Target Detection Performance Research Articles

Ultra-Low Sidelobe Waveforms Design for LPI Radar Based on Joint Complementary Phase-Coding and Optimized Discrete Frequency-Coding

In this paper, in order to reduce the probability of the radar waveform intercepted by the passive detection system, the time-bandwidth product of the radar waveform is increased, and the detection probability of the radar waveform to the target is improved. This paper tackles the holographic RF stealth radar and proposes a joint coding waveform based on the linear frequency modulation (LFM) waveform. Joint coding uses complementary codes to perform phase-coding, and combines the codewords optimized by genetic algorithm in order to perform discrete frequency-coding waveform. The joint coding waveform model is theoretically analyzed, and the ambiguity function, pulse compression and target detection probability of the joint coding waveform are obtained by numerical simulation. In addition, the complexity of the algorithm and the low probability of intercept (LPI) characteristic of the joint coding waveform are analyzed. The results show that the joint coding waveform has an approximate “pushpin” ambiguity function, ultra-low sidelobe characteristics, better RF stealth and target detection performance. Finally, it has good application prospects in the current battlefield environment.

Requirement Analysis and Teardrop-Based Design of High Antenna Isolation for FMCW Radar

Frequency-modulated continuous wave (FMCW) radar is widely used in automotive and consumer electronics because of its range, velocity, and angle measurement functionality. In an FMCW radar system, the isolation between transmitting (Tx) and receiving (Rx) subsystems affects the sensitivity of the FMCW system, which directly impacts the system’s overall performance in target detection. The factors that affect system performance include transmitter-to-receiver on-chip coupling and Tx-to-Rx antenna coupling. The on-chip isolation performance is basically fixed once a radar chip is given, but the antenna isolation performance depends on a designed antenna array. Usually, a targeted antenna requirement is first specified, and then the corresponding Tx and Rx antenna array is designed. However, there is no general principle or criteria for specifying a proper antenna isolation requirement in the existing research. In this paper, first, we reveal that the antenna isolation requirement should be set to be almost the same as the given on-chip isolation value, which is very significant as a general guideline in setting a targeted antenna isolation requirement. All current antenna isolation methods cannot reach the level of on-chip isolation in a compactly designed radar system. We further propose a teardrop-based method to provide high antenna isolation. The principle of an antenna isolation requirement and a novel antenna design using teardrops are both analyzed and demonstrated based on a representative 24 GHz FMCW radar. Our teardrop-shaped structure in the mouth of the conventional Vivaldi antenna achieved greater than 50 dB isolation, while the distance between the Tx and Rx antennas could be reduced to 2.1 mm.

Parameter Estimation for Sea Clutter Pareto Distribution Model Based on Variable Interval

The generalized Pareto (GP) distribution model is often used to describe the amplitude statistical feature of sea clutter. Generally, the parameters of GP distribution are estimated by moments estimators. However, when the sea state is high, the appearance of sea spikes will increase the echo of the anomalous scattering units, which leads to a decrease in the parameter estimation accuracy and target detection performance. To improve the parameter estimation accuracy, this paper proposes a novel parameter estimation method based on variable intervals. Considering the local properties of sea clutter, we take a variable interval of the entire sea clutter series for parameter estimation, where the interval position is selected according to the sea state conditions. For contrast, the bipercentile parameter estimation and truncate moment estimation are also introduced. Finally, the experiment based on the real measured X-band sea clutter datasets indicates that the proposed method outperforms the state-of-the-art moments estimators.

First-Order Ocean Surface Cross Section for Shipborne Bistatic HFSWR: Derivation and Simulation

A bistatic high-frequency surface wave radar (HFSWR) with both receiving and transmitting stations placed on different ships (platforms) is a new radar system and referred to as shipborne bistatic HFSWR. In this paper, a first-order ocean surface cross section of shipborne bistatic HFSWR was derived. The first-order cross-section models for three different cases, i.e., ships moving with uniform, periodic, and hybrid motion states, respectively, are presented. The corresponding first-order Doppler spectra were simulated, and the spread width of the first-order spectrum was investigated. The simulation results show that the characteristics of the first-order spectrum are similar to those of a shore-based bistatic HFSWR when the transmitting and receiving platforms move in opposite directions. The first-order spectral spread width in the case of platforms with opposite directions is much smaller than that in the case of platforms with the same direction. This finding is useful for reducing HFSWR first-order spectrum spread due to platform motion, thus improving the target detection performance of the shipborne bistatic HFSWR. In addition, periodic oscillation motion of both platforms will cause complex motion-induced peaks in the first-order spectrum, which may be detrimental to target detection and ocean remote sensing. These results have important implications for the application of shipborne bistatic HFSWR.

