61 research outputs found
An Analytical Measuring Rectification Algorithm of Monocular Systems in Dynamic Environment
Range estimation is crucial for maintaining a safe distance, in particular for vision navigation and localization. Monocular autonomous vehicles are appropriate for outdoor environment due to their mobility and operability. However, accurate range estimation using vision system is challenging because of the nonholonomic dynamics and susceptibility of vehicles. In this paper, a measuring rectification algorithm for range estimation under shaking conditions is designed. The proposed method focuses on how to estimate range using monocular vision when a shake occurs and the algorithm only requires the pose variations of the camera to be acquired. Simultaneously, it solves the problem of how to assimilate results from different kinds of sensors. To eliminate measuring errors by shakes, we establish a pose-range variation model. Afterwards, the algebraic relation between distance increment and a camera’s poses variation is formulated. The pose variations are presented in the form of roll, pitch, and yaw angle changes to evaluate the pixel coordinate incensement. To demonstrate the superiority of our proposed algorithm, the approach is validated in a laboratory environment using Pioneer 3-DX robots. The experimental results demonstrate that the proposed approach improves in the range accuracy significantly
Sensing-Assisted Communication in Vehicular Networks with Intelligent Surface
The recent development of integrated sensing and communications (ISAC)
technology offers new opportunities to meet high-throughput and low-latency
communication as well as high-resolution localization requirements in vehicular
networks. However, considering the limited transmit power of the road site
units (RSUs) and the relatively small radar cross section (RCS) of vehicles
with random reflection coefficients, the power of echo signals may be too weak
to be utilized for effective target detection and tracking. Moreover,
high-frequency signals usually suffer from large fading loss when penetrating
vehicles, which seriously degrades the quality of communication services inside
the vehicles. To handle this issue, we propose a novel sensing-assisted
communication mechanism by employing an intelligent omni-surface (IOS) on the
surface of vehicles to enhance both sensing and communication (S&C)
performance. To this end, we first propose a two-stage ISAC protocol, including
the joint S&C stage and the communication-only stage, to fulfill more efficient
communication performance improvements benefited from sensing. The achievable
communication rate maximization problem is formulated by jointly optimizing the
transmit beamforming, the IOS phase shifts, and the duration of the joint S&C
stage. However, solving this ISAC optimization problem is highly non-trivial
since inaccurate estimation and measurement information renders the achievable
rate lack of closed-form expression. To handle this issue, we first derive a
closed-form expression of the achievable rate under uncertain location
information, and then unveil a sufficient and necessary condition for the
existence of the joint S&C stage to offer useful insights for practical system
design. Moreover, two typical scenarios including interference-limited and
noise-limited cases are analyzed.Comment: IEEE Transactions on Vehicular Technology, 2023. arXiv admin note:
text overlap with arXiv:2211.0420
Cooperative Cellular Localization with Intelligent Reflecting Surface: Design, Analysis and Optimization
Autonomous driving and intelligent transportation applications have
dramatically increased the demand for high-accuracy and low-latency
localization services. While cellular networks are potentially capable of
target detection and localization, achieving accurate and reliable positioning
faces critical challenges. Particularly, the relatively small radar cross
sections (RCS) of moving targets and the high complexity for measurement
association give rise to weak echo signals and discrepancies in the
measurements. To tackle this issue, we propose a novel approach for
multi-target localization by leveraging the controllable signal reflection
capabilities of intelligent reflecting surfaces (IRSs). Specifically, IRSs are
strategically mounted on the targets (e.g., vehicles and robots), enabling
effective association of multiple measurements and facilitating the
localization process. We aim to minimize the maximum Cram\'er-Rao lower bound
(CRLB) of targets by jointly optimizing the target association, the IRS phase
shifts, and the dwell time. However, solving this CRLB optimization problem is
non-trivial due to the non-convex objective function and closely coupled
variables. For single-target localization, a simplified closed-form expression
is presented for the case where base stations (BSs) can be deployed flexibly,
and the optimal BS location is derived to provide a lower performance bound of
the original problem ...Comment: 14 pages, This work has been submitted to IEEE for possible
