The four stages (first heart sound (S1), systole, second heart sound (S2), and diastole) of heartbeat sounds recorded by contact seismocardiogram (SCG) reflect the health of the heart, but these stages are challenging to measure by noncontact millimeter wave radar. If the sampling rate of millimeter wave radar is increased, this will increase the amount of data storage needed for the long-term monitoring of human vital signs. This article presents an algorithm for reconstructing the envelope of high-frequency heart sound signals using low-frequency millimeter wave radar signals, as well as a heart sound envelope segmentation algorithm based on peak points. Its design principle is a combination of signal processing and a transformer network, which is called T-HSER. This technique maps the low-frequency radar signal into a high-frequency heart sound envelope signal through the transformer network and determines the four different stages of the heart sound using appropriate thresholds. Based on the training of more than 30000 heartbeats of 25 healthy subjects and the prediction evaluation of six subjects, the T-HSER algorithm is shown to reconstruct the high-frequency heart sound envelope signal with high correlation. Moreover, the mean correlation can reach 0.85 on one minute of data, which is higher than that of the bidirectional long short-term memory algorithm, and can effectively distinguish the four stages of the heart sound so that the mean absolute error (MAE) between the predicted value and the ground truth of S1 and S2 is within a tolerable range (70 ms). At the same time, the algorithm is suitable for low sampling rate radar, which greatly reduces the amount of data storage required.

In passive Internet of Things, existing synthetic aperture-based 3D localization methods face many challenges, such as high computational load, a large aperture of a virtual antenna array, and sensitivity to noise. To address these challenges, this paper develops a synthetic aperture scheme for integrated localization and navigation, which implements the localization algorithm with a trajectory generated by the navigation algorithm. The localization problem is formulated by multidimensional scaling, which exploits phase differences involving the spatial information between target tags and a virtual antenna array. The new formulation allows the system to provide an accurate location estimate with a large moving step and sparse virtual antenna array of narrow apertures. The navigation problem is formulated to decrease errors of distance differences. Moreover, a navigation criterion is established to determine the feasibility of a virtual antenna position based on phase measurements, and an efficient navigation algorithm is proposed to find such a feasible point. Extensive numerical results validate our theoretical analysis and the performance of the proposed scheme.

Radio-frequency identification (RFID) and computer vision (CV) technology are widely employed in intelligent storage systems for sensing, locating, identifying, and monitoring storage cargo. However, both of them have applicability scenarios and limitations. In this article, we propose a system for spatial perception of storage cargo based on the fusion of RFID and CV data. Specifically, we employ a mobile robot carrying an RFID reader and an RGB camera to move between shelves. The reader is connected to two vertically deployed antennas that receive phase values from the tags on the cargo to perform phase unwrapping. Then, we construct a linear system of equations to solve synthetic aperture radar (SAR)-based 3-D localization to obtain the location of the target tag efficiently and accurately. For the images of the shelves captured by the RGB camera, we use image localization techniques based on color and texture features to obtain the pixel coordinates of the cargo in the image. Since the sampling range of the RFID reader antenna is larger than the shooting range of the RGB camera, we use the coherent point drift (CPD) algorithm with threshold judgment to match the localization results of the two subsystems, enabling us to display the inherent information of the target cargo on the plane rendering. Our proposed system is evaluated through various experiments, and the results show that our localization algorithm achieves high accuracy in 3-D space, and the matching algorithm has high robustness to the number of cargo and missing cargo or tags.

Weighted multidimensional scaling (WMDS), an algorithm extending multidimensional scaling (MDS), has been utilized in a broad spectrum of localization. However, there are still two unsolved theoretical questions: 1) The estimator provided by MDS depicts a relative placement of targets which require further Procrustes analysis to recover the actual placement, referred to as the absolute placement. This fact produces a question: Does the estimator of WMDS is still relative? If not, what underlying mechanisms assert the absoluteness? 2) It has been proved that WMDS attains the Cramér–Rao lower bound (CRLB) when the ranging distribution is Gaussian. Does it hold for a general distribution? If not, what restrictions on distributions are required? Motivated by such theoretical incompleteness, this article offers an in-depth theoretical analysis on WMDS in the scheme of range-based localization. With regard to question 1), we reveal the mechanisms that assert the estimator of WMDS always represents exactly the actual locations and prove that the absoluteness is introduced at the moment of variable separation and all the subsequent matrix equation transformations preserve the absoluteness. As for question 2), the functional behaviors and maximum condition of CRLB are examined under a general ranging distribution. Then, via the comparison to the variance of the WMDS estimator, the stationary condition on ranging distributions for WMDS to attain the CRLB is provided. Extensive simulations are performed to validate the theoretical conclusions numerically. And there exists conformity between numerical and theoretical results.

