Wireless Localization Techniques Research Articles

Exploring wireless device-free localization technique to assist home-based neuro-rehabilitation.

Home-based movement neuro-rehabilitation is quite necessary when the patient goes back home from hospital. Due to lack of supervision from doctors, rehabilitation at home is often forgotten. As an alternate to doctor-supervision, in this research, we explore the wireless device-free localization technique to assist the rehabilitation procedure. The localization technique can judge whether the patient is near the rehabilitation equipment and even obtain the movement trajectory. The most challenging problem in the wireless device-free localization system is that the received-signal-strength (RSS) of the electromagnetic-wave is unpredictable, which increases the localization error. How to select the informative RSS is pretty important. This research proposes a new criterion (i.e., fluctuation-level) to select the informative RSS. Experimental results show the effectiveness of the proposed fluctuation-level in reducing the localization error.

Efficacy of the Radiofrequency Identification Technique in Breast Cancer Patients: A Single Institution Retrospective Study.

Breast conserving surgery is an excellent option in the treatment of breast cancer. To achieve a good result with this modality, a surgeon needs to identify and excise the tumor with adequate margins. The radiofrequency identification (RFID) technique is a wireless localization technique used for intraoperative breast lesion identification. We assessed the efficacy and outcomes of the RFID technique in breast cancer patients at our institution. This is a single institution, retrospective study (BSMH 22-02X-MWH) of 73 patients. We analyzed the medical records of women with biopsy-proven breast cancer from June 2020 to August 2022; participants received surgical care at Mercy Health West Hospital. Data collected included demographics, clinicopathological characteristics, and surgical procedure. The primary objective was to determine the safety and efficacy of RFID. The secondary objective was to assess the impact of obesity and breast density on the RFID outcomes. A total of 73 female patients met the eligibility criteria with stage I (59%) and grade 1 (51%) breast cancer with mean age of at diagnosis of 66.8 years and mean body mass index of 31.4 kg/m2. Patients had invasive ductal carcinoma (61%), hormonal positive (56%), and human epidermal growth factor receptor 2 negative (68%) disease. All RFID tags were placed under image guidance with 100% accuracy of placement with no evidence of migration or procedure revision. Ninety percent of patients had free surgical margins and only seven patients needed margin re-excision with successful removal of the lesion and the tag. RFID localization technique is a safe, effective and reliable procedure that results in favorable patient outcomes and quality of life.

When Wireless Localization Meets Artificial Intelligence: Basics, Challenges, Synergies, and Prospects

The rapid development of information communication and artificial intelligence (AI) technology is driving innovation in various new application fields such as autonomous driving, augmented reality, and the metaverse. In particular, the advancement of wireless localization technology plays a great role in these cutting-edge technologies. However, traditional wireless localization systems rely on the global navigation satellite system (GNSS), which is ineffective in indoor or underground environments. To overcome this issue, indoor positioning systems (IPS) have gained attention, and various localization techniques utilizing wireless communication were studied. Subsequently, AI technologies are improving the performance of wireless localization and addressing problems that were previously difficult to solve. In this paper, we summarize wireless localization techniques and define the factors that impede their performance. Furthermore, we categorize AI algorithms and present examples of how they can be used to address these hindering factors. Finally, we propose open research directions and prospects for AI-assisted wireless localization.

A Convergent Iteration Method for 3-D AOA Localization

Using angle-of-arrival (AOA) measurements from several sensors to locate a target in 3-D space is one of the commonly used wireless localization techniques. A challenge of this technique is that the weighted least-squares (WLS) needed for it tend to incur the threshold effect when the noise level is high or when the localization geometry is poor, because its implementation requires the inversion of a matrix whose condition number will be large under such conditions. This paper presents an iterative method that does not require any matrix operation and is proved to be convergent with any initial value. Additionally, it outperforms the WLS-based methods as validated by comprehensive simulation results.

