A Convex and Global Solution for the P$n$P Problem in 2D Forward-Looking Sonar

The perspective-$n$-point (P$n$P) problem is important for robotic pose estimation. It is well studied for optical cameras, but research is lacking for 2D forward-looking sonar (FLS) in underwater scenarios due to the vastly different imaging principles. In this paper, we demonstrate that, despite the nonlinearity inherent in sonar image formation, the P$n$P problem for 2D FLS can still be effectively addressed within a point-to-line (PtL) 3D registration paradigm through orthographic approximation. The registration is then resolved by a duality-based optimal solver, ensuring the global optimality. For coplanar cases, a null space analysis is conducted to retrieve the solutions from the dual formulation, enabling the methods to be applied to more general cases. Extensive simulations have been conducted to systematically evaluate the performance under different settings. Compared to non-reprojection-optimized state-of-the-art (SOTA) methods, the proposed approach achieves significantly higher precision. When both methods are optimized, ours demonstrates comparable or slightly superior precision.

Rejecting Outliers in 2D-3D Point Correspondences from 2D Forward-Looking Sonar Observations

Rejecting outliers before applying classical robust methods is a common approach to increase the success rate of estimation, particularly when the outlier ratio is extremely high (e.g. 90%). However, this method often relies on sensor- or task-specific characteristics, which may not be easily transferable across different scenarios. In this paper, we focus on the problem of rejecting 2D-3D point correspondence outliers from 2D forward-looking sonar (2D FLS) observations, which is one of the most popular perception device in the underwater field but has a significantly different imaging mechanism compared to widely used perspective cameras and LiDAR. We fully leverage the narrow field of view in the elevation of 2D FLS and develop two compatibility tests for different 3D point configurations: (1) In general cases, we design a pairwise length in-range test to filter out overly long or short edges formed from point sets; (2) In coplanar cases, we design a coplanarity test to check if any four correspondences are compatible under a coplanar setting. Both tests are integrated into outlier rejection pipelines, where they are followed by maximum clique searching to identify the largest consistent measurement set as inliers. Extensive simulations demonstrate that the proposed methods for general and coplanar cases perform effectively under outlier ratios of 80% and 90%, respectively.

Joint Multitarget Detection and Tracking with mmWave Radar

Accurate targets detection and tracking with mmWave radar is a key sensing capability that will enable more intelligent systems, create smart, efficient, automated system. This paper proposes an end-to-end detection-estimation-track framework named MNOMP-SPA-KF consisting of the target detection and estimation module, the data association (DA) module and the target tracking module. In the target estimation and detection module, a low complexity, super-resolution and constant false alarm rate (CFAR) based two dimensional multisnapshot Newtonalized orthogonal matching pursuit (2D-MNOMP) is designed to extract the multitarget's radial distances and velocities, followed by the conventional (Bartlett) beamformer to extract the multitarget's azimuths. In the DA module, a sum product algorithm (SPA) is adopted to obtain the association probabilities of the existed targets and measurements by incorporating the radial velocity information. The Kalman filter (KF) is implemented to perform target tracking in the target tracking module by exploiting the asymptotic distribution of the estimators. To improve the detection probability of the weak targets, extrapolation is also coupled into the MNOMP-SPA-KF. Numerical and real data experiments demonstrate the effectiveness of the MNOMP-SPA-KF algorithm, compared to other benchmark algorithms.

DM-SBL: Channel Estimation under Structured Interference

Channel estimation is a fundamental task in communication systems and is critical for effective demodulation. While most works deal with a simple scenario where the measurements are corrupted by the additive white Gaussian noise (AWGN), this work addresses the more challenging scenario where both AWGN and structured interference coexist. Such conditions arise, for example, when a sonar/radar transmitter and a communication receiver operate simultaneously within the same bandwidth. To ensure accurate channel estimation in these scenarios, the sparsity of the channel in the delay domain and the complicate structure of the interference are jointly exploited. Firstly, the score of the structured interference is learned via a neural network based on the diffusion model (DM), while the channel prior is modeled as a Gaussian distribution, with its variance controlling channel sparsity, similar to the setup of the sparse Bayesian learning (SBL). Then, two efficient posterior sampling methods are proposed to jointly estimate the sparse channel and the interference. Nuisance parameters, such as the variance of the prior are estimated via the expectation maximization (EM) algorithm. The proposed method is termed as DM based SBL (DM-SBL). Numerical simulations demonstrate that DM-SBL significantly outperforms conventional approaches that deal with the AWGN scenario, particularly under low signal-to-interference ratio (SIR) conditions. Beyond channel estimation, DM-SBL also shows promise for addressing other linear inverse problems involving structured interference.

