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Performance of High-Accuracy Phase-Based Ranging in Multipath Environments
Abstract
Narrow-band radios, i.e. Bluetooth low-energy (BLE) or Zigbee, are widely available in smartphones or sensor networks. Distance estimation is a highly desired function, because it enables new kinds of services like asset tracking, commissioning of sensors and keyless entry systems. The current solution based on signal strength, does not offer enough accuracy to support these services. Phase-based ranging is much more accurate, but multipath fading is severely impacting the accuracy. In this work we propose three methods to improve the precision of the distance estimation using phase-based ranging. The first method is to calculate a one-way-channel-response from a two-way-channel response, the second method is to use a superresolution algorithm to improve the separation of line-of-sight from non-line-of-sight components. And the third method is to use antenna polarization and to combine the data in an efficient way. We will support these proposed improvements with measurements in a multipath-rich environment.
... Recent advancements in radio design have introduced the option of measuring the phase of received signals, motivating research into PBR. With the use of the MCPD method [43], also referred to as Active Reflector (AR) [22], the presented solutions achieved even greater accuracy in ranging and localization compared to other methods [22,43,44]. Nevertheless, there has been little consideration of how the PBR process affects the overall communication process. ...
... Recent advancements in radio design have introduced the option of measuring the phase of received signals, motivating research into PBR. With the use of the MCPD method [43], also referred to as Active Reflector (AR) [22], the presented solutions achieved even greater accuracy in ranging and localization compared to other methods [22,43,44]. Nevertheless, there has been little consideration of how the PBR process affects the overall communication process. ...
... After acquiring the phase response of a link, spectral analysis techniques can be employed to estimate the distance between the devices. Among the various algorithms available, such as slope-based [52], Fast Fourier Transform (FFT)-based [53], MUSIC-based [43], and ML-based [54], we use the FFT-based interpolated Complex-valued Distance Estimation (iCDE) algorithm [55] in this study due to its robustness to noise and ability to mitigate multipath effects. ...
Accurate localization of devices within Internet of Things (IoT) networks is driven by the emergence of novel applications that require context awareness to improve operational efficiency, resource management, automation, and safety in industry and smart cities. With the Integrated Localization and Communication (ILAC) functionality, IoT devices can simultaneously exchange data and determine their position in space, resulting in maximized resource utilization with reduced deployment and operational costs. Localization capability in challenging scenarios, including harsh environments with complex geometry and obstacles, can be provided with robust, reliable, and energy-efficient communication protocols able to combat impairments caused by interference and multipath, such as the IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) protocol. This paper presents an enhancement of the TSCH protocol that integrates localization functionality along with communication, improving the protocol’s operational capabilities and setting a baseline for monitoring, automation, and interaction within IoT setups in physical environments. A novel approach is proposed to incorporate a hybrid localization by integrating Direction of Arrival (DoA) estimation and Multi-Carrier Phase Difference (MCPD) ranging methods for providing DoA and distance estimates with each transmitted packet. With the proposed enhancement, a single node can determine the location of its neighboring nodes without significantly affecting the reliability of communication and the efficiency of the network. The feasibility and effectiveness of the proposed approach are validated in a real scenario in an office building using low-cost proprietary devices, and the software incorporating the solution is provided. The experimental evaluation results show that a node positioned in the center of the room successfully estimates both the DoA and the distance to each neighboring node. The proposed hybrid localization algorithm demonstrates an accuracy of a few tens of centimeters in a two-dimensional space.
... [9]. In later works, it was extended to the whole CFR including the magnitude response as well [10]. ...
... The main challenge in this transformation is the positive/negative sign ambiguity due to squareroot operation. This causes extra π phase transitions that ruin the estimated CSI quality and degrade the ranging accuracy.With the aid of a special type of PLL that stays locked to the same phase as carrier frequency changes, the authors of [10] proposed a phase processing technique to mitigate this sign ambiguity problem. They make the strong assumption that by using those special PLLs, phase mismatches from one frequency band to the next one can be calculated and compensated for. ...
... where H is an N SC × M matrix whose columns are the CFR data estimated across N SC sub-carriers from M time snapshots. To minimize the overhead of capturing multiple signal snapshots, the covariance matrix estimateR can be VOLUME 4, 2016 realized using a Hankel matrix that is constructed from one CFR snapshot and spectral smoothing is applied as explained in [10], [16]. To apply spectral smoothing, we replace (4) bŷ R =HH H , whereH is the Hankel matrix given bȳ ...