Radar reflector guided axillary surgery in node positive breast cancer patients

ABSTRACT Introduction In patients with non-palpable breast cancer, the availability of wireless localization techniques facilitates removal of the target lesion. One such technique uses a radar reflector for localization (RRL). This study evaluates the feasibility and effectiveness of RRL to guide excision of axillary lymph nodes in patients with node-positive breast cancer. Methods Our Breast Cancer Database was queried for patients diagnosed with breast cancer, between 5/2017 and 10/2021, who underwent preoperative placement of a radar reflector into a biopsy proven axillary lymph node. Clinicopathologic data were reported using descriptive statistics. Results Twenty patients underwent preoperative placement of a radar reflector into the axilla. Intraoperatively, the clip and radar reflector were successfully removed in all patients. Among the 10 patients treated with NAC, 5 patients achieved an axillary pathologic complete response (pCR) and were spared a complete axillary lymph node dissection (cALND). Among the entire cohort, RRL resulted in a 53% reduction in the number of lymph nodes removed. Conclusions Wireless localization of axillary lymph nodes is safe and feasible. The technique ensures excision of biopsy proven positive axillary lymph nodes and enables a targeted approach to assessing the axilla, both in the setting of NAC and upfront surgery.

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Discussion

Feasibility study of a novel wireless localization technique using radiofrequency identification markers for small and deeply located lung lesions.

ObjectivesTo evaluate the safety and efficacy of a novel wireless localization technique that uses radiofrequency identification markers for small and deep lung lesions.MethodsPreliminary use of the device was retrospectively evaluated in 2 Japanese centers. Under general anesthesia, a marker was placed as close as possible to the tumor via computed tomography-guided bronchoscopy in a hybrid operation theater. Surgeons located the marker without lung palpation using a detection probe the tone of which changed to indicate the marker-probe distance. Efficacy was defined as functional marker placement (bronchoscopy time and marker position) and deep margin distance.ResultsTwelve markers were placed for 11 lesions (mean size, 6.8 ± 2.7 mm) located at a mean depth from the pleura of 11.4 ± 8.4 mm (range = 0-26.0 mm). Of 12 markers, 7 markers (58.3%) were placed within 10 mm from the lesion in 25.5 ± 14.4 minutes. For the 11 wedge resections, markers were placed at a mean distance of 6.7 mm (range, 0-13.0 mm) from the lesion and a mean distance of 14.4 mm (range, 3.0-42.0 mm) from the pleura. All markers were recovered without complications, and all tumors were resected with negative margins. For 5 lesions >10 mm deep to the pleura (mean depth, 18.9 ± 5.5 mm; range, 11.0-26.0 mm), the median depth of the surgical margin was 11.6 ± 2.1 mm (range, 9.0-14.0 mm).ConclusionsRadiofrequency identification marking was safe and precisely localized small lung lesions, including their depth.

An improving sparse coding algorithm for wireless passive target positioning

With the developments of smart city, wireless passive localization (WPL) technique that detects targets without carrying any devices draws a lot of research attention. Some machine learning methods, such as sparse coding and deep learning, have been developed to formulate the WPL as a classification problem. Despite these methods have been proved to be effective in WPL, it remains hot topics to design a precise objective function that could detect the position of the targets with high accuracy and robustness. In this paper, we exploited a sparsity regularizer, named log-regularizer, in the classification objective function. By virtue of the distinguished ability in measuring sparsity, our proposed improved sparse coding algorithm (ISCA) with log-regularizer could position targets accurately with robust performance even in the challenging environments Experimental results show that our proposal achieves better results compared with five other machine learning algorithms. Even the input data is severely polluted by noise (SNR = −10 dB), the proposed method could still obtain high localization accuracy of 99.4%.

Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks

In this paper, we proposed a new wireless localization technique based on the ideology of social network analysis (SNA), to study the different properties of networks as a graph. Centrality is a main concept in SNA, so we propose using closeness centrality (CC) as a measurement to denote the importance of the node inside the network due to its geo-location to others. The node with highest degree of CC is chosen as a cluster heads, then each cluster head can form its trilateration process to collect data from its cluster. The selection of closest cluster based on CC values, and the unknown node’s location can be estimated through the trilateration process. To form a perfect trilateration, the cluster head chooses three anchor nodes. The proposed algorithm provides high accuracy even in different network topologies like concave shape, O shape, and C shape as compared to existing received signal strength indicator (RSSI) techniques. Matlab simulation results based on practical radio propagation data sets showed a localization error of 0.32 m with standard deviation of 0.26 m.

