This paper presents a novel sequential estimator for the direction-of-arrival and polynomial coefficients of wideband polynomial-phase signals impinging on a sensor array. Addressing the computational challenges of Maximum-likelihood estimation for this problem, we propose a method leveraging random sampling consensus (RANSAC) applied to the time-frequency spatial signatures of sources. Our approach supports multiple sources and higher-order polynomials by employing coherent array processing and sequential approximations of the Maximum-likelihood cost function. We also propose a low-complexity variant that estimates source directions via angular domain random sampling. Numerical evaluations demonstrate that the proposed methods achieve Cramér-Rao bounds in challenging multi-source scenarios, including closely spaced time-frequency spatial signatures, highlighting their suitability for advanced radar signal processing applications.

This paper presents an exhaustive review of recent advancements in interference mitigation strategies for automotive radar systems, focusing on the intrinsic characteristics of interference across various radar modulation schemes. It categorizes these strategies into four main groups based on their inherent properties, facilitating a detailed comparative analysis. The primary objective is to enhance understanding in this crucial field and bridge the gap between existing research and comprehensive reviews. Additionally, the paper highlights the practical challenges of implementing these strategies, providing insightful implications for engineering and industry practices. Importantly, it emphasizes the critical role of regulatory agencies and the need for cross-disciplinary collaboration to tackle the challenges associated with interference mitigation effectively.

Phase-modulated continuous wave (PMCW) is a digital modulation waveform being investigated for modern automotive radar systems as a potential alternative to commonly used frequency-modulated continuous wave (FMCW) waveforms. In prior research, we introduced stepped-frequency PMCW (SF-PMCW), a variant of PMCW, which generates a synthetic bandwidth across multiple pulses, allowing lower sampling rates and more cost-effective analog-to-digital converters (ADCs). However, utilizing stepped-frequency waveforms introduces a quadratic phase shift, which causes dispersion in the range-Doppler map, thereby reducing the likelihood of target detection. This work proposes an approach aiming to compensate for the quadratic phase shift. This method decreases the effects of energy dispersion, thereby significantly enhancing target detection performance.

This article proposes a hybrid reverberation model by integrating two conventional models, namely, the multichannel linear prediction (MCLP) model and the spatial coherence model. The late reverberation is divided into two components. One component is modeled using an MCLP model, and the other is modeled using the spatial coherence model. In contrast with the conventional models, the proposed hybrid model increases modeling capacity, especially in the case of long reverberation time. In order to optimally estimate model parameters, joint speech dereverberation and separation is taken into account. The hybrid reverberation model is then used in conjunction with the multichannel nonnegative matrix factorization (MNMF). The method called Hybrid-FastMNMF is proposed by treating the reverberation component modeled by the spatial coherence model as a noise source and estimating its parameters similarly to speech sources. Furthermore, prior knowledge of the spatial coherence matrix is employed to whiten the observations, resulting in another method called Hybrid-FastMNMF-W. Experimental findings demonstrate the proposed methods' superior performance in terms of joint speech dereverberation and separation, and they further justify the efficiency of the proposed hybrid reverberation model.

In this paper, a novel interference mitigation approach using an autoencoder in combination with a traditional interference detection filter is introduced. It is shown that by employing the gated convolution, the encoder has the ability to learn the signal pattern from the remaining interference-free signal. The decoder can recover the interference-contaminated signal segments from the bottleneck representation as computed by the encoder. Experimental results show that the proposed method can provide a remarkable improvement in signal-to-interference-plus-noise ratio (SINR) and preserves its robustness on real radar measurements in severely disturbed scenarios that are more complex than the training dataset.

In this paper, a region-based deep convolutional neural network (R-DCNN) is proposed to detect and classify gestures measured by a frequency-modulated continuous wave radar system. Micro-Doppler (µD) signatures of gestures are exploited, and the resulting spectrograms are fed into a neural network. We are the first to use the R-DCNN for radar-based gesture recognition, such that multiple gestures could be automatically detected and classified without manually clipping the data streams according to each hand movement in advance. Further, along with the µD signatures, we incorporate phase-difference information of received signals from an L-shaped antenna array to enhance the classification accuracy. Finally, the classification results show that the proposed network trained with spectrogram and phase-difference information can guarantee a promising performance for nine gestures.

This paper deals with several original contributions to an automatic target recognition (ATR) system, which is applied to underwater mine classification. The contributions concentrate on feature selection and object classification. First, a sophisticated filter method is designed for the feature selection. This filter method utilizes a novel feature relevance measure, the composite relevance measure (CRM). Feature relevance measures in the literature (e.g., mutual information and relief weight) evaluate the features only with respect to certain aspects. The CRM is a combination of several measures so that it is able to provide a more comprehensive assessment of the features. Both linear and nonlinear combinations of these measures are taken into account. A wide range of classifiers is able to provide satisfactory classification results by using the features selected according to the CRM. Second, in the step of object classification, an ensemble learning scheme in the framework of the Dempster–Shafer theory is introduced to fuse the results obtained by different classifiers. This fusion can improve the classification performance. We propose a reasonable construction of the basic belief assignment (BBA). The BBA considers both the reliability of the classifiers and the support of individual classifiers provided to the hypotheses about the types of test objects. Finally, this ATR system is applied to real synthetic aperture sonar imagery to evaluate its performance.

In this paper we deal with an unsupervised segmentation approach for images given by a synthetic aperture sonar (SAS). The images with objects are segmented into highlight, background and shadow. Since the shape features are extracted from these segmented images, correctness and precision of the segmentation are highly required. We improve the expectation-maximization (EM) methods of Sanjay-Gopal et al. by using the gamma mixture model. Moreover an intermediate step (I-step) based on Dempster-Shafer theory (DST) is introduced between the E- and M-steps of the EM to consider the pixel spatial dependency. Finally, numerical tests are carried out on both synthetic images and SAS images. The results are compared to iterative conditional mode (ICM) and diffused EM (DEM). Our approach provides segmentations with less false alarms and better shape preservation.

This paper presents a new filter method for feature selection, which maximizes a hybrid relevance measure using a sequential forward searching scheme (mHRM-SFS). An individual relevance measure provides the classification information only in a certain aspect. Thus, we choose a modified Relief weight, mutual information, and the information entropy to formalize a hybrid relevance measure (HRM) to identify the most characterizing features. Because of its efficiency, a sequential forward searching scheme is used for maximizing the HRM. The resulting feature selection obtained by mHRM-SFS is appropriate to serve as an input for various classifiers. The mHRM-SFS can also determine the cardinality of the feature selection automatically while choosing the optimal features. Finally, the mHRM-SFS is applied to select features of underwater objects for the classification purpose. The selected features are tested by different classifiers. The classification results are compared to those of 4 existing feature selection methods.