Gongliang Liu

Corrected Continuous Correlation Filter for Long-Term Tracking

Recently, a large number of visual tracking algorithms based on discriminative correlation filter have been proposed with demonstrated success. However, most of algorithms cannot well handle long-term videos in which the locating error may accumulate and lead to drifting or tracking failure. Hence, it is of great importance to design a robust long-term tracker which can effectively alleviate tracking drift and redetect the object in case of tracking failure. In this paper, a continuous correlation filter has been proposed to achieve subpixel object locations in continuous domain. For scale estimation, we present a novel multipyramid strategy and the optimal scale tracker is used to correct object locating error in return. Meanwhile, we learn an online random fern classifier to redetect the target in case of tracking failure. By analyzing the confidence of predicted location, we update the translation model conservatively by the reliable targets throughout the sequence. To evaluate the proposed algorithm, extensive experiments are conducted on a benchmark with 100 video sequences, which demonstrate that our tracking mechanism is well fit to tackle long-term sequences and outperforms the state-of-the-art methods.

Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks

Information acquisition in underwater sensor networks is usually limited by energy and bandwidth. Fortunately, the received signal can be represented sparsely on some basis. Therefore, a compressed sensing method can be used to collect the information by selecting a subset of the total sensor nodes. The conventional compressed sensing scheme is to select some sensor nodes randomly. The network lifetime and the correlation of sensor nodes are not considered. Therefore, it is significant to adjust the sensor node selection scheme according to these factors for the superior performance. In this paper, an optimized sensor node selection scheme is given based on Bayesian estimation theory. The advantage of Bayesian estimation is to give the closed-form expression of posterior density function and error covariance matrix. The proposed optimization problem first aims at minimizing the mean square error (MSE) of Bayesian estimation based on a given error covariance matrix. Then, the non-convex optimization problem is transformed as a convex semidefinite programming problem by relaxing the constraints. Finally, the residual energy of each sensor node is taken into account as a constraint in the optimization problem. Simulation results demonstrate that the proposed scheme has better performance than a conventional compressed sensing scheme.

The Design of Inter-satellite Link Topology Based on Time-Slot Allocation Technology

Inter-satellite networking is the development trend of satellite navigation system through inter-satellite link technologies. How to schedule inter-satellite link resources is the key to whether the navigation satellite constellation network has the best performance in the time division multiple access system. To achieve this goal, this paper assumes a linking rule based on a multi-layer satellite constellation and constructs a corresponding mathematical model to describe the linking process of inter-satellite links. Based on the assumed linking rule and the proposed mathematical model, a time-division topology design scheme is proposed. The process of data transmission is simulated with the simulation software MATLAB and STK. The results show that the inter-satellite network topology generated under this scheme gets a very considerable promotion on inter-satellite measurement accuracy and transmission characteristics.

Designing of Environmental Information Acquisition and Reconstruction System Based on Compressed Sensing

At present, the collection of environmental information is mostly accomplished by sensors. In order to reduce the redundancy of sensor data collection, reduce the energy consumption of nodes, improve the service life of sensors and reduce the cost of the system, a system that combines compressed sensing reconstruction with sensors is proposed in this paper to collect and reconstruct environmental information. The designed system collects the environment information with a limited number of nodes. Compressed sensing reconstructs all the data of the required area through the optimized OMP algorithm. The final information is displayed by the software based on C# designing. The final result shows that the verification system proposed in this paper can realize the accurate reconstruction of the original environmental information, and it is effective to the collection and processing of complex environmental information.

The Performance of Physical-Layer Network Coding in Asymmetric Rayleigh Fading Two-Way Relay Channels

Up to now, most of the researches about physical-layer network coding (PNC) are based on symmetric two-way relay channels. In this paper, we mainly study PNC in asymmetric two-way relay Rayleigh fading channels and classify the systems into five asymmetric cases. So as to describe the asymmetric cases, we introduce the asymmetric factors. We simulate the bit error rate (BER) performance of PNC in both symmetric and asymmetric cases with asymmetric factors fixed. After that we analyze the BER performance of PNC with one of the asymmetric factors is variable when two channels are relatively unreliable. Finally, we give the conditions that one kind of asymmetric scenario transforming to other kind. Also, in the matter of anti-noise performance, uplink asymmetry has worst performance and the phase asymmetry-downlink is the best case.