Indoor Localization is gaining increased importance due to numerous location-based services in healthcare, logistics, and security, to name few, that are expected to be provided by next-generation wireless networks. Such services are characterized by stringent accuracy requirements, short response time, and lower cost which makes the localization problem more challenging and deserving of attention. A key element of the localization process is distance estimation (also known as ranging). In this paper, we design and analyze an efficient decimeter-level two-way ranging scheme for ubiquitous WiFi networks in the 5 GHz frequency band whose accuracy approaches ideal one-way ranging with no phase mismatches. We investigate the idea of channel frequency response (CFR) stitching across non-contiguous WiFi channels and how two-way CFR measurements help in achieving the CFR coherency necessary for accurate ranging. In addition, we quantify the decrease in ranging accuracy of two-way compared to one-way ranging due to SNR degradation, Line-of-Sight (LoS) component shrinkage, and doubling the multipath delay spread. Furthermore, We design a novel scheme to bridge the performance gap between two-way ranging and ideal one-way ranging which operates in three main steps: square-root of the two-way CFR, followed by phase unwrapping, and finally deep fade detection and phase errors correction. Our proposed scheme achieves significant performance gains over two-way ranging with only a slight performance gap from ideal one-way ranging. Moreover, our proposed scheme enjoys robustness as it preserves the ranging accuracy gains in various WiFi communication scenarios when operating at different SNR levels, different multipath channel models, and different CFR bandwidths, as well as operating under system impairments such as Sample Timing Offset (STO). The accuracy gains achieved by the proposed schemes are demonstrated using both simulations and an in-house WiFi testbed. Finally, we quantify the added complexity of our proposed scheme and show it to be insignificant compared to that of the MUSIC super-resolution ranging steps which confirms the practical viability of our proposed scheme.
... In phase-based ranging, the channel is measured between two devices (known as initiator and reflector) on a uniform frequency grid over the bandwidth of interest [7]. The measurements can be processed using fast Fourier transform (FFT) [8] to obtain the CIR, however, the ranging accuracy is not great in multipath environment due to limited resolution of the FFT [9]. Instead of FFT, super-resolution algorithms such as Multiple signal classification (MUSIC) can be used to improve the accuracy of ranging in multipath environments [10]. ...
... Instead of FFT, super-resolution algorithms such as Multiple signal classification (MUSIC) can be used to improve the accuracy of ranging in multipath environments [10]. In [9] a novel ranging scheme based on single-snap shot MUSIC [11] is proposed which only employs a single IQ measurement over the frequency band. ...
... The critical assumption of applying the super-resolution algorithms such as MUSIC on phase-based ranging is that the IQ measurements at the initiator and reflector are taken on a uniform, frequency domain sampling grid [9]. However, this assumption will be violated in the case that there are missing or interfered tones. ...
The growth in the number of low-cost narrow band radios such as Bluetooth low energy (BLE) enabled applications such as asset tracking, human behavior monitoring, and keyless entry. The accurate range estimation is a must in such applications. Phase-based ranging has recently gained momentum due to its high accuracy in multipath environment compared to traditional schemes such as ranging based on received signal strength. The phase-based ranging requires tone exchange on multiple frequencies on a uniformly sampled frequency grid. Such tone exchange may not be possible due to some missing tones, e.g., reserved advertisement channels. Furthermore, the IQ values at a given tone may be distorted by interference. In this paper, we proposed two phase-based ranging schemes which deal with the missing/interfered tones. We compare the performance and complexity of the proposed schemes using simulations, complexity analysis, and two measurement setups. In particular, we show that for small number of missing/interfered tones, the proposed system based on employing a trained neural network (NN) performs very close to a reference ranging system where there is no missing/interference tones. Interestingly, this high performance is at the cost of negligible additional computational complexity and up to 60.5 Kbytes of additional required memory compared to the reference system, making it an attractive solution for ranging using hardware-limited radios such as BLE.
... The Multi-Carrier Phase Difference (MCPD) ranging method is able to overcome this bandwidth limitation by collecting channel frequency response (CFR) data over multiple frequencies and it mitigates the coarse clock synchronization problem by working with the two-way instead of one-way channel. Using Inphase/Quadrature (IQ)-samples in MCPD instead of just phase information of the channel measurements has shown robust and superior performance against multipath effects [6]. ...
... The Active Reflector (AR)-principle is a well-known method [7], [8] for ranging where two radio transceivers, called the Initiator and the Reflector, send and receive constant tone signals over multiple frequencies. Different range estimators, such as gradient-based [9], Fourier-transformbased [10] and super-resolution-based [6] approaches are then applied to extract the range information from multifrequency channel measurements. These range estimators model the channel impulse response (CIR) as a superposition of multipath components (MPCs) defined by their complex magnitudes, angles-of-arrival (AoAs), and ToFs. ...
... When formulated as a delay estimation problem, the ranging problem can be considered a classical array signal processing problem, where MUSIC [12], ESPIRIT [13], and Matrix Pencil [14] estimators are applicable. In [6], MUSIC is applied for ranging based on single-snapshot one-way channel measurements, where spatial smoothing is applied to form a Hankel matrix from the CFR [15]. However, this work assumed the singleantenna case and no enhancements were applied to improve the MUSIC estimator performance. ...
