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This study deals with the problem of adaptive radar detection when a limited number of training data, due to environmental heterogeneity, is present. Suppose that some a priori spectral models for the interference in the cell under test and a lower bound on the power spectral density (PSD) of the white disturbance term are available. Hence, generalised likelihood ratio test-based detection algorithms have been devised. At the design stage, the basic idea is to model the actual interference inverse covariance as a combination of the available a priori models and to account for the available lower bound on the PSD. At the analysis stage, the capabilities of the new techniques have been shown to detect targets when few training data are available as well as their superiority with respect to conventional adaptive techniques based on the sample covariance matrix.

The problem of detecting point like targets over a glistening surface is investigated in this manuscript, and the design of an optimal waveform through a two-step process for a multipath exploitation radar is proposed. In the first step, a non-adaptive waveform is transmitted and a constrained Generalized Likelihood Ratio Test (GLRT) detector is deduced at reception which exploits multipath returns in the range cell under test by modelling the target echo as a superposition of the direct plus the multipath returns. Under the hypothesis of heterogeneous environments, thus by assuming a compound-Gaussian distribution for the clutter return, this latter is estimated in the range cell under test through the secondary data, which are collected from the out-of-bin cells. The Fixed Point Estimate (FPE) algorithm is applied in the clutter estimation, then used to design the adaptive waveform for transmission in the second step of the algorithm, in order to suppress the clutter coming from the adjacent cells. The proposed GLRT is also used at the end of the second transmission for the final decision. Extensive performance evaluation of the proposed detector and adaptive waveform for various multipath scenarios is presented. The performance analysis prove that the proposed method improves the Signal-to-Clutter Ratio (SCR) of the received signal, and the detection performance with multipath exploitation.

In this study, the authors deal with the problem of adaptive detection of point-like targets in Gaussian disturbance with unknown but persymmetric structured covariance matrix induced by the space and/or time symmetry of the sensing system. In this framework, they devise and assess two selective receivers exploiting the Rao test and the generalised likelihood ratio test design criteria. The performance assessment, conducted by Monte Carlo simulation, has shown that the proposed receivers can significantly outperform their unstructured counterparts and guarantee enhanced rejection performance of unwanted signals with respect to their natural competitors.

This paper studies adaptive radar detection of a subspace signal embedded in two disturbance sources. The former is thermal noise with known power. The latter is a Gaussian subspace interference with zero mean and unknown covariance matrix (CM). It is assumed that the signal and interference subspaces are known and partially related. As customary, several secondary data containing only interference and thermal noise are used to estimate this interference CM. This paper derives the generalized likelihood ratio test (GLRT), and theoretically deduces the probabilities of false alarm (PFA) and detection of the new detector. This PFA shows that the new detector has the constant false alarm rate (CFAR) property against the interference CM. Several numerical experiments are performed to evaluate the detection performance of the new detector. The results show that the performance of the new detector is better than the natural counterparts in some scenarios.

This paper discusses the problem of detecting subspace signals in structured interference plus compound Gaussian sea clutter with persymmetric structure. The sea clutter is represented by a compound Gaussian process wherein the texture obeys the inverse Gaussian distribution. The structured interference lies in a known subspace, which is independent with the target signal subspace. By resorting to the two-step generalized likelihood ratio test, two-step Rao, and two-step Wald design criteria, three adaptive subspace signal detectors are proposed. Moreover, the constant false-alarm rate property of the proposed detectors is proved. The experimental results based on IPIX real sea clutter data and simulated data illustrate that the proposed detectors outperform their counterparts.

Effects of Mutual Coupling of Radiating Antennas on an Adaptive Radar Detector In this paper, we address the adaptive detection/classification of signals in a homogenous interference environment. We refer to a radar system equipped with a phased array antenna and account for both the presence of mutual coupling between radiating antennas and a possible coherent interferer impinging on the array mainbeam. To deal with this scenario, we adopt a two-stage detection/classification scheme, enjoying the Costant False Alarm Rate (CFAR) property, to discriminate between target detection and coherent interferer rejection. Finally, we evaluate the system performance via Monte Carlo simulations. The results show that our system has interesting rejection capabilities and satisfactory detection levels. As a consequence, it could be successfully applied in real scenarios where mutual coupling is present.

Small targets with low radar cross-section (RCS) and high velocities are very hard to track by radar as long as the frequent variations in speed and location demand shorten the integration temporal window. In this paper, we propose a technique for tracking evasive targets using a continuous wave (CW) radar array of multiple transmitters operating in the millimeter wavelength (MMW). The scheme is demonstrated to detect supersonic moving objects, such as rifle projectiles, with extremely short integration times while utilizing an adaptive processing algorithm of the received signal. Operation at extremely high frequencies qualifies spatial discrimination, leading to resolution improvement over radars operating in commonly used lower frequencies. CW transmissions result in efficient average power utilization and consumption of narrow bandwidths. It is shown that although CW radars are not naturally designed to estimate distances, the array arrangement can track the instantaneous location and velocity of even supersonic targets. Since a CW radar measures the target velocity via the Doppler frequency shift, it is resistant to the detection of undesired immovable objects in multi-scattering scenarios; thus, the tracking ability is not impaired in a stationary, cluttered environment. Using the presented radar scheme is shown to enable the processing of extremely weak signals that are reflected from objects with a low RCS. In the presented approach, the significant improvement in resolution is beneficial for the reduction in the required detection time. In addition, in relation to reducing the target recording time for processing, the presented scheme stimulates the detection and tracking of objects that make frequent changes in their velocity and position.

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.

Space–time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. When working in the heterogeneous environment, the number of training samples that satisfy independent and identically distributed (IID) conditions is insufficient, making it difficult to ensure the estimation accuracy of the clutter plus noise covariance matrix for traditional STAP methods. Sparse recovery–based STAP (SR–STAP) methods have received widespread attention in the past few years. The accurate estimation of the clutter plus noise covariance matrix can be achieved using only a few training samples. The iterative adaptive approach (IAA) can quickly and accurately estimate the power spectrum, but applying this method directly to the STAP method cannot produce good performance. In this paper, a fast IAA–based SR–STAP method is proposed. Based on the weighted l1 problem, the IAA spectrum is used as a weighted term to obtain a good approximation. In order to obtain an analytical solution, we use the weighted l2 norm to approximate the weighted l1 norm without loss of performance. Compared with the IAA–STAP method, the proposed method is more robust to errors. Moreover, the proposed method has a fast computational speed. The effectiveness of the proposed method is demonstrated by simulations.

Low-altitude slow-moving small (LSS) targets are defined as flying at altitudes less than 1000 m with speeds less than 55 m/s and a radar crossing-section (RCS) less than 2 m2. The detection performance of ground-based radar using the LSS target detection technique can be significantly deteriorated by the diversity of LSS targets, background clutter, and the occurrence of false alarms caused by multipath interference. To address the LSS target detection problem, we have devised a novel two-dimensional electronic scanning active phased array radar system that is implemented in the software-defined radar architecture and propose a transmit beam control algorithm based on the low peak-to-average ratio (PAPR). Meanwhile, we devised a flexible arbitrary radar waveform generator to adapt to complex environmental situations. Field experiment results effectively demonstrate that our radar can be used to detect LSS targets. Moreover, an ablation experiment was conducted to verify the role played by transmit beam control and adaptive waveform optimization and generation in improving the system performance.