Trellis-Based Equalization for Sparse ISI Channels Revisited

Sparse intersymbol-interference (ISI) channels are encountered in a variety of high-data-rate communication systems. Such channels have a large channel memory length, but only a small number of significant channel coefficients. In this paper, trellis-based equalization of sparse ISI channels is revisited. Due to the large channel memory length, the complexity of maximum-likelihood detection, e.g., by means of the Viterbi algorithm (VA), is normally prohibitive. In the first part of the paper, a unified framework based on factor graphs is presented for complexity reduction without loss of optimality. In this new context, two known reduced-complexity algorithms for sparse ISI channels are recapitulated: The multi-trellis VA (M-VA) and the parallel-trellis VA (P-VA). It is shown that the M-VA, although claimed, does not lead to a reduced computational complexity. The P-VA, on the other hand, leads to a significant complexity reduction, but can only be applied for a certain class of sparse channels. In the second part of the paper, a unified approach is investigated to tackle general sparse channels: It is shown that the use of a linear filter at the receiver renders the application of standard reduced-state trellis-based equalizer algorithms feasible, without significant loss of optimality. Numerical results verify the efficiency of the proposed receiver structure.Comment: To be presented at the 2005 IEEE Int. Symp. Inform. Theory (ISIT 2005), September 4-9, 2005, Adelaide, Australi

A novel method for surface to subsea localization utilizing a modified hough transform

A new approach for acoustic localization of a fixed subsea transponder using a surface vessel equipped with a transceiver and global positioning system (GPS) based on a modified Hough transform (MHT) is presented. The MHT developed in this work is used to determine the latitude and longitude coordinates of a transponder utilizing acoustic range and GPS data gathered by the surface vessel while traveling a particular route. Various survey scenarios for a single seabed transponder have been simulated and studied considering both, accurate and inaccurate ranging, as well as realistic conditions such as different route lengths and inexactly geometrical routes (inter alia ellipse-shaped routes). The MHT-based localization approach may particularly find use in the survey of long baseline transponders. The fixed seabed transponders are provided to enable exploration tasks by acoustic networking in various fields, from science and research covering the seas and oceans (e.g. oceanography, marine biology and geology) to industrial use (e.g. exploration of deep-sea resources and minerals, monitoring of offshore constructions). The simulation results demonstrate that the proposed approach can localize the transponder unambiguously and precisely for accurate ranging. Concerning the impact of uniform ranging uncertainties, e.g. arising from spatio-temporally coherent sound speed variations, it can be concluded that full circle and ellipse routes enable a precise estimate while half and quarter circle as well as ellipse routes enable a positioning accuracy within the millimeter range. In the presence of noisy range measurements, e.g. impacted by GPS errors, the approach can provide root mean squared errors from less than 5 mm to 5 m for ranging with a standard deviation of 7.5 mm and 7.5 m, respectively. The proposed positioning approach outperforms the least-squares estimation when shortened survey routes such as half and quarter ellipse are considered. These route forms accelerate the data gathering process, which are motivated by the reduction of the vessel time and cost for the transponder survey

Enhanced State Estimation Based on Particle Filter and Sensor Data With Non-Gaussian and Multimodal Noise

This paper presents a novel approach to state estimation based on particle filter dealing with measurement data effected by non-Gaussian, multimodal noise. The implementation focusses on autonomous underwater vehicles (AUVs) utilizing data of a magnetic compass and a mechanical scanning sonar for spatial navigation. Nowadays, particle filter approaches often require complicated feature extraction methods culminating in semantic interpretation of the data. This is not suitable for low-cost and low-weight AUVs, because these steps require high computational power. Therefore, efficient CPUs and higher power delivery are required. To test the novel approach, the algorithm is simulated in different scenarios with different parameters. Additionally, the filter is applied to real environment data. Finally, the performance is tested and evaluated by several methods. We demonstrate the computational efficiency and superiority of our method over other approaches through simulations