In this paper, we investigate enhanced super-resolution range estimators with decimeter-level accuracy for multi-antenna and multipath Bluetooth systems. To enhance the traditional MUSIC range estimator for a two-way frequency-hopping Bluetooth channel model, we apply forward-backward averaging and bandwidth extrapolation using Burg’s algorithm to improve ranging accuracy, which is limited by the used Bluetooth bandwidth and the quality of the estimated sample covariance matrix. For the multi-antenna case, we compare the Summed Antenna Processing and Individual Antenna Processing methods to process multiple-antenna Bluetooth channel measurements and enhance the range estimation accuracy compared to the single-antenna case. In addition, we investigate a sparsity-aware range estimator which exploits the sparsity of Bluetooth channel impulse response and achieves comparable ranging accuracy to the enhanced MUSIC estimator but at a much lower computational complexity. We apply the greedy Orthogonal Matching Pursuit algorithm to heuristically solve the sparsity-constrained optimization problem for Bluetooth ranging. Furthermore, we evaluate the computational complexity of our investigated Bluetooth range estimators with two complexity-reduction techniques to further reduce the complexity of MUSIC range estimator. Moreover, we analyze the Cramer-Rao Lower Bound (CRLB) on unbiased range estimation using the frequency-hopping Bluetooth channel model and derive a new insightful CRLB expression for a two-path channel model. Finally, we evaluate the Root-Mean-Square Error and Empirical Cumulative Distribution Function performance of our investigated range estimators both on simulated and real-world Bluetooth data that we collected in line-of-sight (LOS) and non-line-of-sight (NLOS) multipath scenarios. Our proposed enhancements on the range estimators improved the ranging accuracy by 58% for our collected Bluetooth data.
... To circumvent the aforementioned challenges , some contemporary attempts include weighted least square algorithm, provisioning a robust channel sounding algorithm, with relatively low complexity [12][13]. ...
The recent attempts for vehicle thefts using Relay Station or Man in Middle attacks have triggered the need for precise and secure distance measurement between the owner device and vehicle. Ultra-Wide Band offers high accuracy ranging; it is expensive and hence could not be deployed in vehicle access. Bluetooth® Low Energy Channel Sounding is excellent cost effective and mass scale alternative for enhanced ranging and vehicle access solution. The motivation for this research work lies in minimising power consumption thereby maximising battery life for the handheld owner device such as key fob or smartphone; whilst offering unparalleled security against Man in the Middle or Relay Station Attacks. The proposed work combines the conventional low power Received Signal Strength Indicator based approach with the Bluetooth® Low Energy Channel Sounding framework for accurate distance estimation. The coarse ranging offered by conventional Received Signal Strength Indicator approach is embedded into proximity based secure pairing as well as a pre-check before establishing connection between paired devices. The proposed work performs bidirectional measurement of values of Received Signal Strength Indicator, Phase Based Ranging and Round-Trip Time and cross-correlation of them to enhance accuracy using novel method. The development process utilized ANSYS HFSS 3D High Frequency Simulation for dual antenna design, enabling precise phase difference calibration. The implementation was achieved using a chipset with an ARM Cortex M33 core, hardware accelerators, and a BLE Channel Sounding stack integrated through a Software Development Kit. This work demonstrates a practical, scalable, and secure solution for BLE-based ranging in modern vehicle access systems. Besides security, proposed work provides opportunities for many more use cases such as distance based welcome feature where vehicle will recognise approaching owner and welcome with step by step turning ON of functions such as approach light, ambient light, unlocking the doors etc. The experimental results demonstrate that the proposed method achieves ranging accuracy comparable to existing LF and RF technologies.
... Seifallah et al. [10] combined the 2D-FFT algorithm with the Newton gradient algorithm for parameter estimation, improving spatial resolution and reducing computational complexity. Furthermore, researchers have proposed other new algorithms, such as spectrum refinement algorithms [14], ratio algorithms [15], phase difference algorithms [16], super-resolution algorithms [17], and sparse optimization algorithms [18]. These algorithms aim to overcome the limitations of existing algorithms in terms of low resolution, poor accuracy, weak robustness, and high computational complexity. ...
