A hybrid method for time-of-arrival based ranging.

Abstract

To provide a method for hybrid ranging in the field of time-of-arrival-based ranging.SOLUTION: A method includes: receiving from a transmitter a ranging signal comprising a set of multiplexed sub-signals, so as to calculate a time-based time-of-arrival estimate based on a series of time-domain samples of the ranging signal; calculating a time-based uncertainty of the time-based time-of-arrival; for each sub-signal pair in a subset of multiplexed sub-signals of the set of multiplexed sub-signals, extracting a phase difference of the sub-signal pair; calculating a phase-based time-of-arrival estimate based on the phase difference of each sub-signal pair in the subset of multiplexed sub-signals; calculating a phase-based uncertainty of the phase-based time-of-arrival estimate; and calculating a hybrid time-of-arrival estimate as a weighted combination of the time-based time-of-arrival estimate and the phase-based time-of-arrival estimate based on the uncertainties.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates generally to the field of time-of-arrival based ranging, and more particularly to a novel and useful method for hybrid ranging in the field of time-of-arrival based ranging.

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 63 / 105,822, filed October 26, 2020, which is incorporated by reference in its entirety.

[0003] This application is related to U.S. Patent Application No. 17 / 379,885, filed July 19, 2021, U.S. Patent Application No. 17 / 379,873, filed July 19, 2021, U.S. Patent Application No. 17 / 105,458, filed November 25, 2020, and U.S. Patent Application No. 16 / 846,030, filed April 10, 2020, each of which is incorporated by reference in its entirety. [Brief description of the drawings]

[0004] [Figure 1] FIG. 1 is a flow chart of the method. [Diagram 2] FIG. 2 is a flow chart of one embodiment of the present method. [Diagram 3] Figure 3A is a flow chart of one embodiment of the present method, Figure 3B is a flow chart of one embodiment of the present method, and Figure 3C is a flow chart of one embodiment of the present method. [Figure 4] FIG. 4 is a flow chart of one embodiment of the present method. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0005] The following description of the embodiments of the present invention is not intended to limit the present invention to those embodiments, but is intended to enable those skilled in the art to make and use the present invention. The aspects, configurations, implementations, embodiments, and examples described herein are optional and are not limited to only the aspects, configurations, implementations, embodiments, and examples they describe. The invention described herein can include any and all permutations of those aspects, configurations, implementations, embodiments, and examples.

[0006] 1. Method As shown in FIG. 1, a method for estimating a time of arrival of a ranging signal transmitted from a transmitter to a receiver includes, in block S110, accessing a ranging signal including a set of multiplexed sub-signals, each multiplexed sub-signal characterized by a frequency within a set of frequencies; in block S112, capturing a series of time domain samples indicative of the ranging signal; in block S120, calculating a time-based time of arrival estimate based on the series of time domain samples; in block S122, calculating a time-based uncertainty based on a sampling frequency of the receiver; and in block S130, calculating a maximum frequency difference in the set of frequencies characterized by a total phase accumulation of less than 2π radians during the time-based time of arrival estimate. The method also includes, in block S132, extracting a phase difference of the sub-signal pair for each sub-signal pair in the set of sub-signal pairs characterized by a frequency difference less than the maximum frequency difference, and, in block S134, calculating a sub-signal pair arrival time estimate in the set of sub-signal arrival time estimates based on the phase difference of the sub-signal pair.The method further includes calculating a phase-based time of arrival estimate based on a representative of the set of sub-signal pair time of arrival estimates in block S136, calculating a phase-based uncertainty based on a variability measure of the set of sub-signal pair time of arrival estimates in block S138, performing a multiple signal classification algorithm to detect a set of multipath components of the ranging signal in block S140, calculating a phase-magnitude-based time of arrival estimate based on the set of multipath components of the ranging signal in block S142, calculating a K-factor based on a power ratio of the earliest arriving component to all other components in the set of multipath components in block S144, and calculating a hybrid time of arrival estimate as a weighted combination of the time-based time of arrival estimate, the phase-based time of arrival estimate, and the phase-magnitude-based time of arrival estimate based on the time-based uncertainty, the phase-based uncertainty, and the K-factor in block S150.

