Recent advancements in Network Intrusion Detection Systems (NIDS) primarily focus on detecting intrusions at the network layer. However, most solutions identify malicious activities when the attacker is already inside the network. This study introduces an innovative approach to NIDS, utilizing the Wi-Fi Channel State Information (CSI) combined with machine learning to proactively detect threats at the physical and link layers. Unlike traditional methods, our system leverages physical layer data, significantly enhancing early detection capabilities. We evaluated the performance of classical machine learning models, including SVM, Random Forest, Decision Tree, KNN, and Naive Bayes, on 800, 000 instances across three different environments: laptops, iPhones, and Android devices. The Decision Tree algorithm emerged as the most effective, achieving an accuracy and F1-score of 99.95%. This research demonstrates that the amplitude variations of Wi-Fi signals across subcarriers during brute-force attacks are markedly distinct from benign activities, providing a robust indicator for early threat detection. To the best of our knowledge, our approach advances the state-of-the-art in NIDS by integrating data from layers 1 and 2, enabling the identification of malicious users before they associate with the target Wi-Fi network.

This study investigates the application of Tensor Robust Principal Components analysis (TRPCA) as a pre-processing tool in classifying oil rigs using synthetic aperture radar (SAR) images. The pre-processing considers the tensor composition of original images and subsequent attribute extraction using the VGG-16 convolutional neural network from the low-rank and sparse images. The extracted features are then classified using Support Vector Machine (SVM), Neural Network (NET), and Logistic Regression (LR) classifiers. The experimental evaluation utilized C-band VH-polarization SAR images from the Sentinel-1 system. The findings indicate that TRPCA-based pre-processing enhances classification accuracy, outperforming existing methods documented in the literature.

We propose a novel deep learning-based method that adapts the domains of images acquired by different remote sensing sensors. It adapts a lower resolution image to the domain of an an higher resolution targeted sensor. This is effective in the case of change detection, where differences between sensors, such as spatial resolution and radiometry, can hinder the detection performance and where model hallucination artifacts are unwanted. The proposed technique divides the input image into patches and uses a diffusion-based model to generate translated patches in the style of the target sensor. The translated patches are stitched together to form the output image, which provides global generative consistency. Our approach can handle images with different resolutions and tonalities. We show its effectiveness on a Sentinel-II + Planet Dove data set and demonstrate its high generation quality and contribution to enhance change detection performance.

In the context of synthetic aperture radar (SAR) systems, the Gamma distribution stands out as a strong contender for clutter modeling. The cell-averaging constant false alarm rate (CA-CFAR) detector has frequently been employed as a well-balanced detection technique, ensuring a blend of high performance and comparatively low complexity. In this study, we evaluate, in an exact manner, the CA-CFAR detector’s performance in an SAR system, assuming the presence of correlated Gamma-distributed clutter. To this aim, we derive novel exact expressions for the probability of detection (PD) and the probability of false alarm (PFA), simplifying their runtime evaluations without depending on specific mathematical software. The independent and identically distributed (IID) and independent not identically distributed (INID) cases are also analyzed. In particular, for the IID case, our derived PD and PFA expressions are given in closed form. Our analytical findings are validated through Monte Carlo simulations.

Wavelength-resolution (WR) synthetic aperture radar (SAR) change detection (CD) has been used to detect concealed targets in forestry areas. However, most proposed methods are generally based on matrix or vector analyses and, therefore, do not exploit information embedded in multidimensional data. In this letter, a CD method for WR SAR image stacks based on tensor robust principal component analysis (TRPCA) is proposed. The proposed CD method used the new tensor nuclear norm induced by the definition of the tensor-tensor product to exploit temporal and spatial information contained in the image stack. To assess the performance of the proposed method, we considered SAR images obtained by the very high frequency (VHF) WR CARABAS-II SAR system. Experiments for three different stack sizes show that a significant performance gain can be achieved when large image stacks are considered. The proposed CD method performs better in terms of probability of detection (PD) and false alarm rate (FAR) than the other five CD methods in VHF WR SAR images, including one based on matrix robust principal component analysis (RPCA). In a particular setting, it achieves a PD of 99% and a FAR of 0.028 false alarms per km2.

