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Cooperative perception is a promising technique for enhancing the perception capabilities of automated vehicles through vehicle-to-everything (V2X) cooperation, provided that accurate relative pose transforms are available. Nevertheless, obtaining precise positioning information often entails high costs associated with navigation systems. Moreover, signal drift resulting from factors such as occlusion and multipath effects can compromise the stability of the positioning information. Hence, a low-cost and robust method is required to calibrate relative pose information for multi-agent cooperative perception. In this paper, we propose a simple but effective inter-agent object association approach (CBM), which constructs contexts using the detected bounding boxes, followed by local context matching and global consensus maximization. Based on the matched correspondences, optimal relative pose transform is estimated, followed by cooperative perception fusion. Extensive experimental studies are conducted on both the simulated and real-world datasets, high object association precision and decimeter level relative pose calibration accuracy is achieved among the cooperating agents even with larger inter-agent localization errors. Furthermore, the proposed approach outperforms the state-of-the-art methods in terms of object association and relative pose estimation accuracy, as well as the robustness of cooperative perception against the pose errors of the connected agents. The code will be available at https://github.com/zhyingS/CBM.

Cooperative perception is challenging for safety-critical autonomous driving applications.The errors in the shared position and pose cause an inaccurate relative transform estimation and disrupt the robust mapping of the Ego vehicle. We propose a distributed object-level cooperative perception system called OptiMatch, in which the detected 3D bounding boxes and local state information are shared between the connected vehicles. To correct the noisy relative transform, the local measurements of both connected vehicles (bounding boxes) are utilized, and an optimal transport theory-based algorithm is developed to filter out those objects jointly detected by the vehicles along with their correspondence, constructing an associated co-visible set. A correction transform is estimated from the matched object pairs and further applied to the noisy relative transform, followed by global fusion and dynamic mapping. Experiment results show that robust performance is achieved for different levels of location and heading errors, and the proposed framework outperforms the state-of-the-art benchmark fusion schemes, including early, late, and intermediate fusion, on average precision by a large margin when location and/or heading errors occur., Accepted by IEEE IV 2023

Modern wireless communication systems operating at high carrier frequencies are characterized by a high dimensionality of the underlying parameter space (including channel gains, angles, delays, and possibly Doppler shifts). Estimating these parameters is valuable for communication purposes, but also for localization and sensing, making channel estimation a critical component in any joint communication and localization or sensing application. The high dimensionality make it difficult to use search-based methods such as maximum likelihood. Search-free methods such as ESPRIT provide an attractive alternative, but require a complex decomposition step in both the tensor and matrix version of ESPRIT. To mitigate this, we propose, develop, and analyze a reduced complexity beamspace ESPRIT method. Complexity is reduced both by beampace processing as well as low-complex implementation of the singular value decomposition. A novel perturbation analysis provides important insights for both channel estimation and localization performance. The proposed method is compared to the tensor ESPRIT method, in terms of channel estimation, communication, localization, and sensing performance, further validating the perturbation analysis., Comment: 17 pages, 7 figures

Future intelligent transport systems depend on the accurate positioning of multiple targets in the road scene, including vehicles and all other moving or static elements. The existing self-positioning capability of individual vehicles remains insufficient. Also, bottlenecks in developing on-board perception systems stymie further improvements in the precision and integrity of positioning targets. Vehicle-to-everything (V2X) communication, which is fast becoming a standard component of intelligent and connected vehicles, renders new sources of information such as dynamically updated high-definition (HD) maps accessible. In this paper, we propose a unified theoretical framework for multiple-target positioning by fusing multi-source heterogeneous information from the on-board sensors and V2X technology of vehicles. Numerical and theoretical studies are conducted to evaluate the performance of the framework proposed. With a low-cost global navigation satellite system (GNSS) coupled with an initial navigation system (INS), on-board sensors, and a normally equipped HD map, the precision of multiple-target positioning attained can meet the requirements of high-level automated vehicles. Meanwhile, the integrity of target sensing is significantly improved by the sharing of sensor information and exploitation of map data. Furthermore, our framework is more adaptable to traffic scenarios when compared with state-of-the-art techniques.

In this paper, the l1 norm penalty on the filter coefficients is incorporated in the least mean absolute deviation (LAD) algorithm to improve the performance of the LAD algorithm. The performance of LAD, zero-attracting LAD (ZA-LAD) and reweighted zero-attracting LAD (RZA-LAD) are evaluated for linear time varying system identification under the non-Gaussian (alpha-stable) noise environments. Effectiveness of the ZA-LAD type algorithms is demonstrated through computer simulations.