publicatio
Duration-adaptive Video Highlight Pre-caching for Vehicular Communication Network
Video traffic in vehicular communication networks (VCNs) faces exponential
growth. However, different segments of most videos reveal various
attractiveness for viewers, and the pre-caching decision is greatly affected by
the dynamic service duration that edge nodes can provide services for mobile
vehicles driving along a road. In this paper, we propose an efficient video
highlight pre-caching scheme in the vehicular communication network, adapting
to the service duration. Specifically, a highlight entropy model is devised
with the consideration of the segments' popularity and continuity between
segments within a period of time, based on which, an optimization problem of
video highlight pre-caching is formulated. As this problem is non-convex and
lacks a closed-form expression of the objective function, we decouple multiple
variables by deriving candidate highlight segmentations of videos through
wavelet transform, which can significantly reduce the complexity of highlight
pre-caching. Then the problem is solved iteratively by a highlight-direction
trimming algorithm, which is proven to be locally optimal. Simulation results
based on real-world video datasets demonstrate significant improvement in
highlight entropy and jitter compared to benchmark schemes
A MANET Routing Algorithm Based on Difference Degree and Stability of Nodes
Aiming at the team-based mobile ad-hoc networks (MANETs), this paper proposes a main-route mechanism by the mobility difference degree of nodes and a backup route mechanism by the nodes stability. In this paper, the whole network is divided into different partitions, and the prediction node computes the changing rate of local topology to determine whether local status broadcast is needed. In order to reduce the similarity of the main route and backup route and minimize the probability of simultaneous failure of the two routes, different routing metrics are used to discover these two routes to ensure reliable data transmission. As a result, the availability of backup route can be increased when the main route fails. While a link is broken, we advance a novel local confirmation method of link interruption and local route reparation. The simulation results shows that our routing algorithm is effective and can improve the network performance significantly
Multi-Rate Base on OFDM in Underwater Sensor Networks
Underwater acoustic communication has the characteristics of multipath effect and frequency selectively attenuation. Aiming at these characteristics, this paper proposes a Multi-Rate model based on channel feature based on OFDM (Orthogonal Frequency Division Multiplexing) technology. With the frequency selectivity of underwater acoustic channel and the link distance, the optimal carrier frequency can be derived. The pilot in OFDM symbol can be used to attain the SNR of each sub-carrier, and the optimal modulation mechanism can be determined by the preset threshold. So we can get the maximal transmission rate under different link distances. This model addresses the problem of ISI (inter-symbol interference) caused by multipath in acoustic channel, and improves the throughput as well as transmission efficiency in underwater sensor networks. The simulation results show that under different link distances, the theoretical bandwidth can be obtained by the frequency selectivity of underwater acoustic channel, different sub-bands and modulation mechanisms can be obtained by channel estimation, and finally the maximal transmission rate can be acquired
A meta-deep-learning framework for spatio-temporal underwater SSP inversion
Sound speed distribution, represented by a sound speed profile (SSP), is of great significance because the nonuniform distribution of sound speed will cause signal propagation path bending with Snell effect, which brings difficulties in precise underwater localization such as emergency rescue. Compared with conventional SSP measurement methods via the conductivity-temperature-depth (CTD) or sound-velocity profiler (SVP), SSP inversion methods leveraging measured sound field information have better real-time performance, such as matched field process (MFP), compressed sensing (CS) and artificial neural networks (ANN). Due to the difficulty in measuring empirical SSP data, these methods face with over-fitting problem in few-shot learning that decreases the inversion accuracy. To rapidly obtain accurate SSP, we propose a task-driven meta-deep-learning (TDML) framework for spatio-temporal SSP inversion. The common features of SSPs are learned through multiple base learners to accelerate the convergence of the model on new tasks, and the model’s sensitivity to the change of sound field data is enhanced via meta training, so as to weaken the over-fitting effect and improve the inversion accuracy. Experiment results show that fast and accurate SSP inversion can be achieved by the proposed TDML method
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