The recently proposed frequency scanning (F-Scan) technique, together with the new International Telecommunication Union (ITU) allocation of 1200 MHz in X-band, enables the improvement of important performance parameters of synthetic aperture radar (SAR) acquisitions, such as the swath width, the signal-to-noise ratio, and the range ambiguity-to-signal ratio. The concurrent imaging technique, in turn, increases the flexibility of the radar system by allowing the simultaneous imaging of two or more areas with independent imaging modes. The integration of both techniques, therefore, enables the already valuable concurrent mode to achieve much better performance. Furthermore, motivated by the high-resolution wide-swath (HRWS) mission proposal in X-band, the displaced phase center antenna (DPCA) technique is considered to improve the azimuth resolution by using multiple receive (Rx) channels in azimuth. In this article, we discuss the design and performance of a new concurrent imaging mode that is enhanced by the F-Scan and DPCA techniques to make simultaneous imaging more flexible and powerful. Special attention is given to timing, range ambiguities, and availability aspects. The capability to simultaneously acquire two high-quality images with noteworthy flexibility is innovative and of great value, not only in daily operational applications but especially in extraordinary and crisis situations.

We propose an algorithm based on Information Theory to detect changes in Ultra-Wideband (UWB) Very-High Frequency (VHF) Synthetic Aperture Radar (SAR) images with high performance and low complexity. Our algorithm models the clutter-plus-noise using six different distributions and computes a scalar statistic for each pixel based on a multi-temporal stack of images. With this statistic, it is then possible to apply hypothesis testing and classification methods to infer the occurrence of a change. In this context, we derive expressions necessary for the entropy-based statistics, including the entropy variance for the Weibull and Rice distributions. We also evaluate the computational time complexity of the algorithm for each distribution studied. Furthermore, a masking strategy is used to reduce false alarms significantly. We show that the mask mapping assumptions are mild in scenarios with stacks of images, allowing its use in many scenarios. Our algorithm achieves a false alarm rate (FAR) of 0.08 and a probability of detection (PD) of 100%, outperforming existing methods on the CARABAS II data set.

Sea clutter has been a long-standing issue in old and modern radars. Under this context, the K-distribution has emerged as a promising clutter model to accurately mimic sea signal variations in a large variety of radar systems. To guarantee an adequate radar performance in the presence of sea clutter, the family of constant false-alarm rate (CFAR) detectors has been commonly used. In particular, due to its adequate balance between performance and implementation, the cell-averaging CFAR (CA-CFAR) detector has been considered an attractive detection mechanism to enhance radar performance over various clutter environments. In this work, we assess radar performance considering a CA-CFAR detector operating over K-distributed sea clutter with fully correlated texture. More precisely, we derive novel closed-form expressions for the probability of detection ( $P_{\text {D}}$ ) and the probability of false alarm ( $P_{\text {FA}}$ ) that can be readily evaluated using any mathematical software. Monte-Carlo (MC) simulations corroborate our analytical findings.

This article introduces a continuous probability distribution as an approximation to the${\mathcal {G}}^{0}_{A}$distribution for amplitude synthetic aperture radar (SAR) imagery modeling. Called${\mathcal {L}}_{A}$distribution, it is an empirical model and an analytically more tractable alternative than${\mathcal {G}}^{0}_{A}$model, with the same number of parameters and no special functions in its formulation. It also has a closed form for the quantile function, making it easier to calculate quantiles and generate pseudorandom numbers and obtain closed-form expressions for skewness and kurtosis coefficients. Useful properties of the${\mathcal {L}}_{A}$distribution are introduced, and the maximum likelihood method is considered for parameter estimation. Based on the Kullback–Leibler divergence (KLD), it is shown that the average information missed when using the${\mathcal {L}}_{A}$instead of${\mathcal {G}}^{0}_{A}$distribution is negligible. Numerical studies in simulated and measured SAR images obtained by different systems and representing different land-use regions are conducted to compare the performances of the${\mathcal {G}}^{0}_{A}$and${\mathcal {L}}_{A}$distributions. The simulation results suggest that the parameter estimation performances of both distributions are similar. Applications to real data show that the images were best fit with the${\mathcal {L}}_{A}$distribution in all considered cases and figure-of-metrics.