[0007] One aspect of method S100 includes receiving at a receiver a ranging signal from a transmitter, the ranging signal including a set of multiplexed sub-signals, each multiplexed sub-signal characterized by a frequency within a set of frequencies, capturing a series of time-domain samples indicative of the ranging signal, calculating a time-based time-of-arrival estimate based on the series of time-domain samples, calculating a time-based uncertainty of the time-based time-of-arrival estimate based on a sampling frequency of the receiver, and capturing a series of time-domain samples indicative of the ranging signal, the series of time-domain samples indicative of the ranging signal, the time-domain uncertainty of the time-based time-of-arrival estimate based on a sampling frequency of the receiver, and calculating a time-based uncertainty of the time-based time-of-arrival estimate based on a sampling frequency of the receiver, the time-domain uncertainty of ... For each subsignal pair in the subset of multiplexed subsignals, extracting a phase difference of the subsignal pair, in block S136, calculating a phase-based time of arrival estimate based on the phase difference of each subsignal pair in the subset of multiplexed subsignals, in block S138, calculating a phase-based uncertainty of the phase-based time of arrival estimate based on a degree of variation based on the phase difference of each subsignal pair in the subset of multiplexed subsignals, and calculating a hybrid time of arrival estimate as a weighted combination of the time-based and phase-based time of arrival estimates based on the time-based and phase-based uncertainties, in block S150.

[0008] Another aspect of method S100 includes receiving at a receiver a ranging signal from a transmitter, where the ranging signal includes a set of multiplexed sub-signals, each multiplexed sub-signal characterized by a frequency within a set of frequencies, capturing a series of time domain samples indicative of the ranging signal, calculating a time-based time of arrival estimate based on the series of time domain samples, calculating a time-based uncertainty based on a sampling frequency of the receiver, and calculating a time-based time of arrival estimate based on a sampling frequency of the receiver, in block S140. The method includes performing a heavy signal classification algorithm to detect a set of multipath components of the ranging signal, calculating a phase-amplitude based time of arrival estimate based on the set of multipath components of the ranging signal in block S142, calculating a K factor based on a power ratio of the earliest arriving component to all other components in the set of multipath components in block S144, and calculating a hybrid time of arrival estimate as a weighted combination of the time-based time of arrival estimate and the phase-amplitude based time of arrival estimate based on the time-based uncertainty and the K factor in block S150.

[0009] Yet another aspect of the method S100 includes, in block S110, accessing a ranging signal transmitted from a transmitter and received by a receiver, the ranging signal including a set of multiplexed subsignals, each multiplexed subsignal characterized by a frequency within a set of frequencies; in block S132, for each subsignal pair in a subset of multiplexed subsignals of the set of multiplexed subsignals, extracting a phase difference of the subsignal pair; in block S136, calculating a phase-based time of arrival estimate based on the phase difference of each subsignal pair in the subset of multiplexed subsignals; in block S138, calculating a phase-based time of arrival estimate based on the degree of dispersion based on the phase difference of each subsignal pair in the subset of multiplexed subsignals. In block S140, the method includes calculating a phase-based uncertainty of a time of arrival estimate of the ranging signal from the base station; performing a multiple signal classification algorithm to detect a set of multipath components of the ranging signal in block S142; calculating a phase-magnitude based time of arrival estimate based on the set of multipath components of the ranging signal in block S142; calculating a K factor based on a power ratio of an earliest arriving component to all other components in the set of multipath components in block S144; and calculating a hybrid time of arrival estimate as a weighted combination of the phase-based time of arrival estimate and the phase-magnitude based time of arrival estimate based on the phase-based uncertainty and the K factor in block S150.

[0010] 2.Applications As shown in FIG. 1, the method S100 is performed by a system including a receiver (or transceiver) and / or a remote server to estimate the time of arrival (hereinafter "TOA") of a ranging signal transmitted by a transmitter (or transmitting device) for accurate ranging and location purposes. More specifically, the system can receive a ranging signal including a set of multiplexed sub-signals each characterized by a unique frequency (e.g., subcarrier frequency or carrier frequency), perform a time-based TOA estimation process, a phase-based TOA estimation process, and a phase-amplitude-based TOA estimation process based on the ranging signal, and combine the results of the processes based on parameters obtained from the processes that characterize the propagation channel between the receiver and the transmitter. Thus, the system can mitigate the inaccuracies inherent in any one of the processes and consistently calculate accurate TOA estimates regardless of local multipath channels, hardware anomalies (e.g., frequency-dependent amplitude), or software anomalies at the receiver or transmitter.

