C²-YOLO on the NWPU dataset 1-shot recognition task is 78.45%, which is much higher than that of other detection algorithms, and is able to reach a superior performance in the remote sensing image target detection task.

Aiming at maritime infrared target detection with low contrast influenced by maritime clutter and illumination, this paper proposes a Modified Histogram Equalization with Edge Fusion (MHEEF) pre-processing algorithm in backlight maritime scenes and establishes Local-Contrast Saliency Models with Double Scale and Modes (LCMDSM) for detecting a target with the properties of positive and negative contrast. We propose a local-contrast saliency mathematical model with double modes in the extension of only one mode. Then, the big scale and small scale are combined into one Target Detection Unit (TDU), which can approach the “from bottom to up” mechanism of the Visual Attention Model (VAM) better and identify the target with a suitable size, approaching the target’s actual shape. In the experimental results and analysis, clutter, foggy, backlight, and dim maritime scenes are chosen to verify the effectiveness of the target detection algorithm. From the enhancement result, the LCMDSM algorithm can achieve a Detection Rate (DR) with a value of 98.26% under each maritime scene on the average level and can be used in real-time detection with low computational cost.

ABSTRACTComplex processing algorithms associated with requirements for real‐time target detection, acquisition and recognition have far outdistanced our ability to package the necessary processing power into real‐time weapon‐sized hardware. Weapon developers need new, radically different, approaches to solve these difficult weapon signal processing problems. Artificial neural networks (ANN) is one of the new, radically different, signal processing approaches that is currently receiving much attention for weapon applications. The human brain is a powerful image and pattern recognition processor whose basic processing element is the neuron. Synapses are the weighted interconnections between neurons that permit learning and communication between the neurons. Artificial neural network technology attempts to mathematically and/or electrically model neurons and synapses and then interconnect these models in architectures suitable for signal processing tasks.ANN technology is particularly applicable to pattern recognition, speech recognition, machine vision, robotics, and optimization signal processing tasks. Specific military applications include missile seekers, missile fuzing, sonar target discrimination, automatic target recognition, and autopilots. Two unique characteristics of ANN processors are that they are non‐linear processors and that they are trained, not programmed, to accomplish processing tasks in a manner analogous to the way the human brain learns. Learning is achieved by modifying the synaptic weights of each artificial neuron until the final desired system processing response is achieved.Several years ago the Naval Weapons Center (NavWpnCen) identified ANN technology as a high risk‐high payoff approach to missile signal processing requirements and initiated several research and development efforts in this area. One result of this work is the new 80170NW ANN analog VLSI chip produced by Intel Inc. Another is the current Missileborne Integrated Neural Network Demonstration (MINND) Advanced Technology Demonstration (ATD) project to develop a hybrid analog/digital artificial neural computer for real‐time processing of data from imaging missile seekers. The neural computer will use several Intel chips interconnected in a flexible architecture, which is loosely based on the operation of the human retina, to demonstrate desired target/background discrimination and target detection and tracking processing capabilities. This paper provides an introduction to artificial neural network technology and discusses some of the results that have been achieved by applying ANN technology to weapon system problems at the Naval Weapons Center.

Recently target detection is widely regarded as a typical hot spot in research of sensor networks. A fast target detection algorithm is proposed by using the hypothesis testing (HT) method in the paper. The objective is to determine whether a target is present in a sensor network for decision-makers. Due to the nature of sensor networks, it is desirable to have a fast algorithm to accomplish the detection and judgment process with low computation cost on a distributed network end node. In the paper mobile target detection is formulated as a statistical inference problem according to the mathematical statistics theory. Moreover, a data fusion process with several sensors is also designed to optimize the final decision result for detection synthesis. It has the advantage of low computational complexity, good performance of real time, and yields high target detection correctness. Numerical experiments are used to demonstrate the efficiency of the HT detection algorithm, where magnetic sensors are applied to collect the output signal from an undetermined target.

Reliance on Automated Target Recognition (ATR) technology is essential to the future success of Intelligence, Surveillance, and Reconnaissance (ISR) missions. Although benefits may be realized through ATR processing of a single data source, fusion of information across multiple images and multiple sensors promises significant performance gains. A major challenge, as ATR fusion technologies mature, is the establishment of sound methods for evaluating ATR performance in the context of data fusion. The Deputy Under Secretary of Defense for Science and Technology (DUSD/S&T), as part of their ongoing ATR Program, has sponsored an effort to develop and demonstrate methods for evaluating ATR algorithms that utilize multiple data source, i.e., fusion-based ATR. This paper presents results from this program, focusing on the target detection and cueing aspect of the problem. The first step in assessing target detection performance is to relate the ground truth to the ATR decisions. Once the ATR decisions have been mapped to ground truth, the second step in the evaluation is to characterize ATR performance. A common approach is to vary the confidence threshold of the ATR and compute the Probability of Detection (PD) and the False Alarm Rate (FAR) associated with each threshold. Varying the threshold, therefore, produces an empirical performance curve relating detection performance to false alarms. Various statistical methods have been developed, largely in the medical imaging literature, to model this curve so that statistical inferences are possible. One approach, based on signal detection theory, generalizes the Receiver Operator Characteristic (ROC) curve. Under this approach, the Free Response Operating Characteristic (FROC) curve models performance for search problems. The FROC model is appropriate when multiple detections are possible and the number of false alarms is unconstrained. The parameterization of the FROC model provides a natural method for characterizing both the operational environment and the ability of the ATR algorithm to detect targets. One parameter of the FROC model indicates the complexity of the clutter by characterizing the propensity for false alarms. The second parameter quantifies the separability between clutter and targets. Thus, the FROC model provides a framework for modeling and predicting ATR performance in multiple environments. This paper presents the FROC model for single sensor data and generalizes the model to handle the fusion case.