This letter presents two new change detection (CD) methods for synthetic aperture radar (SAR) image stacks based on the Neyman–Pearson criterion. The first proposed method uses the data from wavelength–resolution images stack to obtain background statistics, which are used in a hypothesis test to detect changes in a surveillance image. The second method considersa prioriinformation about the targets to obtain the target statistics, which are used together with the previously obtained background statistics, to perform a hypothesis test to detect changes in a surveillance image. A straightforward processing scheme is presented to test the proposed CD methods. To assess the performance of both proposed methods, we considered the coherent all radio band sensing (CARABAS)-II SAR images. In particular, to obtain the temporal background statistics required by the derived methods, we used stacks with six images. The experimental results show that the proposed techniques provide a competitive performance in terms of probability of detection and false alarm rate compared with other CD methods.

In this letter, we propose a method to reduce the number of false alarms in a wavelength–resolution synthetic aperture radar (SAR) change detection scheme by using a convolutional neural network (CNN). The detection is performed in two steps: change analysis and object classification. A simple technique for wavelength–resolution SAR change detection is implemented to extract potential targets from the image of interest. A CNN is then used for classifying the change map detections as either a target or nontarget, further reducing the false alarm rate (FAR). The scheme is tested for the CARABAS-II data set, where only three false alarms over a testing area of 96 km2are reported while still sustaining a probability of detection above 96%. We also show that the network can still reduce the FAR even when the flight heading of the SAR system measurement campaign differs by up to 100° between the images used for training and test.

This letter presents an incoherent change detection algorithm (CDA) for wavelength-resolution synthetic aperture radar (SAR) based on convolutional neural networks (CNNs). The proposed CDA includes a segmentation CNN, which localizes potential changes, and a classification CNN, which further analyzes these candidates to classify them as real changes or false alarms. Compared to state-of-the-art solutions on the CARABAS-II data set, the proposed CDA shows a significant improvement in performance, achieving, in a particular setting, a detection probability of 99% at a false alarm rate of 0.0833/km2.

The presence of outliers (anomalous values) in synthetic aperture radar (SAR) data and the misspecification in statistical image models may result in inaccurate inferences. To avoid such issues, the Rayleigh regression model based on a robust estimation process is proposed as a more realistic approach to model this type of data. This article aims at obtaining Rayleigh regression model parameter estimators robust to the presence of outliers. The proposed approach considered the weighted maximum likelihood method and was submitted to numerical experiments using simulated and measured SAR images. Monte Carlo simulations were employed for the numerical assessment of the proposed robust estimator performance in finite signal lengths, their sensitivity to outliers, and the breakdown point. For instance, the nonrobust estimators show a relative bias value 65-fold larger than the results provided by the robust approach in corrupted signals. In terms of sensitivity analysis and break down point, the robust scheme resulted in a reduction of about 96% and 10%, respectively, in the mean absolute value of both measures, in compassion to the nonrobust estimators. Moreover, two SAR datasets were used to compare the ground type and anomaly detection results of the proposed robust scheme with competing methods in the literature.