A Modulo Sampling Hardware Prototype and Reconstruction Algorithm Evaluation

Analog-to-digital converters (ADCs) play a vital important role in any devices via manipulating analog signals in a digital manner. Given that the amplitude of the signal exceeds the dynamic range of the ADCs, clipping occurs and the quality of the digitized signal degrades significantly. In this paper, we design a joint modulo sampling hardware and processing prototype which improves the ADCs' dynamic range by folding the signal before sampling. Both the detailed design of the hardware and the recovery results of various state-of-the-art processing algorithms including our proposed unlimited sampling line spectral estimation (USLSE) algorithm are presented. Additionally, key issues that arise during implementation are also addressed. It is demonstrated that the USLSE algorithm successfully recovers the original signal with a frequency of 2.5 kHz and an amplitude 10 times the ADC's dynamic range, and the linear prediction (LP) algorithm successfully recovers the original signal with a frequency of 3.5 kHz and an amplitude 10 times the ADC's dynamic range.

Image-Conditional Diffusion Transformer for Underwater Image Enhancement

Underwater image enhancement (UIE) has attracted much attention owing to its importance for underwater operation and marine engineering. Motivated by the recent advance in generative models, we propose a novel UIE method based on image-conditional diffusion transformer (ICDT). Our method takes the degraded underwater image as the conditional input and converts it into latent space where ICDT is applied. ICDT replaces the conventional U-Net backbone in a denoising diffusion probabilistic model (DDPM) with a transformer, and thus inherits favorable properties such as scalability from transformers. Furthermore, we train ICDT with a hybrid loss function involving variances to achieve better log-likelihoods, which meanwhile significantly accelerates the sampling process. We experimentally assess the scalability of ICDTs and compare with prior works in UIE on the Underwater ImageNet dataset. Besides good scaling properties, our largest model, ICDT-XL/2, outperforms all comparison methods, achieving state-of-the-art (SOTA) quality of image enhancement.

On the Identifiability from Modulo Measurements under DFT Sensing Matrix

Unlimited sampling was recently introduced to deal with the clipping or saturation of measurements where a modulo operator is applied before sampling. In this paper, we investigate the identifiability of the model where measurements are acquired under a discrete Fourier transform (DFT) sensing matrix first followed by a modulo operator (modulo-DFT). Firstly, based on the theorems of cyclotomic polynomials, we derive a sufficient condition for uniquely identifying the original signal in modulo-DFT. Additionally, for periodic bandlimited signals (PBSs) under unlimited sampling which can be viewed as a special case of modulo-DFT, the necessary and sufficient condition for the unique recovery of the original signal are provided. Moreover, we show that when the oversampling factor exceeds $3(1+1/P)$, PBS is always identifiable from the modulo samples, where $P$ is the number of harmonics including the fundamental component in the positive frequency part.

Joint Channel Estimation and Turbo Equalization of Single-Carrier Systems over Time-Varying Channels

Block transmission systems have been proven successful over frequency-selective channels. For time-varying channel such as in high-speed mobile communication and underwater communication, existing equalizers assume that channels over different data frames are independent. However, the real-world channels over different data frames are correlated, thereby indicating potentials for performance improvement. In this paper, we propose a joint channel estimation and equalization/decoding algorithm for a single-carrier system that exploits temporal correlations of channel between transmitted data frames. Leveraging the concept of dynamic compressive sensing, our method can utilize the information of several data frames to achieve better performance. The information not only passes between the channel and symbol, but also the channels over different data frames. Numerical simulations using an extensively validated underwater acoustic model with a time-varying channel establish that the proposed algorithm outperforms the former bilinear generalized approximate message passing equalizer and classic minimum mean square error turbo equalizer in bit error rate and channel estimation normalized mean square error. The algorithm idea we present can also find applications in other bilinear multiple measurements vector compressive sensing problems.

Information-Preserved Blending Method for Forward-Looking Sonar Mosaicing in Non-Ideal System Configuration

Forward-Looking Sonar (FLS) has started to gain attention in the field of near-bottom close-range underwater inspection because of its high resolution and high framerate features. Although Automatic Target Recognition (ATR) algorithms have been applied tentatively for object-searching tasks, human supervision is still indispensable, especially when involving critical areas. A clear FLS mosaic containing all suspicious information is in demand to help experts deal with tremendous perception data. However, previous work only considered that FLS is working in an ideal system configuration, which assumes an appropriate sonar imaging setup and the availability of accurate positioning data. Without those promises, the intra-frame and inter-frame artifacts will appear and degrade the quality of the final mosaic by making the information of interest invisible. In this paper, we propose a novel blending method for FLS mosaicing which can preserve interested information. A Long-Short Time Sliding Window (LST-SW) is designed to rectify the local statistics of raw sonar images. The statistics are then utilized to construct a Global Variance Map (GVM). The GVM helps to emphasize the useful information contained in images in the blending phase by classifying the informative and featureless pixels, thereby enhancing the quality of final mosaic. The method is verified using data collected in the real environment. The results show that our method can preserve more details in FLS mosaics for human inspection purposes in practice.