Low Observable Radar Target Detection Method within Sea Clutter Based on Correlation Estimation

The long-time coherent integration can effectively improve the detection ability of radar targets. However, this strategy usually shows poor effect in resisting the sea clutter, which produces difficulties for accurate estimation of sea clutter characteristics and results in the inability to differentiate between the target and sea clutter. To solve this problem, a two-stage method is proposed, which consists of the sea clutter suppression stage and target decision stage. In the sea clutter suppression stage, the correlation time differences in the time and the space domains are adopted to estimate the correlation of sea clutter. Then, a selective whitening filter is proposed, which is performed more adaptively according to the estimation results. In the decision stage, the peak average ratio in the fractional Fourier domain (FRFT-PAR) is presented, which can make better use of the energy accumulation characteristics and further suppress the interference of sea clutter. Experiments on the IPIX datasets with various observation times and polarization modes are included. The results indicate that the proposed method could not only effectively suppress sea clutter but also achieve better target detection performance than baseline algorithms.

Multi-UAV cooperative system for search and rescue based on YOLOv5

Wilderness search and rescue (WiSAR) usually need to search a large and complex area and has high speed requirements. When an unmanned aerial vehicle (UAV) is used for auxiliary operation, it usually needs to carry a variety of sensors, and it is difficult to ensure real-time performance. In recent years, through the continuous development of target detection algorithms based on deep learning, the accuracy and real-time performance of target detection for visible images are getting better. This study aims to shorten the time of search and rescue (SAR) and improve the success rate by using UAV and target detection technology. In this study, a multi-UAV cooperative system for SAR based on YOLOv5 is proposed. The system has these functions of free-grafting multiple UAVs, independent control of each UAV, real-time target detection, monocular positioning. The performance of this system is tested through several simulated search and rescue missions, and the results prove that the system can meet the requirements of search and rescue operations.

A Deep Neural Network Based on Circular Representation for Target Detection

Convolutional neural network (CNN) model based on deep learning has excellent performance for target detection. However, the detection effect is poor when the object is circular or tubular because most of the existing object detection methods are based on the traditional rectangular box to detect and recognize objects. To solve the problem, we propose the circular representation structure and RepVGG module on the basis of CenterNet and expand the network prediction structure, thus proposing a high-precision and high-efficiency lightweight circular object detection method RebarDet. Specifically, circular tubular type objects will be optimized by replacing the traditional rectangular box with a circular box. Second, we improve the resolution of the network feature map and the upper limit of the number of objects detected in a single detect to achieve the expansion of the network prediction structure, optimized for the dense phenomenon that often occurs in circular tubular objects. Finally, the multibranch topology of RepVGG is introduced to sum the feature information extracted by different convolution modules, which improves the ability of the convolution module to extract information. We conducted extensive experiments on rebar datasets and used AB-Score as a new evaluation method to evaluate RebarDet. The experimental results show that RebarDet can achieve a detection accuracy of up to 0.8114 and a model inference speed of 6.9 fps while maintaining a moderate amount of parameters, which is superior to other mainstream object detection models and verifies the effectiveness of our proposed method. At the same time, RebarDet’s high precision detection of round tubular objects facilitates enterprise intelligent manufacturing processes.

Analysis of Small Sea-Surface Targets Detection Performance According to Airborne Radar Parameters in Abnormal Weather Environments.

Along with the rapid development of marine radar, and particularly those carried on aircraft, the detection of small-sized targets which pose an increasing threat has become one of the main areas of interest. However, by considering an observation chain from an aircraft (such as a drone) in a maritime environment, with the aim of detecting and tracking of low signal-to-clutter ratio (SCR) targets, one of the important points would be the analysis of the radar system performance according to the radar input parameters, the atmospheric propagation medium, the various sea clutter characterization, and the type of targets (RCS, speed, etc.) in this environment. Therefore, it is necessary to obtain the overall path loss including the anomalous atmospheric environment, gas attenuation, clouds attenuation, rainfall attenuation, and beam scanning loss. To consider atmospheric attenuations, ITU-R models are used. On another side, because of spikes and dynamic variation properties, sea clutter is generally described by the statistical distribution with long tail and by its wider Doppler spectrum. Conventional algorithms such as those based on statistical models, MTI, and MTD processing are often limited, especially for the target of low speed and low RCS. Therefore, sea clutter, including empirical and statistical models available, is considered to estimate and simulate the impact of radar input parameters, targets RCS, and sea state on detection performance. The Doppler frequency of target echo which can be exploited for coherent processing is described by assuming an adequate scenario of observation geometry.