This article presents two supervised change detection algorithms (CDA) based on convolutional neural networks (CNN) that use stacks of co-registered wavelength-resolution synthetic aperture radar (SAR) images to detect changes in an image under monitoring. The additional information of a scene of interest provided by SAR image stacks can be explored to enhance the performance of change detection algorithms. In particular, stacks of images with similar statistics can be obtained for ultra-wideband (UWB) very high frequency (VHF) SAR systems, as they produce images highly stable in time. The proposed CDAs can be summed up into four stages: difference image formation, semantic segmentation, clustering, and change classification. The CNN-GSP algorithm is based on a ground scene prediction (GSP) image, which is used as a reference to form a difference image (DI). A CNN-based model then analyzes the DI. The CNN-MDI algorithm feeds multiple DIs with identical monitored images to a CNN-based model, which will concurrently analyze their features. Tests with CARABAS-II data show that the proposed CDAs can outperform other state-of-the-art algorithms that also use stacks of WR-SAR images. Beyond that, the proposed algorithms outperformed a CNN-based CDA that does not use image stacks, which shows that CNN-based algorithms can use the additional information provided by stacks of SAR images to reduce false alarm occurrences while increasing the probability of detection of changes.

In this paper, we present an unsupervised automatic target detection algorithm for multitemporal SAR images. The proposed two-fold method is expected to reduce processing time for large scene sizes with sparse targets while still improving detection performance. Firstly, pixel blocks are extracted from an initial change map to reduce the algorithm's search space and favor target detection. Secondly, an adaptive k-means algorithm selects the number of clusters that better separates targets from false alarms, which are discarded. Preliminary results show the advantages of the proposed method in processing time and detection performance over a recently proposed supervised method for the CARABAS-II dataset.

This letter presents a clutter statistical analysis for stacks of wavelength-resolution synthetic aperture radar (SAR) images. Each image stack consists of SAR images generated by the same sensor, using the same flight track illuminating the same scene but with a time separation between the illuminations. We test three candidate statistical distributions for time changes in the stack, namely, Rician, Rayleigh, and log-normal. The tests results reveal that the Rician distribution is a very good candidate for modeling stack of wavelength-resolution SAR images, where 98.59% of the tested samples passed the Anderson-Darling (AD) goodness-of-fit test. Also, it is observed that the presence of changes in the ground scene is related to the tested samples that have failed in the AD test for the Rician distribution hypothesis.

This paper presents an incoherent change detection algorithm (CDA) for synthetic aperture radar (SAR) images based on logistic regression. The input data consists of a set of 24 SAR images acquired in a test site in northern Sweden [1]. Subsets of these images are trained based on pixel amplitude, flight heading and neighboring features such as local mean, standard deviation and skewness. The proposed method intends to explore the advantadges from both pixel- and object-based approaches, while evaluating multiple features in amplitude-only SAR images. Preliminary results based on K-fold cross-validation have shown that the proposed CDA achieves good performance when compared to the results presented in [1].

This letter proposes a regression model for nonnegative signals. The proposed regression estimates the mean of Rayleigh distributed signals by a structure which includes a set of regressors and a link function. For the proposed model, we present: 1) parameter estimation; 2) large data record results; and 3) a detection technique. In this letter, we present closed-form expressions for the score vector and Fisher information matrix. The proposed model is submitted to extensive Monte Carlo simulations and to the measured data. The Monte Carlo simulations are used to evaluate the performance of maximum likelihood estimators. Also, an application is performed comparing the detection results of the proposed model with Gaussian-, Gamma-, and Weibull-based regression models in synthetic aperture radar (SAR) images.

This paper presents a study on the constant false alarm rate (CFAR) filter design for change detection algorithms (CDA). More specifically, we are interested in CFAR filters used in CDA for low frequency ultra-wideband (UWB) synthetic aperture radar (SAR) systems. The filter design performance was evaluated in terms of false alarm rate (FAR) and probability of detection (PD). For evaluation purposes, we considered a set of SAR images obtained with the CARABAS-II system. The results are compared with the ones presented in [1], where the same CDA was considered, except for the CFAR filter. The results show that relevant FAR performance improvements can be obtained by just modifying the CFAR filter parameters taking into account the image resolution and target characteristics.