[0011] Upon receiving the ranging signal from the transmitter, the system can capture (i.e., measure and / or record) a series of time domain samples indicative of the ranging signal and can calculate a time-based TOA estimate of the ranging signal based on the samples, such as via a matched filter. However, the time-based TOA estimate is limited in accuracy by the sampling frequency of the system and the effect of multipath fading on the ranging signal. Thus, the system can supplement the time-based estimate with additional estimates based on other characteristics of the ranging signal to improve the time-based estimate and arrive at a hybrid TOA estimate with higher accuracy.

[0012] In addition to capturing a series of time domain samples indicative of the ranging signal, the system can further extract the phase (e.g., arrival phase at the receiver) of each sub-signal in the set of sub-signals that make up the ranging signal. The system can then calculate a phase-based TOA estimate based on the extracted phase of each sub-signal of the ranging signal. Although the system can calculate a phase-based TOA estimate that is robust to hardware-dependent amplitude variations, the phase-based TOA can still be affected by strong multipath, which can manifest as unpredictable frequency-dependent phase delays in the set of sub-signal phases. For this reason, the system can use a third TOA estimate as a cross-reference between the time-based TOA estimate and the phase-based TOA estimate.

[0013] In addition to extracting the phase of each sub-signal, the system can utilize both the phase of each sub-signal and the amplitude of each sub-signal to calculate a phase-amplitude based TOA estimate. For example, the system can execute a multiple signal classification algorithm (hereinafter "MUSIC algorithm"), which allows the system to identify the TOA associated with each multipath component of the ranging signal and the power associated with each multipath component by calculating the eigenvectors of the autocorrelation matrix of the ranging signal. The system can then calculate the phase-amplitude TOA by selecting the earliest identified TOA as the line-of-sight (hereinafter "LOS") TOA. Although the system can calculate a phase-amplitude TOA estimate that can remain accurate in a strong multipath environment, the phase-amplitude TOA estimate may be sensitive to hardware imperfections, including nonlinearities or uncalibrated non-repeatability in the transmit / receive chain that violate the assumptions of the MUSIC algorithm, and / or low peak-to-average power ratio of the ranging signal. However, in addition to extracting a third estimate for the TOA of the ranging signal, the system may utilize the MUSIC algorithm and / or other phase-amplitude based TOA estimation methods (e.g., conventional beamforming algorithm, Capon's beamforming algorithm, rotational invariance techniques or ESPRIT signal parameter estimation) to characterize the multipath environment of the ranging signal (e.g., by calculating a multipath index of the ranging signal based on the identified multipath components of the ranging signal).

[0014] The system can then combine the time-based, phase-based and phase-amplitude-based TOA estimates based on the characterization of the multipath environment and the time-based and phase-based uncertainties of the time-based and phase-based TOA estimates, respectively, to compute a hybrid TOA estimate that provides improved accuracy in the face of multipath and hardware nonlinearities without the need to increase the receiver sampling rate or the ranging signal bandwidth.

[0015] 3. Network Generally, method S100 is performed in the context of a wired or wireless communication network (such as a mesh network described in U.S. Patent Application No. 17 / 105,458). A mesh network may include a set of transceivers (e.g., custom radio tag readers, gNodeBs in 5G, eNodeBs in 4G-LTE) configured to receive ranging signals from other transceivers or transmitters (e.g., passive or active tags, 5G or 4G-LTE user equipment) participating in the mesh network and / or perform the blocks of method S100. Upon receiving the ranging signals, the transceivers may perform the blocks of method S100 locally, as shown in FIG. 3A, or alternatively, may transmit raw waveform data or any intermediate output of method S100 to a remote server or primary transceiver for further processing according to the blocks of method S100, as shown in FIG. 3B. To this end, the system may execute the blocks of method S100 at the transceivers within the mesh network, or at a central computing device (i.e., a remote server or a central transceiver) that can track other parameters that characterize the mesh network (e.g., the relative position and time bias of each transceiver within the mesh network).