Interrupted sampling repeater jamming recognition and suppression based on phase-coded signal processing

In modern electronic warfare, interrupted sampling repeater jamming (ISRJ) seriously affects radar target detection performance. It has the advantages of flexible modulation and fast response. The existing approaches mostly require accurate ISRJ parameter estimation and only apply to a specific type of ISRJ. We propose an ISRJ recognition and suppression method based on phase-coded signal processing. By analyzing the cross ambiguity function (CAF) of ISRJ, we find that ISRJ can form multiple peaks, and these peaks are uniformly distributed on the Doppler cut of the CAF. Using this characteristic, we propose an ISRJ recognition method based on Doppler compensation processing. On this basis, a Doppler filtering processing method is proposed to suppress the ISRJ. Both simulated and measured results corroborate the effectiveness of the ISRJ suppression with the developed methods.

Cognitive waveform design with desired spectrum-autocorrelation properties

This paper proposes a novel cognitive waveform design methodology aimed at shaping a specified spectrum and achieving good autocorrelation properties in terms of the spectrum band notch and local autocorrelation peak sidelobe level (A-PSL). To this end, the Pareto optimization framework is introduced that minimizes the lp-norm of multi-objective functions both encompassing the stopband energy and local A-PSL. In addition, along with energy and peak-to-average power ratio constraints to ensure hardware compatibility, the out-of-band (OOB) spectrum energy and the rest autocorrelation sidelobe level are restricted to resist the OOB strong interference (also prevent spectrum leakage) and enhance multiple target detectability, respectively. To tackle the resultant nonconvex nonsmooth (in general NP-hard) design problem, an iterative majorization minimization proximal method of multipliers (MM-PMM) algorithm is developed along with each iteration involving multiple subproblems which are solved through a majorization method or closed-form solutions. In particular, resorting to Wiener-Khintchine theorem, the developed algorithm is efficiently implemented via the fast fourier transform and element-wise arithmetic. Extensive numerical examples highlight that the synthesized waveform shares the desired spectrum-autocorrelation properties and is capable of enhancing the weak target detection and anti-jamming performance in comparison with some counterparts.

A STAP clutter suppression algorithm based on sparse representation for small-array HFSWR

ABSTRACT In high-frequency surface wave radar (HFSWR) systems, the main-lobe of the receiving angle spectrum suffers from severe broadening due to a small-array aperture effect. This broadening causes more clutter components to be mixed into the main-lobe, resulting in target vessels more prone to be submerged in the clutter. This problem severely affects the target detection performance. A reduction in the number of array elements decreases the degrees of freedom (DOF), making clutter suppression more difficult. Space-time adaptive processing (STAP) has recently become a crucial tool for clutter suppression in advanced HFSWR systems, using two-dimensional data to increase DOF. Focusing on low DOF and typical nonhomogeneous clutter with high energy, this study proposes a STAP clutter suppression method by estimating clutter components accurately in the space-time domain based on sparse representation in few snapshots. The experimental results verify that the proposed algorithm provides high suppression performance for measured clutter.

RFI Mitigation for One-Bit UWB Radar Systems

Radio frequency interference (RFI) mitigation is critical to the proper operation of ultrawideband (UWB) radar systems because RFI can severely degrade the radar imaging capability and target detection performance. In this article, we address the RFI mitigation problem for one-bit UWB radar systems. A one-bit UWB system obtains its signed measurements via a low-cost and high rate sampling scheme, referred to as the continuous time binary value (CTBV) technology. This sampling strategy compares the signal to a known threshold that varies with slow-time and can be used to achieve a high sampling rate and quantization resolution with simple and affordable hardware. This article establishes a proper data model for the RFI sources and proposes a novel RFI mitigation method for the one-bit UWB radar system that uses the CTBV sampling technique. Specifically, we model the RFI sources as a sum of sinusoids with frequencies fixed during the coherent processing interval (CPI) and we exploit the sparsity of the RFI spectrum. We use an extended majorization-minimization-based 1bRELAX algorithm, referred to as 1bMMRELAX, to estimate the RFI source parameters from the signed measurements obtained by using the CTBV sampling strategy. We also devise a new fast frequency initialization method for the extended 1bMMRELAX algorithm to improve its computational efficiency. Moreover, a sparse method is introduced to recover the desired radar echoes using the estimated RFI parameters. Both simulated and experimental results are presented to demonstrate that our proposed algorithm outperforms the existing digital integration method, especially for severe RFI cases.