Frequency-modulated continuous-wave (FMCW) radar is used to extract range and velocity information from the beat signal. However, the traditional joint range–velocity estimation algorithms often experience significant performances degradation under low signal-to-noise ratio (SNR) conditions. To address this issue, this paper proposes a novel approach utilizing the complementary ensemble empirical mode decomposition (CEEMD) combined with singular value decomposition (SVD) to reconstruct the beat signal prior to applying the FFT-Root-MUSIC algorithm for joint range and velocity estimation. This results in a novel joint range–velocity estimation algorithm termed as the CEEMD-SVD-FFT-Root-MUSIC (CEEMD-SVD-FRM) algorithm. First, the beat signal contaminated with additive white Gaussian noise is decomposed using CEEMD, and an appropriate autocorrelation coefficient threshold is determined to select the highly correlated intrinsic mode functions (IMFs). Then, the SVD is applied to the selected highly correlated IMFs for denoising the beat signal. Subsequently, the denoised IMFs and signal residuals are combined to reconstruct the beat signal. Finally, the FFT-Root-MUSIC algorithm is applied to the reconstructed beat signal to estimate both the range and Doppler frequencies, which are then used to calculate the range and velocity estimates of the targets. The proposed CEEMD-SVD-FRM algorithm is validated though simulations and experiments, demonstrating significant improvement in the robustness and accuracy of range and velocity estimates for the FMCW radar due to the effective denoising of the reconstructed beat signal. Moreover, it substantially outperforms the traditional methods in low SNR environments.
... Later works proposed further MCPD improvements including the incorporation of super-resolution algorithms. The use of MUltiple SIgnal Classification (MUSIC) [20] and support vector regression (SVR) [24] for BLE ranging discriminates the line-of-sight (LoS) component from multi-path signals, improving accuracy in complex environments. A number of simulations, anechoic chamber measurements, and small-scale ranging experiments demonstrated high accuracy in MCPD range estimation (e.g. ...
Historically, the first indoor localization method using Bluetooth Low Energy (BLE) relied on received signal strength indicator (RSSI) measurements. This simple and cost-effective method suffers from limited accuracy due to its high susceptibility to environmental factors. The introduction of angle-of-arrival (AoA) functionality in BLE v5.1 significantly improved accuracy by enabling signal phase measurements. AoA exploits the phase difference of the received signal across spatially distributed antennas to estimate the direction of arrival and then pinpoint the location using triangulation. In contrast, multi-carrier phase difference (MCPD) leverages the phase difference of the signal at a single antenna but across different frequencies. The phase difference is used to estimate the distance, which allows the location to be determined using multilateration. MCPD is a recent addition to the BLE standard known as channel sounding, but unfortunately, real-world evaluations of this method are scarce. This paper addresses this gap by experimentally analyzing the performance of MCPD in an indoor environment. We conduct comprehensive experiments in a 100m
2
office space, comparing the accuracy of MCPD with RSSI and AoA-based approaches in the same area. The results show that the MCPD technique yields similar localization accuracy to the AoA method, with a mean localization error of 0.98m and 1.26 m, respectively, while avoiding the need for complex antenna arrays. Additionally, MCPD significantly outperforms RSSI-based methods, which have a mean localization error of 3.83 m.
... Phase-based ranging approaches utilize the phase information of Bluetooth signals to estimate distances [3]. While phase-based ranging methods have shown improved accuracy [4], they are still susceptible to limitations and challenges, including multi-path reflections and RF interferences, limited robustness to environmental changes, and high computational complexity and power consumption [5], [6]. ...
The Internet of Things (IoT) relies on accurate distance estimation between devices, crucial for localization in various applications. While RSSI-based ranging lacks precision and ToF narrow band systems perform poorly, phase-based ranging emerges as the preferred choice for Bluetooth Low Energy (BLE). Infineon's BLE prototype and its performance with a novel processing pipeline based on MVDR algorithm are presented in this paper. Our pipeline comprises sub-algorithms for pre-processing, scene identification, feature selection, feature engineering, and post-processing. Pre-processing includes zero distance calibration, low pass filtering, and time history averaging. Scene identification adapts parameters to environmental conditions. MVDR algorithms enable high-resolution feature transformation to project the residual Phase Correction Term to range domain. Post-processing includes tracker and data-dependent adaptation. Post-processing in conjunction with feature selection tracks the Line of Sight path, minimizing distance jitter. Our proposed pipeline achieves a 90 % peak error of
1.6 m without data-dependent adaptation and
1.2 m with data-dependent adaptation and tracking, outperforming existing methods in literature. This work demonstrates the potential of Infineon's BLE channel sounding for accurate range estimation in IoT applications.
... We consider a two-way system that utilizes the active reflector (AR) principle [31] to eliminate the effects of the unknown phase and timing offsets between the transmitter and receiver in one-way ranging. We apply the AR principle on the complex CFR of the propagation channel since the magnitude information also contains useful channel information [32] in addition to the phase information. Following the AR principle, two roles are defined, namely Initiator (I) and Reflector (R). ...