This paper introduces a method to reduce false alarms in wavelength-resolution synthetic aperture radar (SAR) change detection and aims at very high frequency-band systems like the Coherent All Radio Band System (CARABAS). The false alarms are usually caused by the elongated structures, such as power lines and fences, which stand out from the background. The responses of elongated structures are sensitive to flight path. The introduced method aims at minimizing the false alarms caused by the elongated structures and is based on the well-known adaptive processing mechanism, i.e., the so-called adaptive noise canceler (ANC) where a separate reference signal is required. The changes between measurements are considered by the input signal of ANC while the separate reference signal comes from the measurements without change. Hence, the method requires three SAR images associated with three measurements, with no changes between two of them. The reference data for the study are provided by CARABAS. The experimental results indicate that the method can reduce false alarms significantly and provide high probability of detection (≥98%). The experimental results also show that the method still works well even in the case where the flight tracks of the SAR system in the change detection measurements are slightly different.

This letter presents an analysis of prefiltered clutter ultrawideband (UWB) very high frequency synthetic aperture radar (SAR) images. The image data are reorganized into subvectors based on the observation of the image-pair magnitude samples. Based on this approach, we present a statistical description of the SAR clutter obtained by the subtraction between two real SAR images. The statistical analysis based on bivariate distribution data organized into different intervals of magnitude can be an important tool to further understand the properties of the backscattered signal for low-frequency SAR images. In this letter, it is found that, for “good” image pairs, the subtracted image has Gaussian distributed clutter backscattering and that the noise mainly consists of the thermal noise and, therefore, speckle noise does not have to be considered. This is a consequence of the stable backscattering for a UWB low-frequency SAR system.

In this paper we propose a new change detection (CD) algorithm based on the Bayes theorem and probability assignments. Differently from any kind of likelihood ratio test (LRT) algorithms, the proposed algorithm does not present target alarms, but the probability of certain image position is a target position. In other words, the proposed method leads to quantitative estimates on the probability of a target at any pixel, whereas LRT algorithms can only be used as a figure of merit for any pixel to contain a target.

This paper presents an analysis of pre-filtered clutter VHF SAR images. The image data are reorganized into sub-vectors based on the observation of the image-pair magnitude samples. Based on this approach, we present a statistical description of the SAR clutter obtained by the subtraction between two real SAR images. The statistical analysis based on bivariate distribution data organized into different intervals of magnitude can be an important tool to further understand the properties of the backscattered signal, which can be a valuable premise for change detection processing.

In this paper we propose a low-complexity codebook-based beamforming with four transmit antennas and quantized feedback channel. The codebook design aggregates the effect of power allocation and phase rotation through a simple quantized transmit scheme. The codebook-based beamforming uses the feedback information in order to maximize the instantaneous signal-to-noise ratio (SNR) at the receiver. As a result, the proposed scheme presents an array gain. An SNR analysis is performed and it is used to find the optimal feedback information in the sense of maximizing the instantaneous SNR. A bit error rate (BER) analysis for a quantized feedback channel is also derived and it is used to compare to the results obtained for the proposed scheme under different levels of quantization. Simulations are performed over quasi-static flat Rayleigh fading channels for different closed-loop codebook-based schemes with four transmit antennas and unitary transmission rate. Results illustrate that the proposed scheme achieves full diversity order and outperforms other good schemes in terms of array gain.

A new cooperative diversity scheme with two relay stations and linear coherent detection at the destination node is proposed in this paper. We consider that a perfect CSI is available at the destination node, as well to the relay stations, allowing each relay station to perform optimal beamforming. In order to evaluate the effect of a quantized feedback link between the destination node and the relay stations, we also consider that the destination node sends, additionally, b more bits to the relays. Using these b bits, the relay stations can apply a correction factor to the detected symbol and then send it to the destination node which performs a linear coherent detection. In our proposal, the communication between the relay stations is not allowed. The performance of the proposed scheme is compared with the well known MRC receiver technique with four receive antennas in order to show that the proposed and the MRC schemes achieve the same diversity order. In addition, the influence of the number of feedback bits and the SNR between the transmit node and relay stations are also addressed.