Constrained Contextual Bandit Learning for Adaptive Radar Waveform Selection

A sequential decision process in which an adaptive radar system repeatedly interacts with a finite-state target channel is studied. The radar is capable of passively sensing the spectrum at regular intervals, which provides side information for the waveform selection process. The radar transmitter uses the sequence of spectrum observations as well as feedback from a collocated receiver to select waveforms which accurately estimate target parameters. It is shown that the waveform selection problem can be effectively addressed using a linear contextual bandit formulation in a manner that is both computationally feasible and sample efficient. Stochastic and adversarial linear contextual bandit models are introduced, allowing the radar to achieve effective performance in broad classes of physical environments. Simulations in a radar-communication coexistence scenario, as well as in an adversarial radar-jammer scenario, demonstrate that the proposed formulation provides a substantial improvement in target detection performance when Thompson sampling and EXP3 algorithms are used to drive the waveform selection process. Further, it is shown that the harmful impacts of pulse-agile behavior on coherently processed radar data can be mitigated by adopting a time-varying constraint on the radar’s waveform catalog.

표적 탐지 및 식별 성능 향상을 위한 레이더 측정치 분석

Using artificial intelligence, radar measurements can improve the performance of target detection and identification methods. Testing data was generated from existing radar data, high-resolution range profile, and inverse synthetic aperture radar images. Radar cross section was chosen as the main parameter because it has been widely studied and its parameters are well known. To ensure the quality of the training dataset, we used simulation tools to determine which parameters had the greatest effect on the generated data. Missile models with different shapes were applied during simulations to confirm the possibility of detection and identification when suitable parameters were applied.

Double layer local contrast measure and multi-directional gradient comparison for small infrared target detection

Infrared small target detection is one of the key technologies in the search and track (IRST) based on infrared imaging equipment. At present, the performance of small target detection based on single frame infrared image is directly related to the accuracy of subsequent target tracking, so it has been studied a lot. However, the existing small target detection algorithms have certain limitations in detection accuracy and real-time performance, especially when the contrast between the target and the background area is not high or the background is complex, especially in the complex sea or sky background, due to the influence of a large amount of noise and clutter in the background, the existing infrared small target detection algorithms have a high false alarm rate. To solve the above problems, this paper proposes a small target detection algorithm based on weighted double layer local contrast and multi-directional gradient map, which realizes the accurate detection of small targets from two aspects of targets’ local contrast and gradient. Firstly, we design an improved two layer local contrast measurement architecture, and use the weighted mean method to better represent the gray value of the local window; Secondly, a local contrast comparison method based on target and background is proposed to enhance the intensity of small targets and suppress some background clutter; Then, the multi-directional gradient map is used to further suppress the noise so as to improve the contrast between the target and the background. At the same time, singular value decomposition (SVD) method is used to extract the main features including small targets, which can effectively suppress the small texture interference around the targets in the background without losing the target intensity; Finally, an adaptive threshold method is used to separate small targets from their background. Experimental results show that compared with the existing algorithms, the proposed detection algorithm can effectively reduce the false alarm rate in different complex scenes, and the computational efficiency is improved compared with some multi-scale small target detection methods. At the same time, the signal to clutter ratio (SCR), background suppression factor (BSF) and receiver operating characteristic (ROC) curve are also better than these existing state of the art algorithms, which can display good robustness.

Accurate modeling and coherent integration method for weak maneuvering target with oblique movement

Prolonging the integration time is a widely used method to enhance the detection performance for weak targets, whereas range migration (RM) and Doppler frequency migration (DFM) might occur when the target moves with high speed and high maneuverability. Although various algorithms have been proposed to solve the migration problem and realize coherent integration, they are based on the assumption that the target is moving radially relative to radar, i.e., the instantaneous slant range from radar to target is expressed as a finite order polynomial with respect to slow time. However, the target in the practical scene is probably moving obliquely relative to radar, thus modeling error will exist between the polynomial range expression and the target’s actual instantaneous slant range from radar. We focus on the accurate modeling and coherent integration problems for the weak maneuvering target with oblique movement, where the oblique movement model (OMM) of target echo is established. Subsequently, the OMM-generalized Radon–Fourier transform method is proposed to coherently accumulate the target’s energy via correcting the complex RM and DFM. Both simulation and real data processed results have been provided to demonstrate the effectiveness of the proposed method.