Localization schemes based on one-way Channel Frequency Response (CFR) are limited in terms of the available coherent bandwidth due to random timing and phase offsets. Two-way CFR-based localization schemes utilize higher coherent bandwidth but suffer from Angle-of-Arrival (AoA) estimation ambiguity. In this paper, we investigate efficient localization schemes for ubiquitous WiFi networks in the 5GHz unlicensed frequency band by utilizing both the one-way and two-way WiFi Channel State Information (CSI) to overcome the AoA estimation ambiguity arising from the two-way-channel-based localization algorithm. We use the two-way CFR to increase the total available coherent bandwidth through stitching and the one-way CFR to resolve the AoA estimation ambiguity. We also propose complexity reduction methods for our localization scheme. Our proposed reduced-complexity localization scheme achieves 58cm median localization accuracy for our collected WiFi data and simulation environment in multipath scenarios with a 98% reduction in computational complexity compared to the state-of-the-art.
... Here, the squareroot is used to avoid generation of additional unknown delays which are the result of inter-products between {φ k } K k=1 . The resulting measurements satisfy the model h D,i = ±MΘ i α, where the ambiguity can be resolved by tracking the phase difference between multiple bands [45]. ...
In wireless networks, an essential step for precise range-based localization is the high-resolution estimation of multipath channel delays. The resolution of traditional delay estimation algorithms is inversely proportional to the bandwidth of the training signals used for channel probing. Considering that typical training signals have limited bandwidth, delay estimation using these algorithms often leads to poor localization performance. To mitigate these constraints, we exploit the multiband and carrier frequency switching capabilities of wireless transceivers and propose to acquire channel state information (CSI) in multiple bands spread over a large frequency aperture. The data model of the acquired measurements has a multiple shift-invariance structure, and we use this property to develop a high-resolution delay estimation algorithm. We derive the Cram\'er-Rao Bound (CRB) for the data model and perform numerical simulations of the algorithm using system parameters of the emerging IEEE 802.11be standard. Simulations show that the algorithm is asymptotically efficient and converges to the CRB. To validate modeling assumptions, we test the algorithm using channel measurements acquired in real indoor scenarios. From these results, it is seen that delays (ranges) estimated from multiband CSI with a total bandwidth of 320 MHz show an average RMSE of less than 0.3 ns (10 cm) in 90% of the cases.
... Here, the squareroot is used to avoid generation of additional unknown delays which are the result of inter-products between {φ k } K k=1 . The resulting measurements satisfy the model h D,i = ±MΘ i α, where the ambiguity can be resolved by tracking the phase difference between multiple bands [47]. ...
In wireless networks, an essential step for precise range-based localization is the high-resolution estimation of multipath channel delays. The resolution of traditional delay estimation algorithms is inversely proportional to the bandwidth of the training signals used for channel probing. Considering that typical training signals have limited bandwidth, delay estimation using these algorithms often leads to poor localization performance. To mitigate these constraints, we exploit the multiband and carrier frequency switching capabilities of wireless transceivers and propose to acquire channel state information (CSI) in multiple bands spread over a large frequency aperture. The data model of the acquired measurements has a multiple shift-invariance structure, and we use this property to develop a high-resolution delay estimation algorithm. We derive the Cramér-Rao Bound (CRB) for the data model and perform numerical simulations of the algorithm using system parameters of the emerging IEEE 802.11be standard. Simulations show that the algorithm is asymptotically efficient and converges to the CRB. To validate modeling assumptions, we test the algorithm using channel measurements acquired in real indoor scenarios. From these results, it is seen that delays (ranges) estimated from multiband CSI with a total bandwidth of 320 MHz show an average RMSE of less than 0.3 ns (10 cm) in 90% of the cases.
Bluetooth ranging relies on two-way multi-carrier phase difference (MCPD) channel frequency response (CFR) measurements to mitigate time and phase offsets. However, the challenge of doubled multi-path components under the two-way MCPD approach, especially with a low number of snapshots, further degrades the performance of the commonly utilized MUSIC algorithm. In this paper, we investigate a reduced complexity signal-subspace-based approach for wireless ranging using Bluetooth Low Energy (BLE) in high multi-path environments. We propose a novel signal subspace decomposition (SSD) algorithm where we utilize the span of individual signal subspace eigenvectors for range estimation. We formulate the integration of the Fourier transform and randomized low rank approximation (RLA) into the SSD algorithm to reduce the computational complexity for better utilization in embedded devices. We then make use of the output features from estimated psuedo-spectrum of the individual eigenvectors, obtained from enhanced SSD algorithm, as an input to the long-short-term-memory (LSTM) recurrent neural network (RNN) to obtain a data-driven SSD-LSTM wireless range estimator for BLE. We evaluate our proposed approach using our real-world BLE data for single- and multiple-antenna scenarios. Our results show an improved performance of our proposed approach by more than 37%, while still enjoying lowest computational complexity than existing MUSIC and Support Vector Regression approaches for BLE ranging.
