# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
## 1 Introduction  
With the further development of mobile communication industry and the rapid rise of the Internet of Things (IoT), the research demand for wireless localization and sensing technologies has greatly increased. People hope they can achieve comprehensive and in-depth sensing of objects such as localization, identification, and tracking in various scenarios, including location-based services, indoor navigation, and tracking of objects [1, 2]. A variety of indoor localization systems based on various wireless communication technologies have been developed to suit the demanding needs of indoor localization sensing [3, 4], such as wireless fidelity (WiFi) localization [5, 6], Bluetooth localization, ultrawideband (UWB) localization [7, 8], ZigBee localization, and ultrahigh frequency (UHF) RFID localization [9, 10, 11, 12]. Among these, passive UHF RFID technology has gained tremendous attention from academics and industry because of its low cost, broad reading range, ease of use, and unique benefits in the fields of localization and identification at the same time. RFID technology has been widely employed in logistics management, asset tracking, and warehousing inventory control application areas. By simply adding an RFID tag to an item, the details of the item can be read using a backscatter signal using a reader. There are many different types of methods that have been proposed for RFID localization systems, including the conventional time-based methods [13] like time of arrival (TOA) and time difference of arrival (TDOA), but their hardware requirements are extremely high because the localization accuracy depends on the signal bandwidth of the receiving device, line-of-sight transmission between devices, etc. The localization method based onreceived signal strength (RSS) is the most commonly used RFID tag localization method due to its easier and more flexible implementation [14; 15; 16; 17; 18]. The LANDMARC [14] project has pioneered the use of RSS similarity between the target tag and the reference tag for RFID tag localization using fingerprints. However, regrettably, in addition to distance, RSS is also susceptible to background noise from the signal propagation environment, non-line-of-sight factors, and multipath factors, resulting in low localization accuracy in complicated indoor environments. In contrast, approaches relying on phase information can achieve more precise localization since the phase characteristics are more stable and more sensitive to distance. They are basically categorized into holographic-based methods, distance-based methods, and DOA-based methods [19; 20]. Holographic methods first divide the region into grids, then evaluate the likelihood that each grid represents a tag location. Taggorm [21] used phase values to generate differential augmented holograms to obtain the probability that all points become RF sources, and chooses the position with the largest probability as the tag location. BackPos [22] converts the localization problem into a hyperbolic model through using the relationship between phase difference and distance difference of signals received by antennas at different locations. HyLoc is proposed in [23], which proposes a simple yet effective RFID tag localization method on the basis of the hyperbolic model. MuSLoc [24] uses an antenna array to accomplish high-precision indoor localization by utilizing a DOA-based methodology combined with a heuristic location search strategy. RF-IDraw [19] similarly employs the DOA through angle information gathered to develop an RFID accurate tracking system to complete the reconstruction of user gestures. The position of each tag was calculated by using the direction of arrival detected by three directional antenna arrays positioned on the outside of the plane in [25]. The use of a multi-signal classification algorithm combined with an RFID tag array to estimate the object pose is implemented in [26].