Analysis of Accuracy and Precision of WLAN Position Estimation System based on RSS

The coordinates of the position of a wireless access point are the main goal in the wireless localization technique. In common, outdoor wireless localization techniques use the trilateration method by installing several devices used as anchors, and this makes the cost for installation and maintenance higher. An outdoor wireless localization system that is more efficient, effective, and flexible but still accurate and precise becomes indispensable. This paper proposes a wireless position estimation system based on a received signal strength (RSS) value called WLAN Position Estimation System (WLAN PES) integrating WLAN Distance Estimation System (WLAN DES) with WLAN PES formula. To confirm the accuracy and precision of WLAN PES, this paper focuses on the analysis of WLAN PES testing conducted at ten points in which each measurement point had coordinates and angles that are different from one another. The test point was made to circle the target WLAN access point. The distance of each test point was 1000 meters against the targeted WLAN access point. In each of these test points, the WLAN finder reads RSSfnd. Then, the system calculated the estimated distance value using WLAN DES based on the RSSfnd value. After the system obtained a distance estimation value, the estimated position value was calculated with the WLAN PES formula. In addition to the distance estimation value, WLAN PES formula required some variables such as latitude and longitude coordinates from the WLAN finder position, and the bearing of the WLAN finder. WLAN PES was found to be capable of determining the estimated position coordinates of a WLAN access point with an accuracy value and precision value of 93.26% and 98.77%, respectively.

A Novel Hybrid RSS and TOA Positioning Algorithm for Multi-Objective Cooperative Wireless Sensor Networks

Due to the significance of positioning in complicated electromagnetic environment of its variety of applications, wireless localization techniques have attracted significant research interests with the trends moving toward boosting the capabilities of multi-objective cooperative networks. This paper addresses the problem of locating multiple targets in 3-D cooperative wireless sensor network. A novel hybrid received signal strength (RSS) and time-of-arrival (TOA) positioning scheme is proposed. Employing RSS and TOA measurements, four valuable expressions are drawn. The TOA and RSS technologies are combined with path loss difference variable being introduced. Based on the weighted least squares criterion, the rough solutions of multiple targets positions are derived. Finally, the locations of obtained targets are optimized by cooperative wireless communication between the target and another target. The location accuracy of the proposed estimation algorithm is analyzed and compared with the conventional RSS and TOA estimation algorithms through simulation experiments. The simulation result demonstrates that the proposed hybrid RSS and TOA localization algorithm for multi-objective cooperative wireless networks outperforms traditional approaches.

A System Concept for Online Calibration of Massive MIMO Transceiver Arrays for Communication and Localization

Massive multiple-input multiple-output (MIMO) techniques are being considered for the fifth generation (5G) mobile communication systems in order to deliver high multiplexing gain. However, hardware impairments like quadrature imbalance in mixers violate the requirement for channel reciprocity and may change, e.g., with temperature or while aging. In addition, advanced wireless localization techniques and the generation of predefined beam patterns require knowledge about all antenna phase center positions and the time and phase delay of all transmit and receive channels. Thus, an efficient online compensation method is needed that scales well for very large numbers of transceiver modules. We propose to extend the transmitter with a small measurement feature at the transmitter output based on one uncalibrated power detector per module as well as a single, external four-element backscatter array for the entire matrix. These enhancements facilitate a fast and efficient iterative calibration, which recognizes and mitigates all major error sources. Beside optimal communication throughput and energy efficiency, it thereby brings localization capabilities to mobile networks as an additional major benefit. For verification, a system of multiple cost-efficient 5.8-GHz massive MIMO transceivers with 150-MHz bandwidth and a backscatter array has been implemented. Measurement results demonstrate the capability of the proposed concept to efficiently compensate major error sources as well as its robustness.

Joint Magnetic Calibration and Localization Based on Expectation Maximization for Tongue Tracking.