In fully-connected passive RFID backscatter networks, it is challenging to provide accurate range estimations due to complex channels. Facing this problem, we propose a differential analysis based anti-multipath technique by introducing a reference tag. Through the linear difference between target tag received power measurements with the reference tag activated and not, the power with respect to the reader-reference-target link is separated out from the mixed measurements, achieving the mitigation of multipath and accurately ranging. Under distributed localization schemes, after breaking down the full network into a series of fragments, it is vital but challenging to determine how to assemble them satisfactorily. VIABLE, virtual-actual assembling algorithm, is proposed to achieve distributed and cooperative localization. The virtual assembling phase rectifies the fragments over and over until their errors converge, which enables the mining of a satisfactory and adaptive assembling order. Subsequently, the actual phase assembles the rectified fragments together with that order and accomplishes the overall localization accurately. The theoretical analysis of performance limit is presented via the derivation of Cramér-Rao lower bound approximated by a particle approach. Extensive simulations demonstrate that our proposed framework outperforms existing algorithms for cooperative localization.

Radio-frequency identification (RFID) electronic license plate (RF-ELP) has been widely used to enable various automatic vehicle identification applications. Endowing RF-ELP with mobile vehicle sensing capabilities, such as localization and speed measurement is of practical importance, yet there is no solution on the shelf. Moreover, the position information is essential for accurate speed measurement, while the related RFID-based vehicular localization and indoor mobile localization methods suffer from at least one of the following major limitations: 1) difficult to deploy in practice; 2) requiring moving speed in advance; 3) only working for indoor-speed vehicles; and 4) not well compatible to frequency-hopping mechanism. To overcome the above limitations, this article proposes an RF-ELP-based mobile vehicle sensing (RESensing) system. RESensing conducts a new signal phase collection strategy to ensure the phase coupling in road-speed cases and converts phases of each interrogation to the relative speed to make it immune to frequency hopping and interinterrogation phase fluctuation. Then, the speed measurement and longitudinal localization are simultaneously performed by solving a nonlinear optimization model. Furthermore, the propagation model and antenna radiation pattern are investigated to facilitate the received signal strength index (RSSI)-based accurate lane-level lateral localization. To our knowledge, RESensing is the first RF-ELP-based speed measurement and localization system for mobile vehicles. The performance of RESensing is evaluated by real experiments under specifications of GB/T 37987 and EPC C1G2, which shows that RESensing achieves the mean speed error ratio of 4.34%, the longitudinal localization error of submeter level, and the lane estimation accuracy of nearly 100%.

In this paper, MUSE, an algorithm enabled by backscattering tag-to-tag network (BTTN) is presented to accomplish simultaneous 2-D localization of large-scale (10 m × 10 m) massive (20$\sim$∼50) passive UHF RFIDs. In BTTNs, the most intractable problem is the high-frequency loss of range measurements. In a particular case of 30 tags to be located with maximum communication range being 3 m, the rate is nearly up to 85.75 percent. In the proposed framework, we utilize relevant knowledge in the theory of graphs to obtain underlying subsets in which tags can communicate with each other and then assemble them stage by stage to achieve overall localization. Theoretical analysis shows that multistage assembly imparts extraordinary characteristics to MUSE: Assembling rectifies fragment maps given some condition, and in later stages prevents errors flowing down into the next stage. Experimental analysis shows that the condition is easy to satisfy. Furthermore, an analytical expression for the Cramér-Rao lower bound is also derived as a benchmark to evaluate the localization performance. Extensive simulations demonstrate that MUSE outperforms existing algorithms for simultaneous localization.

Radio frequency identification (RFID) technology, which is one of the important implementation ways of Internet-of-Things (IoT), has achieved much attention in indoor localization areas. Passive ultrahigh frequency (UHF) RFID tag localization has a great development recently. Most of traditional passive UHF RFID localization algorithms can only achieve the position of one tag at a time while multitag localization is desired in many RFID applications. In this paper, we proposed the weighted multidimensional scaling (WMDS) based on received signal strength (RSS) method and the tag-to-tag communication system to achieve multitag cooperative localization. The targets are marked with passive UHF RFID tags. RSS method is used to determine the distance between readers and target tags through channel models. We can also obtain the estimated distances between target tags by the tag-to-tag communication system. The estimated locations of the tags are determined by the calculation of Euclidean distance matrix through a few iterations. Simulation results show that the WMDS algorithm achieves higher localization accuracy than traditional algorithms and the cooperation between tags improves the localization accuracy.