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Radar target detection (RTD) is one of the most significant techniques in radar systems, which has been widely used in the field of military and civilian. Although radar signal processing has been revolutionized since the introduction of deep learning, applying deep learning in RTD is considered as a novel concept. In this paper, we propose a model for multitask target detection based on convolutional neural network (CNN), which works directly with radar echo data and eliminates the need for time-consuming radar signal processing. The proposed detection method exploits time and frequency information simultaneously; therefore, the target can be detected and located in multi-dimensional space of range, velocity, azimuth and elevation. Due to the lack of labeled radar complex data, we construct a radar echo dataset with multiple signal-to-noise ratio (SNR) for RTD. Then, the CNN-based model is evaluated on the dataset. The experimental results demonstrated that the CNN-based detector has better detection performance and measuring accuracy in range, velocity, azimuth and elevation and more robust to noise in comparison with traditional radar signal processing approaches and other state-of-the-art methods.

This study presents a reliable classification of walking pedestrians and animals using a radar operating in the millimeter waves (mmW) regime. In addition to the defined targets, additional targets were added in an attempt to fool the radar and to present the robustness of the proposed technique. In addition to the classification capabilities, the presented scheme allowed for the ability to detect the range of targets. The classification was achieved using a deep neural network (DNN) architecture, which received the recordings from the radar as an input after the pre-processing procedure. Qualitative detection was made possible due to the radar’s operation at extremely high frequencies so that even the tiny movements of limbs influenced the detection, thus enabling the high-quality classification of various targets. The classification results presented a high achievable accuracy even in the case where the targets attempted to fool the radar and mimicked other targets. The combination of the use of high frequencies alongside neural-network-based classification demonstrated the superiority of the proposed scheme in this research over the state of the art. The neural network was analyzed with the help of interpretable tools such as explainable AI (XAI) to achieve a better understanding of the DNN’s decision-making process and the mechanisms via which it was able to perform multiple tasks at once.

This book shows you how to adopt data-driven techniques for the problem of radar detection, both per se and in combination with model-based approaches. In particular, the focus is on space-time adaptive target detection against a background of interference consisting of clutter, possible jammers, and noise. It is a handy, concise reference for many classic (model-based) adaptive radar detection schemes as well as the most popular machine learning techniques (including deep neural networks) and helps you identify suitable data-driven approaches for radar detection and the main related issues. You'll learn how data-driven tools relate to, and can be coupled or hybridized with, traditional adaptive detection statistics; understand fundamental concepts, schemes, and algorithms from statistical learning, classification, and neural networks domains. The book also walks you through how these concepts and schemes have been adapted for the problem of radar detection in the literature and provides you with a methodological guide for the design, illustrating different possible strategies. You'll be equipped to develop a unified view, under which you can exploit the new possibilities of the data-driven approach even using simulated data. This book is an excellent resource for Radar professionals and industrial researchers, postgraduate students in electrical engineering and the academic community.

Ground-penetrating radar (GPR) signal recognition depends much on manual feature extraction. However, the complexity of radar detection signals leads to conventional intelligent algorithms lacking sufficient flexibility in concrete pavement detection. Focused on these problems, we proposed an adaptive one-dimensional convolution neural network (1D-CNN) algorithm for interpreting GPR data. Firstly, the training dataset and testing dataset were constructed from the detection signals on pavement samples of different types of distress; secondly, the raw signals are were directly inputted into the 1D-CNN model, and the raw signal features of the radar wave are extracted using the adaptive deep learning network; finally, the output used the Soft-Max classifier to provide the classification result of the concrete pavement distress. Through simulation experiments and actual field testing, the results show that the proposed method has high accuracy and excellent generalization performance compared to the conventional method. It also has practical applications.

Waveform design for integrated radar and jamming is generally based on the concept of shared waveform, which uses jamming signals without typical radar signal characteristics for detection. Existing waveforms have shown limited design flexibility, high levels of sidelobe in detection results, and overall ordinary performance. We propose an integrated radar and jamming waveform based on smart modulation and complementary coding. Unlike traditional integrated radar and jamming waveform based on smart modulation, the phase angle of the binary phase-coded sequence is adjustable in this smart modulation method, allowing for a controllable jamming effect, achieving true smart modulation. However, this smart modulation waveform also suffers from high sidelobes in detection. To address this issue, we take a complementary coding approach and design a smart modulation waveform with complementary characteristics. This waveform can synthesize a complete linear frequency modulation (LFM) signal by adding two pulses together, thereby reducing the sidelobes in the smart modulation waveform and enhancing its detection performance. Theoretical analysis indicates that the detection and jamming effects of this integrated waveform can be flexibly controlled by adjusting the phase angles of the coding sequences. Simulation analysis and experimental results confirm the significant advantages of this waveform.