The Multi-Carrier Phase Difference (MCPD) wireless ranging technique, which uses the two-way channel frequency response (CFR) measurements, helps to mitigate time and phase offsets. However, these measurements are challenged by an increased number of multi-path components due to squaring the one-way CFR, a low number of snapshots, and model deficiencies in practical scenarios. Deep learning methods have produced remarkable improvements in different fields of research by learning complex systems and mitigating the system model imperfections using collected data. Although, data-driven deep learning models produce good ranging accuracy for the environment it is trained on, generalizing the model to new unseen environments is challenging. Furthermore, deep learning algorithms are expensive in terms of memory and computational resources on embedded devices. To mitigate these problems, we provide system level information to our low complexity deep learning model by using novel randomized low-rank (RLA) approximation of the signal subspace from the two-way CFR covariance matrix for Bluetooth ranging. The estimated signal subspace is utilized as an input to our Separable Convolution-Neural-Network (CNN) deep learning model, utilized for the first time for ranging. Our model greatly reduces the amount of parameters of the deep network, reducing memory requirements and being computationally more efficient than traditional approaches such as multiple signal classification (MUSIC). Overall, our approach outperforms MUSIC in terms of ranging accuracy by achieving overall RMSE of 0.5 m (range measurements from 1 m to 7 m) with 44% improvement over MUSIC in new unseen environments, and without the need for fine tuning or additional data collection.
Sensing is a critical aspect of wireless networks, used in applications such as localization, tracking, and human–computer interaction. However, prevalent commercial networks, such as WiFi, suffer from a lack of strict time synchronization between terminals and access points (APs). This leads to the introduction of phase errors in channel state information (CSI), hindering the effective utilization of sensing technology. To obtain accurate CSI with synchronization performance from orthogonal frequency division multiplexing (OFDM) signals, we propose a method that eliminates phase errors using the bidirectional channel. However, this approach only yields the square of the true channel, which includes fake multipath and disrupts the two-way channel. To recover the true channel, we introduce a channel recovery algorithm based on swarm optimization. Our proposed approach is validated through simulations and the use of software-defined radios (SDRs) in real-world measurements. The results show that accurate CSI can be acquired without phase errors, enabling the estimation of the true path after executing the CSI recovery algorithm. We demonstrate the effectiveness of our proposed method for perceiving accurate CSI in practical scenarios using a localization application.
The growth in the number of low-cost narrow band radios such as Bluetooth low energy (BLE) enabled applications such as asset tracking, human behavior monitoring, and keyless entry. The accurate range estimation is a must in such applications. Phase-based ranging has recently gained momentum due to its high accuracy in multipath environment compared to traditional schemes such as ranging based on received signal strength. The phase-based ranging requires tone exchange on multiple frequencies on a uniformly sampled frequency grid. Such tone exchange may not be possible due to some missing tones, e.g., reserved advertisement channels. Furthermore, the IQ values at a given tone may be distorted by interference. In this paper, we proposed two phase-based ranging schemes which deal with the missing/interfered tones. We compare the performance and complexity of the proposed schemes using simulations, complexity analysis and measurement setups. In particular, we show that for small number of missing/interfered tones, the proposed system based on employing a trained neural network (NN) performs very close to a reference ranging system where there is no missing/interference tones. Interestingly, this high performance is at the cost of negligible additional computational complexity and up to 60.5 Kbytes of additional required memory compared to the reference system, making it an attractive solution for ranging using hardware-limited radios such as BLE.
Ranging solutions for Internet of Things (IoT) localization applications seek to provide high accuracy with low cost of implementation. Among candidate IoT technologies that may fit this criterion, Bluetooth is a desirable choice as Bluetooth Low Energy (BLE) support is ubiquitous in modern smartphones, providing low implementation costs and low power consumption. Recent advancements in BLE ranging technology employ the Multi-Carrier Phase Difference technique which takes two-way channel frequency response (CFR) measurements. However, accurate ranging with these measurements is challenging due to many closely spaced multipath components from squaring the one-way CFR, a single or low number of snapshots, and model imperfections that arise in practical scenarios. To overcome these challenges, we propose a data-driven support vector regression (SVR) approach. Using real-world BLE measurements, our proposed SVR method demonstrates decimeter-level accuracy with single antenna devices, whereas Multiple Signal Classification (MUSIC), a popular model-based method, requires multiple antennas to obtain comparable performance. Moreover, we show robustness in different multipath environments including indoor, outdoor, and non-line-of-sight conditions, we determine generalization capabilities with training size, and we analytically establish the reduction in computational complexity compared to MUSIC.
Ranging and subsequent localization have become more and more critical in today’s factories and logistics. Tracking goods precisely enables just-in-time manufacturing processes. We present the InPhase system for ranging and localization applications. It employs narrowband 2.4 GHz IEEE 802.15.4 radio transceivers to acquire the radio channel’s phase response. In comparison, most other systems employ time-of-flight schemes with Ultra Wideband transceivers. Our software can be used with existing wireless sensor network hardware, providing ranging and localization for existing devices at no extra cost. The introduced Complex-valued Distance Estimation algorithm evaluates the phase response to compute the distance between two radio devices. We achieve high ranging accuracy and precision with a mean absolute error of 0.149 m and a standard deviation of 0.104 m. We show that our algorithm is resilient against noise and burst errors from the phase-data acquisition. Further, we present a localization algorithm based on a particle filter implementation. It achieves a mean absolute error of 0.95 m in a realistic 3D live tracking scenario.