Marine Target Extraction Based on Adjoint Covariance Correction Model

AbstractConstant False‐Alarm Rate (CFAR) algorithm is a mature method in marine target detection at present, the key of which is the choice of probability model. The most classical probability model is Gaussian distribution, which is suitable for modeling the calm ocean surface. Complex ocean conditions generally do not obey to Gaussian distribution, which limits the performance of CFAR algorithm. Therefore, we propose Adjoint Covariance Correction Model (ACCM) to improve the performance of CFAR marine target detection. Analyzing the distribution characteristic of ocean clutter under complex ocean conditions based on experiments, we find that it has long‐tail characteristic. Aiming at this characteristic, we have conducted fitting experiment on 23 kinds of probability density functions (PDFs) and find Loglogistic distribution has a better fitting effect on long‐tail characteristic, therefore, we use it to model ocean clutter under complex ocean conditions for the first time. In order to further improve the fitting goodness, we add a variance correction term to the Loglogistic model to construct ACCM for marine target extraction. The experiment result shows that ACCM effectively fits the long‐tail characteristic caused by Synthetic Aperture Radar backscatter under complex ocean conditions. The goodness of fit improves by 50% compared with Loglogistic model, and the amount of false alarms is 81.42% of that of Loglogistic model. The extraction accuracy of marine target characteristic parameter based on ACCM is 11.58% higher than that of Loglogistic model, and 12.18% higher than that of two‐parameter CFAR model based on standard error function (named SEF‐CFAR).

Signal fusion research in passive radar based on polarization diversity technology

AbstractPolarization diversity technology is an effective method to improve the detection performance of passive radar systems, but the related papers mainly conduct research on the polarization diversity in interference suppression, and there are few studies focusing on the polarization characteristics of the target itself. The work in this paper is divided into two parts. The first part is going to investigate the potential benefits of signal fusion in target polarization-scattering characteristic. The analysis results show that the cross-polarized component of the target scattered echo is not always weaker than the co-polarized component, and the polarization amplitude ratio value is mostly concentrated near 1. This indicates that polarization signal fusion will likely improve target detection performance. The other part introduces a polarization signal fusion method based on the non-coherent integration (P-NCI), but this method is susceptible to the influence of the difference in the signal-to-noise ratio (SNR) of the signal of each polarization channel, resulting in a reduction in the SNR of the fusion signal. The polarization signal fusion method based on adaptive weighting (AW-PSF) can effectively improve the robustness of signal fusion. The experimental results show that AW-PSF has better detection performance compared with single-polarization channel and P-NCI.

Anti-jamming pulse diversity phase coded waveform design based on periodic ambiguity function shaping

In modern electronic warfare, artificial repeat jamming from hostile jammers and range-Doppler sidelobe interference from strong scatterers seriously affect target detection performance. Most of the existing anti-jamming methods are proposed for one of them. In this paper, we propose a pulse diversity phase coded (PDPC) waveform design scheme based on periodic ambiguity function (PAF) shaping. This method can minimize the jamming energy in the target detection area and has significant applications in the radar anti-jamming field. We first define the PDPC waveform and analyze its characteristics. On this basis, the principle of jamming suppression based on PDPC waveform PAF shaping is discussed. Further, a closed-loop confrontation method including jamming cognition and suppression is proposed. Simulation examples demonstrate that the proposed method can comprehensively call waveform optimization resources to suppress two kinds of jamming simultaneously and possess better jamming suppression performance.

Fast ship detection based on lightweight YOLOv5 network

Aiming at a series of problems such as detection accuracy, calculation blocking, display delay, and so on in the ship detection of surveillance video, an improved YOLOv5 algorithm is proposed in this paper. First, to improve the detection performance, it is proposed to optimize the anchor box algorithm in the YOLOv5 network according to the ship target characteristics. Then, the t-SNE algorithm is used to reduce and visualize the data set label information and perform weighted analysis on the processed features for low-dimensional data. The mapped kernel k-means clustering algorithm adaptively selects a more appropriate anchor box and considers the detection performance of large and small ship targets. Secondly, to improve the problem of computational blocking and delay, the BN scaling factor γ is used to compress the YOLOv5 network, so that the model can be reduced without reducing the detection performance. The optimized YOLOv5 framework is trained on the self-integrated data set. The accuracy of the algorithm is increased by 2.34%, and the ship detection speed reaches 98 fps and 20 fps in the server environment and the low computing power version (Jetson nano), respectively.