[0016] In one aspect, transceivers in a mesh network locate themselves by performing the blocks of method S100 on ranging signals transmitted from transceiver to transceiver in the mesh network. More specifically, by performing method S100 to obtain accurate time-of-flight measurements between transceivers pair by pair, the system can calculate distances for pair-by-pair ranging of transceiver pairs and estimate the relative location of each transceiver in the mesh network, assuming sufficient connectivity between transceivers in the mesh network. Additionally or alternatively, the system can perform the blocks of method S100 on ranging signals transmitted from transmitters (e.g., passive or active tags, 5G or 4G-LTE, user equipment) to transceivers as part of an uplink location protocol. Thus, by performing multiple instances of method S100 on ranging signals transmitted from a transmitter and received by multiple transceivers, the system can calculate accurate TOFs of ranging signals at each transceiver in the mesh network and perform TOA or TDOA multilateration to estimate the location of the transmitter.

[0017] In another embodiment shown in Figure 3C, the blocks of method S100 are performed by a receiver of multiple ranging signals in a downlink position location protocol. In this embodiment, a single receiver (e.g., 5G or 4G-LTE) performs an instance of method S100 for each ranging signal received by the receiver. The receiver can then estimate its location given the location of the transmitter of each received ranging signal.

[0018] Generally, method S100 is described below with respect to ranging signals being transmitted by a transmitter and received by a transceiver. However, the receivers and transmitters described below may be deployed in any of the above-mentioned contexts. Additionally, the blocks of method S100 may be performed by a receiver of the ranging signals, a remote computing device, or a combination of both.

[0019] 3.1 Receiver Generally, the system may include a receiver configured to receive ranging signals transmitted from a transmitter. More specifically, the receiver may include RF receiving components such as antennas, amplifiers, mixers, filters, analog-to-digital converters (hereinafter "ADCs") and / or digital signal processors (hereinafter "DSPs") arranged in a superheterodyne, heterodyne or homodyne architecture. In one aspect, the system may include a receiver such as a wireless base station or wireless node in a network (e.g., a 4G or 5G network). In this aspect, the system may time synchronize the receiver with other receivers in the network to improve localization of transmitters within range of multiple receivers.

[0020] In one aspect, such as in the context of a downlink position location protocol or a pair-wise position location protocol, a receiver is configured to perform the blocks of method S100 to generate a hybrid TOA estimate locally. Alternatively, the receiver is configured to receive a ranging signal and transmit waveform data indicative of the ranging signal to a remote computing device that executes method S100.

[0021] 3.2 Transmitter Generally, the system can estimate the TOA of a ranging signal transmitted from a transmitter to a receiver in order to calculate the time of flight (hereinafter "TOF") of the ranging signal and thus the distance between the transmitter and the receiver. More specifically, the transceiver may be any device (including another receiver in the network) capable of transmitting a ranging signal. In one aspect, the transmitter may be a user device in the network, such as a smartphone, a smartwatch, a tablet computer, a laptop computer, or any other network-enabled computing device.

[0022] 3.3 Remote Computing Devices In one aspect, a remote computing device in communication with the receiver is configured to perform the blocks of method S100 to generate the hybrid TOA estimate. In this aspect, the remote computing device may be a cloud server connected to one or more receivers configured to receive ranging signals from the transmitters. In this aspect, the remote computing device may perform any subset of the blocks of method S100 that are not performed by the receiver.

[0023] 4. Ranging signal In general, the system performs the blocks of method S100 upon receipt of a ranging signal by a receiver to estimate the TOA of the ranging signal. For the system to estimate the TOA of the ranging signal, the system can receive the ranging signal in block S110, which includes a set of multiplexed sub-signals, each sub-signal being characterized by a unique carrier frequency within a set of carrier frequencies. Thus, by receiving a multiplexed signal having multiple frequency components, the system can perform phase-only and phase-magnitude processes to estimate the TOA of the ranging signal.

[0024] Further, to calculate the time-based TOA estimate, the system can capture a series of time-domain digital samples indicative of the ranging signal in block S112. By capturing a series of samples indicative of the ranging signal, the system can compute a more accurate time-based TOA estimate by implementing matched filter techniques or other super-resolution techniques that are more effective as the signal length and complexity increases.

[0025] 4.1 Carrier frequency hopping method In one aspect, the system can receive a ranging signal that includes a set of time division multiplexed sub-signals, each characterized by a unique carrier frequency (i.e., a carrier frequency hopping scheme). More specifically, in this aspect, the system can receive a frequency hopping spread spectrum signal that includes a set of multiplexed sub-signals, each multiplexed sub-signal characterized by a frequency within the set of frequencies.