Equalization of Sparse Intersymbol-Interference Channels Revisited

Sparse intersymbol-interference (ISI) channels are encountered in a variety of communication systems, especially in high-data-rate systems. These channels have a large memory length, but only a small number of significant channel coefficients. In this paper, equalization of sparse ISI channels is revisited with focus on trellis-based techniques. Due to the large channel memory length, the complexity of maximum-likelihood sequence estimation by means of the Viterbi algorithm is normally prohibitive. In the first part of the paper, a unified framework based on factor graphs is presented for complexity reduction without loss of optimality. In this new context, two known reduced-complexity trellis-based techniques are recapitulated. In the second part of the paper a simple alternative approach is investigated to tackle general sparse ISI channels. It is shown that the use of a linear filter at the receiver renders the application of standard reduced-state trellis-based equalization techniques feasible without significant loss of optimality.</p

Activity Segmentation and Fish Tracking From Sonar Videos by Combining Artifacts Filtering and a Kalman Approach

Ecosystems are highly dynamic systems that are constantly changing under the influence of a variety of external factors. This is especially true for marine ecosystems, which are under multiple stresses. The cumulative effects of overexploitation, on the one hand, and the simultaneous manifestation of anthropogenic climate change, on the other, mean that fish stocks are the most endangered components of marine ecosystems. To minimize these vulnerabilities to marine ecosystems and ensure natural and sustainable resource use, monitoring systems must be placed in oceans and seas. Examples of the development of these monitoring systems are provided by the Underwater Fish Observatory (UFO) and UFOTriNet, two projects being conducted by several researchers from marine biology, engineering, and industry in Germany between 2014 and 2016 and between 2019 and 2023, respectively. The systems collect abiotic as well as camera and sonar data to count and analyze fish populations over the seasons. This work proposes a method for robust fish counting using sonar data, supplemented by camera data. To successfully accomplish this task, activity segmentation and object tracking are important steps. Background subtraction is often used as a pre-processing step for stationary sonars. Our proposed method improves this step by bandpass filtering considering the motion of all actors in the sonar data. For the segmentation step, our method uses a simple Gaussian distribution model with positional covariances which are computed directly from the intensity image. The tracking step is performed using a classical Kalman filter which estimates the velocity and position of each object in Cartesian coordinates. Sonar detections in close range of the observation area are compared with camera detections for validation. In addition, automated parameter optimization is used to maximize the correlation with the camera detections. Furthermore, the proposed method is applied to the Caltech fish counting dataset and compared with a deep learning method based on YOLOv5. While YOLO is still superior in detection and counting metrics, the multi object tracking accuracy is somewhat higher with our method

Prefiltering and trellisbased equalization for sparse ISI channels

Abstract — Sparse intersymbol-interference (ISI) channels are encountered in a variety of high-data-rate communication systems. Such channels have a large channel memory length, but only a small number of significant channel coefficients. In this paper, trellis-based equalization of sparse ISI channels is revisited. Due to the large channel memory length, the complexity of maximumlikelihood detection, e.g., by means of the Viterbi algorithm (VA), is normally prohibitive. In the first part of the paper, a unified framework based on factor graphs is presented for complexity reduction without loss of optimality. In this new context, two known reduced-complexity algorithms for sparse ISI channels are recapitulated: The multi-trellis VA (M-VA) and the parallel-trellis VA (P-VA). It is shown that the M-VA, although claimed, does not lead to a reduced computational complexity. The P-VA, on the other hand, leads to a significant complexity reduction, but can only be applied for a certain class of sparse channels. In the second part of the paper, a unified approach is investigated to tackle general sparse channels: It is shown that the use of a linear filter at the receiver renders the application of standard reduced-state trellisbased equalizer algorithms feasible, without significant loss of optimality. Numerical results verify the efficiency of the proposed receiver structure. Index Terms — Trellis-based equalization, sparse ISI channels, complexity reduction, prefiltering. I