This paper studies a localization system, where an extremely large-scale antenna array (ELAA) is deployed at the base station (BS) to locate a user equipment (UE) residing in the near-field (Fresnel) region. We propose a novel algorithm, named array partitioning-based location estimation (APLE), for scalable near-field localization. The APLE algorithm is developed based on the basic assumption that, by partitioning the ELAA into multiple subarrays, the UE can be approximated as in the far-field region of each subarray. We establish a Bayeian inference framework based on the geometric constraints between the UE location and the angles of arrivals (AoAs) at different subarrays. Then, the APLE algorithm is designed based on the message-passing principle for the localization of the UE. APLE exhibits linear computational complexity with the number of BS antennas, leading to a significant reduction in complexity compared to existing methods. We further propose an enhanced APLE (E-APLE) algorithm that refines the location estimate obtained from APLE by following the maximum likelihood principle. The E-APLE algorithm achieves superior localization accuracy compared to APLE while maintaining a linear complexity with the number of BS antennas. Numerical results demonstrate that the proposed APLE and E-APLE algorithms outperform the existing baselines in terms of both localization accuracy and computational complexity.

Existing near-field localization algorithms generally face a scalability issue when the number of antennas at the sensor array goes large. To address this issue, this paper studies a passive localization system, where an extremely large-scale antenna array (ELAA) is deployed at the base station (BS) to locate a user that transmits signals. The user is considered to be in the near-field (Fresnel) region of the BS array. We propose a novel algorithm, named array partitioning based location estimation (APLE), for scalable near-field localization. The APLE algorithm is developed based on the basic assumption that, by partitioning the ELAA into multiple subarrays, the user can be approximated as in the far-field region of each subarray. The APLE algorithm determines the user's location by exploiting the differences in the angles of arrival (AoAs) of the sub arrays. Specifically, we establish a probability model of the received signal based on the geometric constraints of the user's location and the observed AoAs. Then, a message-passing algorithm, i.e., the proposed APLE algorithm, is designed for user localization. APLE exhibits linear computational complexity with the number of BS antennas, leading to a significant reduction in complexity compared to the existing methods. Besides, numerical results demonstrate that the proposed APLE algorithm outperforms the existing baselines in terms of localization accuracy.

The application of big data technology and the change of consumers’ concept make the game between supply chain members move from competition to un-ion. Under the premise of considering the impact of promotional spillover effects on the promotion decisions of supply chain members, promotion spillover effect affects the promotion decision of supply chain members. Taking the product reputation as the state variable, this paper studies the joint promotion problem in a two-tier supply chainc, which is composed of a single manufacturer and two retailers with promotion spillovers. This paper constructs three decision-making models: centralized decision-making, decentralized decision-making and cost-sharing decision-making with spillover effect. The results show that the spillover effect only increases the sales promotion effort, product reputation and demand of the centralized decision-making retailers, it has no effect on the retailer’s promotion effort, product reputation and demand of de-centralized decision-making and cost-sharing decision-making. Spillover effect can improve the total profit of supply chain in three decision-making modes: centralized decision-making, decentralized decision-making and cost-sharing decision-making. Under the decision of cost-sharing, manufacturers are more willing to increase the cost-sharing ratio with the increase of spillover effect, and the cost-sharing ratio of manufacturers should be kept in a certain range, if the costsharing ratio exceeds a certain value, it will harm the benefit of the manufacturer, and the profit of the retailer will increase with the increase of the cost-sharing ratio of the manufacturer.

Forward scatter radar (FSR) can achieve target detection by capturing the signal disturbance caused by targets crossing the baseline, which provides a countermeasure to stealth/small targets. Firstly, the FSR net model based on the Global Navigation Satellite System (GNSS) is constructed. Then, the statistical features in the time and frequency domains are used to describe the fluctuation characteristics of the forward-scatter signal. Moreover, the influences of the relative position of the target and baseline on the statistical features are analyzed through dynamic simulations, and the change regulation of time and frequency features is revealed. Finally, a FSR experiment is carried out in an anechoic chamber and the results validate the theoretical analysis. This work provides a meaningful reference for detection and parameter estimation of targets crossing the baseline in a FSR.

The parameter estimation in the forward scatter radar (FSR) network is the focus of attention in the radar field. In this letter, a parameter estimation method by using the crossing time difference and Doppler rate is proposed in the FSR network. The continuous position and velocity estimation of the uniform moving target is realized. Then, the factors affecting the estimation accuracy are theoretically analyzed, including parameter measurement error and receiver locations. The effectiveness and adaptability of the method are validated through Monte Carlo simulations. Compared with the parameter estimation method only based on the crossing time, higher accuracy can be achieved and fewer receivers or transmitters are needed, so it should be easier to be deployed in the real application.

This paper analyzes the cost control and optimization of marketing logistics by using intelligent communication technology, and reports the current situation in smart living. Taking the beer company affiliated to XX Brewery Group for example, factor analysis method and artificial intelligence network theory are adopted to optimize the marketing logistics cost in smart living, suggestions for optimizing the marketing logistics cost of the company under the background of smart living are given as follows: Using intelligent communication technology to unify logistics cost management and financial accounting methods, innovating logistics cost management mechanism through big data technology, comprehensive data mining and intelligent communication network and other technologies to reduce logistics cost.