European Technology Platform NetWorld2020; Strategic Research and Innovation Agenda 2021-27
Relay attacks pose a serious security threat to wireless systems, such as, contactless payment systems, keyless entry systems, or smart access control systems. Distance bounding protocols, which allow an entity to not only authenticate another entity but also determine whether it is physically close by, effectively mitigate relay attacks. However, secure implementation of distance bounding protocols, especially of the time critical challenge-response phase, has been a challenging task. In this paper, we design and implement a secure and accurate distance bounding protocol based on Narrow-Band signals, such as Bluetooth Low Energy (BLE), to particularly mitigate relay attacks. Narrow-Band ranging, specifically, phase-based ranging, enables accurate distance measurement, but it is vulnerable to phase rollover attacks. In our solution, we mitigate phase rollover attacks by also measuring time-of-flight (ToF) to detect the delay introduced by such attacks. Therefore, our protocol effectively combines the best of both worlds: phase-based ranging for accuracy and time-of-flight (ToF) measurement for security. To demonstrate the feasibility and practicality of our solution, we prototype it on NXP KW36 BLE chips and evaluate its performance and relay attack resistance. The obtained precision and accuracy of the presented ranging solution are 2.5 cm and 30 cm, respectively, in wireless measurements.
Nowadays, indoor positioning and asset tracking have become popular and essential for many applications and use-cases. As Bluetooth Low Energy (BLE) is widely deployed in smart tags, smart-phones, and smart-devices, adding functionality to support localization and asset tracking is crucial. In order to locate a device, either the angle or range to the device needs to be calculated. In this paper, we focus on a phase-based solution to calculate the range between devices. We introduce a multi-carrier phase-based ranging solution compatible with the BLE standard that utilizes BLE channel hopping to exchange tones in the entire 2.4 GHz frequency band to mitigate the multi-path fading problem. We recognize that in the BLE standard, slow channel-hopping, long packet size and frame spacing between two consecutive communications (named T IFS) affect ranging error/accuracy, in the case of crystal offset and phase-noise. Therefore, we introduce a new mathematical model to analyze the impact of the BLE link layer protocol on the ranging error. Finally, we evaluate the accuracy of our proposed solution through modelling, by considering the results of real experiments and by validating the correctness of our mathematical model for the ranging error.
We consider the problem of performing ranging measurements between a source
and multiple receivers efficiently and accurately, as required by
distance-based wireless localization systems. To this end, a new multipoint
ranging algorithm is proposed, which is obtained by adapting superresolution
techniques to the ranging problem, using for the sake of illustration the
specific cases of ToA and PDoA, unified under the same mathematical framework.
The algorithm handles multipoint ranging in an efficient manner by employing an
orthogonalized non-uniform sampling scheme optimised via Golomb rulers. Since
the approach requires the design of mutually orthogonal sets of Golomb rulers
with equivalent properties -- a problem that founds no solution in current
literature -- a new genetic algorithm to accomplish this task is presented,
which is also found to outperform the best known alternative when used to
generate a single ruler. Finally, a CRLB analysis of the overall optimised
multipoint ranging solution is performed, which together with a comparison
against simulation results validates the proposed techniques.
Position information of nodes within wireless sensor networks (WSNs) is often a requirement in order to make use of the data recorded by the sensors themselves. On deployment the nodes normally have no prior knowledge of their position and thus a locationing mechanism is required to determine their positions. In this paper, we describe a method to determine the point-to-point range between sensor nodes as part of the locationing process. A two-way time-of-flight (TOF) ranging scheme is presented using narrow-band RF. The frequency difference between the transceivers involved with the point-to-point measurement is used to obtain a sub-clock TOF phase offset measurement in order to achieve high resolution TOF measurements. The ranging algorithm has been developed and prototyped on a TI CC2430 development kit with no additional hardware being required. Performance results have been obtained for the line-of-sight (LOS), non-line-of-sight (NLOS) and indoor conditions. Accuracy is typically better than 7.0 m RMS for the LOS condition over 250.0 m and 15.8 m RMS for the NLOS condition over 120.0 m using a 100 sample average. Indoor accuracy is measured to 1.7 m RMS using a 1000 sample average over 8.0 m. Ranging error is linear and does not increase with the increased transmitter–receiver distance. Our TOA ranging scheme demonstrates a novel system where resolution and accuracy are time dependent in comparison with alternative frequency-dependent methods using narrow-band RF.