Crowdsourcing is an observational method that has gained increasing popularity in recent years. In hail research, crowdsourced reports bridge the gap between heuristically defined radar hail algorithms, which are automatic and spatially and temporally widespread, and hail sensors, which provide precise hail measurements at fewer locations. We report on experiences with and first results from a hail size reporting function in the app of the Swiss National Weather Service. App users can report the presence and size of hail by choosing a predefined size category. Since May 2015, the app has gathered >50,000 hail reports from the Swiss population. This is an unprecedented wealth of data on the presence and approximate size of hail on the ground. The reports are filtered automatically for plausibility. The filters require a minimum radar reflectivity value in a neighborhood of a report, remove duplicate reports and obviously artificial patterns, and limit the time difference between the event and the report submission time. Except for the largest size category, the filters seem to be successful. After filtering, 48% of all reports remain, which we compare against two operationally used radar hail detection and size estimation algorithms, probability of hail (POH) and maximum expected severe hail size (MESHS). The comparison suggests that POH and MESHS are defined too restrictively and that some hail events are missed by the algorithms. Although there is significant variability between size categories, we found a positive correlation between the reported hail size and the radar-based size estimates.

The deployment of weapon systems prior to the engagement of anti-cruise missiles constitutes the primary research challenge in surface-to-air missile operations. Initially, it is essential to analyze operational requirements and scientifically delineate the guided radar detection area, kill zone, and interception zone based on physical realities. A model should be established to ascertain the fundamental conditions for site deployment, taking into account factors such as position height, shielding angle, and geographic coordinates. In relation to low-altitude approaches by adversarial forces, key considerations include the depth and breadth of firepower from the weapon system in the attack direction, firepower density in that direction, and lethality probability. An Operational Effectiveness model should be developed based on coverage area of firepower, effective interceptions per unit time, and lethality probabilities of weapon systems to evaluate each location’s effectiveness. Finally, leveraging land selection principles and external information coordination mechanisms will facilitate establishing a genetic algorithm-based strategy for deploying surface-to-air missile systems. The validity of this algorithm has been demonstrated through practical examples.

In recent years, the three-dimensional (3D) radar detection range has played an essential role in the layout of devices such as aircraft and drones. To compensate for the shortcomings of three-dimensional calculations for radar terrain masking, a new calculation method is proposed for assessing the terrain occlusion of radar detection range. First, the high-dimensional electromagnetic data after discretization are modeled based on the tensor data structure, and the tensor grid dilation operator is constructed. Then, the dilation process begins from the overlapping section of the radar detection range and terrain, and it is adjusted by the terrain occlusion judgment factor and the dilation judgment factor to obtain the obstructed part due to the terrain. Finally, the actual radar detection range under terrain occlusion is obtained. The simulation results show that the method proposed in this paper can adapt to different grid sizes and terrain shapes, significantly enhancing computational efficiency while maintaining internal features.

Sea surface target detectors are often interfered by various complex sea surface factors such as sea clutter. Especially when the signal-to-clutter ratio (SCR) is low, it is difficult to achieve high-performance detection. This paper proposes a triple-channel network model for maritime target detection based on the method of multi-modal data fusion. This method comprehensively improves the traditional multi-channel inputs by extracting highly complementary multi-modal features from radar echoes, namely, time-frequency image, phase sequence and correlation coefficient sequence. Appropriate networks are selected to construct a triple-channel network according to the internal data structure of each feature. The three features are utilized as the input of each network channel. To reduce the coupling between multi-channel data, the SE block is introduced to optimize the feature vectors of the channel dimension and improve the data fusion strategy. The detection results are output by the false alarm control unit according to the given probability of false alarm (PFA). The experiments on the IPIX datasets verify that the performance of the proposed detector is better than the existing detectors in dealing with complex ocean scenes.

Detecting weak radar targets in complex cluttered environments remains a significant challenge, particularly when attempting to effectively detect low signal-to-clutter ratio (SCR) targets while maintaining a constant false alarm rate (CFAR). We propose novel CFAR detectors based on time series analysis and statistical foundations. We model radar echo data within a coherent processing interval as stationary time series governed by linear random processes, enabling the application of a time series resampling approach to establish the autoregressive sieve bootstrap consistency of the banded sample autocovariance matrix (SACM) in the spectral norm. Leveraging this, we derive the numerical distribution of statistics related to the largest eigenvalue of the banded SACM. We introduce two improved CFAR detectors: one based on the banded SACM spectral norm (BSN detector) and another based on the likelihood ratio test in banded SACM eigenvalues (BLR detector). Additionally, we propose an adaptive CFAR detector, the maximum eigenvalue trimmed (MET) detector, developed using single-sample hypothesis testing. Our analysis demonstrates that detection probabilities stabilize as the number of bands exceeds a certain threshold, with robust performance under varying SCRs and false alarm probabilities. Simulations and real data experiments validate that all three detectors significantly outperform traditional radar target detection methods in terms of both detection performance and computational efficiency. Notably, the MET detector offers unique advantages by eliminating the need for non-target reference data and exhibiting strong adaptive characteristics. Experimental results confirm its remarkable robustness in scenarios with other targets present in reference cells, achieving over 80% detection probability when the SCR is set to -5 dB with appropriate parameter adjustments. This work provides a comprehensive framework for enhancing radar target detection performance through advanced statistical methods and innovative detector designs.