Tongue tracking, which helps researchers gain valuable insights into speech mechanism, has many applications in speech therapy and language learning. The wireless localization technique, which involves tracking a small magnetic tracer within the 3-D oral space, provides a low cost and convenient approach to capture tongue kinematics. In practice, this technique requires accurate calibration of three-axial magnetic sensors used in the tracking system. The data-driven calibration depends on the trajectories of magnetic tracer and the ambient noise, which may change across time and space. In this paper, we model the kinematics of tracer movement and the noisy magnetic measurements in a Bayesian framework, then present a joint calibration and localization (JCL) algorithm based on expectation maximization (EM), where the unscented Rauch-Tung-Striebel smoother is employed for tracer localization and the curvilinear search algorithm is applied for sensor calibration. Based on measurements conducted on our tongue tracking system with a small magnetic tracer (diameter: 6.05mm, thickness: 1.25mm, residual induction: 14800 G), the JCL algorithm achieves averaged root mean square error of 0.45mm for tracer position estimation and for tracer orientation estimation, which are significantly lower than those of the separate calibration and localization algorithms. These results show that JCL can help improve the localization accuracy of this system. A potentially high precision tongue tracking method is demonstrated.

Wireless Indoor Localization Systems and Techniques: Survey and Comparative Study

<p>The popularity, great influence and huge importance made wireless indoor localization has a unique touch, as well its wide successful on positioning and tracking systems for both human and assists also contributing to take the lead from outdoor systems in the scope of the recent research works. In this work, we will attempt to provide a survey of the existing indoor positioning solutions and attempt to classify different its techniques and systems. Five typical location predication approaches (triangulation, fingerprinting, proximity, vision analysis and trilateration) are considered here in order to analysis and provide the reader a review of the recent advances in wireless indoor localization techniques and systems to have a good understanding of state of the art technologies and motivate new research efforts in this promising direction. For these reasons, existing wireless localization position systems and location estimation schemes are reviewed. We also made a comparison among the related techniques and systems along with conclusions and future trends to identify some possible areas of enhancements. </p>

Seamless Outdoors-Indoors Localization Solutions on Smartphones

The demand for more sophisticated Location-Based Services (LBS) in terms of applications variety and accuracy is tripling every year since the emergence of the smartphone a few years ago. Equally, smartphone manufacturers are mounting several wireless communication and localization technologies, inertial sensors as well as powerful processing capability, to cater to such LBS applications. A hybrid of wireless technologies is needed to provide seamless localization solutions and to improve accuracy, to reduce time to fix, and to reduce power consumption. The review of localization techniques/technologies of this emerging field is therefore important. This article reviews the recent research-oriented and commercial localization solutions on smartphones. The focus of this article is on the implementation challenges associated with utilizing these positioning solutions on Android-based smartphones. Furthermore, the taxonomy of smartphone-location techniques is highlighted with a special focus on the detail of each technique and its hybridization. The article compares the indoor localization techniques based on accuracy, utilized wireless technology, overhead, and localization technique used. The pursuit of achieving ubiquitous localization outdoors and indoors for critical LBS applications such as security and safety shall dominate future research efforts.

A New Sparse Signal-Matched Measurement Matrix for Compressive Sensing in UWB Communication

Ultra wideband (UWB) technology is suitable for high data rate short range wireless communication, localization, and imaging techniques. However, UWB systems require high sampling rate and precise synchronization. In order to reduce the sampling rate, have precise synchronization, and for low power requirement, UWB systems are implemented using compressive or sub-Nyquist rate measured samples by exploiting the sparsity of the UWB signal. Compressive sensing (CS)-based UWB systems are being designed in two ways: 1) signal demodulation or detection is performed in the CS domain without full signal recovery at the front-end. Thus, demodulation or detection works on compressive measurements. However, system performance deteriorates in the CS domain as compared with full Nyquist rate sampling and 2) after, Nyquist rate signal is recovered using efficient algorithms at the front-end, the signal demodulation or detection is performed using the conventional receiver. Thus, one requires an efficient CS/sampling of signal measurement at the front-end for better system performance for both the cases stated earlier. In this paper, we propose a deterministic (partial) UWB waveform-matched measurement matrix. The proposed measurement matrix has a circulant structure and is sparse in nature. The proposed matrix is easy to implement in hardware and is operationally time efficient as needed in a practical system. The bit error rate performance of the corresponding UWB system and the operational time complexity with the proposed measurement matrix are better as compared with the existing measurement matrices in the CS domain for both the above receiver designs. The efficacy of the proposed measurement matrix is verified through extensive simulations in both the additive white Gaussian noise and multipath communication environments. In addition, we have also compared other desirable properties of the proposed measurement matrix with the existing measurement matrices.