We present an analysis of a "spatial smoothing" preprocessing scheme, recently suggested by Evans et al., to circumvent problems encountered in direction-of-arrival estimation of fully correlated signals. Simulation results that illustrate the performance of this scheme in conjunction with the eigenstructure technique are described.
We consider the problem of performing ranging measurements between a source and multiple receivers efficiently and accurately, as required by distance-based wireless localiza-tion systems. To this end, a new multipoint ranging algorithm is proposed, which is obtained by adapting superresolution techniques to the ranging problem, using for the sake of illustration the specific cases of time of arrival (ToA) and phase-difference of arrival (PDoA), unified under the same mathematical framework. The resulting nonparametric algorithm handles multipoint ranging in an efficient manner by employing an orthogonalized nonuniform sampling scheme optimized via Golomb rulers. Since the approach requires the design of mutually orthogonal sets of Golomb rulers with equivalent properties—a problem that founds no solution in current literature—a new genetic algorithm to accomplish this task is presented, which is also found to outper-form the best known alternative when used to generate a single ruler. Finally, a Cramér-Rao lower bound (CRLB) analysis of the overall optimized multipoint ranging solution is performed, which together with a comparison against simulation results validates the proposed techniques.
Indoor localization is important for logistics, industrial applications and for several consumer applications. In the area of logistics, e.g. warehouses, localization accuracy within a few meters is desired. Available radio based systems within that accuracy are neither cost effective nor easy to deploy. Distance estimation together with triangulation are one of the standard solutions for localization. In this work, we propose phase measurements between two wireless sensor nodes for distance estimation. We introduce a mathematical model to estimate distances from phase measurements with multiple frequencies and provide a systematic analysis of possible sources of errors. Additionally, we derive requirements, e.g. resolution for a phase measurement unit to achieve a given accuracy. We present measurements for evaluation to confirm our theoretical results. Our implementation comprises a low cost IEEE 802.15.4 hardware with a built-in phase measurement unit. We implement the developed algorithm for distance estimation in our wireless sensor networks and use two wireless sensor nodes to perform a phase measurement. The contributions of the paper comprise a new model for phase measurements to estimate distances and a preliminary evaluation with our hardware.
This paper studies the problem of line spectral estimation in the continuum of a bounded interval with one snapshot of array measurement. The single-snapshot measurement data is turned into a Hankel data matrix which admits the Vandermonde decomposition and is suitable for the MUSIC algorithm. The MUSIC algorithm amounts to finding the null space (the noise space) of the Hankel matrix, forming the noise-space correlation function and identifying the s smallest local minima of the noise-space correlation as the frequency set. In the noise-free case exact reconstruction is guaranteed for any arbitrary set of frequencies as long as the number of measurement data is at least twice the number of distinct frequencies to be recovered. In the presence of noise the stability analysis shows that the perturbation of the noise-space correlation is proportional to the spectral norm of the noise matrix as long as the latter is smaller than the smallest (nonzero) singular value of the noiseless Hankel data matrix. Under the assumption that the true frequencies are separated by at least twice the Rayleigh Length (RL), the stability of the noise-space correlation is proved by means of novel discrete Ingham inequalities which provide bounds on the largest and smallest nonzero singular values of the noiseless Hankel data matrix. The numerical performance of MUSIC is tested in comparison with other algorithms such as BLO-OMP and SDP (TV-min). While BLO-OMP is the stablest algorithm for frequencies separated above 4 RL, MUSIC becomes the best performing one for frequencies separated be-tween 2 RL and 3 RL. Also, MUSIC is more efficient than other methods. MUSIC truly shines when the frequency separation drops to 1 RL or below when all other methods fail. Indeed, the resolution length of MUSIC decreases to zero as noise decreases to zero as a power law with an exponent much smaller than an upper bound established by Donoho.
Phase measurement based time-of-flight ranging techniques are a valid approach for radio-frequency distance measurements using a small relative bandwidth. Step-frequency radar enhances this approach for application on multiple frequencies. In case of presence of narrowband interference, distance calculations are possibly affected depending on the number of frequencies measured and the type of the antennae used. The Inverse Fast Fourier Transform (IFFT) tends to be robust against interferences compared to calculation of a weighted mean. Resolution of IFFT and therefore accuracy of the ranging result depends on the number of measurement taps available. In this paper, the Compressive Sampling (CS) approach is investigated to increase the resolution of IFFT. It is shown that the CS approach is applicable for SFR in such scenario. Field tests have been performed for verification using a commercial ranging system from ZigPos company with different prototype antennae to minimize multipath influence in indoor environment.
Efficient slope sampling ranging and trilateration techniques for wireless localization
- O Oshiga
- A Ghods
- S Severi
- G Abreu
Method for distance determination
- J Romme
Efficient slope sampling ranging and trilateration techniques for wireless localization
- oshiga