Physical-Layer Network Coding Scheme over Asymmetric Rayleigh Fading Two-Way Relay Channels

To improve the bit error rate (BER) performance of physical-layer network coding (PNC) in asymmetric two-way relay channels (TWRC), in this paper, we study a new PNC scheme named bi-quadrature physical-layer network coding (BQ-PNC) for Rayleigh flat fading TWRC. In BQ-PNC scheme, the two users employ quadrature carriers and the relay node use quadrature combining rather than XOR which is very common in other PNC schemes. We give the BER analysis of BQ-PNC and simulate the performance. Theoretical and simulation results show that the proposed scheme can significantly enhanced the BER performance, either in symmetric or asymmetric cases. Especially for uplink asymmetric TWRC, BQ-PNC can provide more than 4 dB gain compared with PNC scheme.

Simultaneous Cooperative Spectrum Sensing and Energy Harvesting in Multi-antenna Cognitive Radio

In cognitive radio (CR), the secondary user (SU) may use more battery energy to perform spectrum sensing, thus decreasing the transmission energy. In order to guarantee the transmission performance, an energy harvesting-based multi-antenna CR is proposed, which lets the SU harvest the radio frequency (RF) energy of the PU signal and the noise to supplement the energy loss. Time splitting model and antenna splitting model have been proposed to realize the simultaneous cooperative spectrum sensing and energy harvesting for multi-antenna CR, in which cooperative spectrum sensing, energy harvesting and data transmission can be performed in one SU. The joint resource allocations of these two models have been formulated as a class of optimization problems about sensing time, harvesting time, the number of sensing antennas and transmission power. The joint optimization algorithm has been proposed to obtain the optimal solutions to the optimization problems. The simulation results have indicated that the proposed models can achieve larger throughput compared with the sensing-throughput tradeoff model.

Dual-Domain Compressed Sensing Method for Oceanic Environmental Elements Collection with Underwater Sensor Networks

Bandwidth and energy constraints of underwater wireless sensors networks necessitate an efficient data transmission between sensor nodes and the fusion center. This paper considers the data gathering underwater networks for monitoring oceanic environmental elements (e.g. temperature, salinity) and only a portion of measurements from sensors allows for oceanic information map reconstruction under compressed sensing (CS) theory. By utilizing the spatial sparsity of active sensors’ data, we introduce an activity and data detection based on CS at the receiver side resulting in an efficient data communication by avoiding the necessity of conveying identity information. For an interleave division multiple access (IDMA) sporadic transmission, CS-CBC detection that combines the benefits from chip-by-chip (CBC) multi-user detection and CS detection is proposed. Further, by successively exploring the sparsity of sensor data in spatial and frequency domain, we propose a novel efficient data gathering scheme named Dual-domain compressed sensing (DCS). Simulation results validate the effectiveness of the proposed scheme compared to IDMA-CS scheme and an optimal sensing probability problem related to minimum reconstruction error is explored.

Modeling and Performance Analysis of Multi-layer Satellite Networks Based on STK

With the difference of satellite altitude, there are always some inherent defects in the traditional single-layer satellite networks. In this paper, in order to improve the performance of the single-layer networks, a multi-layer satellite network model composed of LEO/MEO/GEO and inter satellite link is proposed. In this model, the LEO and MEO layers are used as the access layer, and the data transmission is carried out to the ground. As the core layer, the GEO layer is responsible for the management of the whole network and the link assignment. Then modeling the network based on the STK satellite simulation platform and carrying out the simulation analysis of ground coverage, the performance of the inter satellite link and the link transmission. Theoretical analysis and simulation results show that the design of multi-layer satellite network is reasonable and effective, and also can be used in the construction of the integrated satellite-terrestrial networks.

Green Resource Allocation in Intelligent Software Defined NOMA Networks

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) is a promising technique for fifth generation wireless communications. In NOMA, multiple users can access the same frequency-time resource simultaneously and multi-user signals can be separated successfully with SIC. In this paper, with recent advances in software-defined networking (SDN), an architecture of SDN-NOMA network was proposed and the SDN controller has a global view of the network. We aim to investigate the resource allocation algorithms for the virtual resource blocks (VRB) assignment and power allocation for the downlink SDN-NOMA network. Different from the existing works, here, energy efficient dynamic power allocation in SDN-NOMA networks is investigated with the constraints of QoS requirement and power consumption. The simulation results confirm that the proposed scheme of SDN-NOMA system yields much better sum rate and energy efficiency performance than the conventional orthogonal frequency division multiple access scheme.