Correction Terms of Ground and Water Reflection Surfaces for Perera’s Breakpoint Distance Model

Wireless localization techniques based on received signal strength require highly accurate prediction of path loss. This has led to revisit existing breakpoint distance models for path loss prediction. Dual-slope path loss models for line-of-sight propagation have been verified by many measurement campaigns. Breakpoint distance is the distance that separates the two different trends in path loss levels. Many prediction breakpoint distance models have already been proposed in literature. Perera’s breakpoint distance model has been verified with some of the published measurement results. In this work, we provide correction terms for ground and water reflection surfaces for both horizontal and vertical polarization. The correction terms generalize application of Perera’s model in terms of applicability ranges and RF propagation scenarios. The correction terms are based on analyzing and modeling of extracted patterns of prediction error.

DEVICE-FREE ELECTROMAGNETIC PASSIVE LOCALIZATION WITH FREQUENCY DIVERSITY

As an emerging wireless localization technique, the electromagnetic passive localization without the need of carrying any device, named device-free passive localization (DFPL) technique has drawn considerable research attention. The DFPL technique detects shadowed links in the monitored area and realizes localization with the received signal strength (RSS) measurements of these links. However, the current RSS-based DFPL techniques have two major challenges: one is that the RSS signal is particularly sensitive to noise, and the other is that it needs a sufficient number of nodes to provide enough RSS measurements of wireless links to guarantee good performance. To overcome these problems, in this paper we take advantage of compressive sensing (CS) theory to handle the spatial sparsity of the DFPL problem for reducing the number of nodes required by DFPL systems and exploit the frequency diversity technique to deal with the problem of the RSS sensitivity. Meanwhile, inspired by the fact that the target’s movement is continuous and that the target’s current location must be around the last location, we add prior information on the support region into the sparse reconstruction process for enhancing sparse reconstruction performance. The effectiveness and robustness of the proposed scheme are demonstrated by experimental results where the proposed algorithm yields substantial improvement for localization performance.

Placement Optimization of Reference Sensors for Indoor Tracking

Location-based applications and services are becoming widespread with the proliferation of smart mobile devices. They require position tracking which is still a challenge today in the indoor environment. Different wireless technologies and localization techniques can be used to obtain the position estimation. One possibility is deploying cheap ZigBee sensors as reference points and using triangulation to compute the position of the visiting node. This technique requires the reception of a wireless signal of at least three reference sensors with well-known positions everywhere within the covered area. In this paper, we propose OptiRef, our simulated annealing based method to find, in a given area, the optimal number and placement of the reference sensors to be used for indoor positioning. Our method has O(n) complexity and shows linear run-time behavior. We investigate the performance of OptiRef via simulations focusing on ZigBee technology, although our method is generic and can be used with any kind of wireless technologies. The resulted reference point setup(s) can be considered as a good starting point for real environment design.

Lightweight Robust Device-Free Localization in Wireless Networks

Due to its ability of realizing localization without the need of equipping the target with a wireless device, the device-free wireless localization technique has become a crucial technique for many security and military applications. However, there still lacks an efficient scheme which could achieve robust location estimation performance with low computational cost. To solve this problem, we propose a lightweight robust Bayesian grid approach (BGA) in this paper. The BGA utilizes not only the observation information of the shadowed links, but also the prior information involved in the previous estimations and the constraint information involved in the non-shadowed links, which ensure its robust performance. Meanwhile, the BGA can be carried out with a series of lightweight grid multiplication and addition operations, which eliminates the complex matrix inversion computation involved in the traditional algorithm. The experimental results demonstrate that BGA could achieve a mean tracking error of 0.155 m with a running time of only 1.5 ms.