We propose a simple cooperative diversity scheme for a communication system consisting of two cooperating nodes that receive a single channel state information (CSI) bit from the destination node. Essentially, the feedback bit tells which cooperating node has the strongest channel, and this information is used appropriately to obtain cooperative diversity. A simple linear receiver is proposed and its performance is shown to be very close to the maximum-likelihood performance. An upper bound on the average error probability is derived for binary phase-shift keying (BPSK) in flat Rayleigh fading channels under the assumption of ideal inter-user channel. In addition, through computer simulations, it is verified that the proposed scheme presents a good error performance when the inter-user channel signal-to-noise ratio is high or when the inter-user channel has a well-defined line-of-sight component. In other words, the new scheme becomes interesting when the cooperating nodes are close to each other. Comparisons with a cooperative scheme based on the Alamouti code are provided.

In this paper, we propose linear dispersion codes (LDCs) for multiple-input multiple-output (MIMO) channels with a prescribed amount of feedback. The proposed scheme selects the LDC from a set of LDCs that minimizes the error probability based on the instantaneous channel conditions. The determination of the best set of LDCs, i.e., the one that minimizes the average error probability, is described as a constrained optimization problem. While this problem appears to be intractable in general, for certain parameters we present good sets of LDCs, obtained from an iterative optimization algorithm. Results are given for rate-one LDCs only, but this restriction can be removed. Computer simulations show that the proposed schemes outperform previously reported comparable schemes for the same number of feedback bits.

Assuming that a feedback channel is available and the fading coefficients are known at the transmitter, Gore and Paulraj (2002) have proposed a transmit antenna selection scheme that uses the Alamouti code with the best pair of antennas, selected from m /spl ges/ 3 transmit antennas available, where the selection criterion is to minimize the instantaneous probability of error. They have showed that a diversity order of m is achieved, as if all the m antennas were used. The advantage is that only two RF chains are required, reducing the transmitter cost. In this paper, full-rate, non-orthogonal space-time block codes are produced by repeating and permuting columns of the Alamouti code matrix. These codes need only two RF chains, have decoding delay equal to two, and their maximum likelihood decoders based on linear processing are essentially the same as that of the Alamouti code. Based on the instantaneous fading coefficients, the transmitter selects either one of the proposed codes with three antennas or the Alamouti code with two antennas. Simulations results for a simple case indicate that full diversity is achieved in spite of using non-orthogonal codes, and a coding gain of up to 1 dB over Gore and Paulraj's scheme is observed.

Complex orthogonal space-time block codes for more than two transmit antennas cannot achieve full rate over the fading channel. By relaxing the orthogonality constraint, however, it is possible to achieve full rate or even rates higher than one for any number of transmit antennas at the expenses of loosing some degree of diversity advantage. On the other hand, if a feedback channel is available and the fading coefficients are known at the transmitter, then maximum diversity advantage can be achieved with transmit antenna selection. In this paper, a new full-rate nonorthogonal space-time block code for three transmit antennas is proposed that needs only two RF chains, has decoding delay equal to two, and whose maximum likelihood decoder based on linear processing is the same as that of the Alamouti code. We then introduce hybrid transmit antenna/code selection. Based on the instantaneous fading coefficients, the transmitter selects either the proposed code with the best three antennas or the Alamouti code with the best pair of antennas. Simulations results for up to six transmit antennas and one receive antenna indicate that full diversity is achieved in spite of using a nonorthogonal code. With this simple example, coding gains of up to 0.4 dB over the pure antenna selection with Alamouti, proposed by Gore and Paulraj (2002), is also observed.