[0026] In this aspect, the transmitter can transmit time division multiplexed sub-signals in successive slots of a time division multiplexing scheme to provide phase information across multiple frequencies. The system can receive each successive sub-signal during a corresponding slot and extract timing and phase information therefrom. The transmitter can vary the carrier frequency of each successive sub-signal such that the frequencies characterizing the set of sub-signals span the available bandwidth in the channel. The system can calculate the TOA of each sub-signal and the phase of each sub-signal. The system can then calculate the precise TOF between the transmitter and receiver based on the TOAs and the phases extracted from the ranging signals.

[0027] The manner in which the present system is able to extract accurate phase and TOA from time division multiplexed signals is further described in US patent application Ser. No. 16 / 846,030.

[0028] 4.2 Subcarrier signaling In another aspect, the system can receive a ranging signal that includes a set of frequency division multiplexed (e.g., orthogonal frequency division multiplexed) sub-signals, each characterized by a unique subcarrier frequency. More specifically, the system can receive an orthogonal frequency division multiplexed signal that includes a set of multiplexed sub-signals, each multiplexed sub-signal characterized by a frequency within the set of frequencies.

[0029] In this aspect, the system can receive a single signal including multiple subcarrier signals and extract a unique phase corresponding to each subcarrier frequency of the subcarrier signals. The system can then extract phase information from each subcarrier according to the method described in U.S. Provisional Application No. 63 / 053,509 and calculate a single time of arrival of the ranging signal.

[0030] 5. Time-based arrival time estimation Typically, upon receiving a ranging signal from a transmitter, the system generates a time-based TOA estimate τ in block S120. t , in block S122, a time-based uncertainty δ is calculated based on the sampling frequency of the receiver. t More specifically, the system can directly calculate a TOA estimate based on time domain samples captured at the receiver. Once the time-based TOA estimate is calculated, the system can leverage the precise time synchronization (or precise characterization of the time bias) between the receiver and the transmitter to estimate the TOF of the ranging signal and thus the distance between the receiver and the transmitter.

[0031] The system may utilize time domain sampling, oversampling and / or matched filter techniques to detect the time of arrival (or arrival time in the case of a time division multiplexed signal) of the ranging signal.

[0032] In one aspect, the system can utilize a matched filter to identify the TOA based on a set of discrete time-domain samples. More specifically, the system can access a template signal corresponding to the ranging signal, correlate the template signal with a set of time-domain samples to identify a correlation peak, and calculate a time-based time-of-arrival estimate based on the correlation peak.

[0033] Alternatively, the system may perform a super-resolution method as described in U.S. Patent Application No. 16 / 846,030 to increase the accuracy and / or precision of the calculated TOA of the ranging signal. Upon calculating the TOA of the ranging signal, the system may access the transmission time of the ranging signal (or each time division sub-signal of the ranging signal) to calculate the TOF or set of TOFs of the ranging signal (assuming sufficient clock synchronization between the transmitter and receiver, e.g., nanosecond level clock synchronization). For example, in an application where the ranging signal is a frequency division multiplexed signal, the system may subtract the transmission time of the ranging signal from the calculated TOA of the ranging signal to calculate a TOF estimate of the ranging signal. Alternatively, in an application where the ranging signal is a time division multiplexed signal, the system may average the difference between the corresponding transmission times and the TOAs of the set of sub-signals that make up the ranging signal.

[0034] 5.1 Uncertainty Estimation In addition to calculating a time-based TOA estimate, the system may also calculate a time-based uncertainty (or confidence interval) of the TOA estimate. More specifically, the system may calculate the time-based uncertainty based on a receiver sampling frequency in block S122. Thus, by calculating the time-based uncertainty, the system may appropriately weight the time-based TOA estimate relative to the phase-based TOA estimate and the phase-amplitude-based TOA estimate.

[0035] In one aspect, the system can calculate a time-based uncertainty based on a peak width of a matched filter output for the ranging signal. More specifically, the system can run a matched filter on a set of time domain samples of the ranging signal to calculate a matched filter output, calculate a peak width of the matched filter output, and calculate an uncertainty of the time-based TOA estimate based on the peak width of the matched filter output.

[0036] In another aspect, in applications where the system does not implement super-resolution techniques, the system can set the time-based uncertainty to be equal to the receiver sampling period.

[0037] 6. Extraction of phase difference In general, the system can calculate a phase-based TOA estimate of the ranging signal at the receiver based on the phase difference of each sub-signal in the set of sub-signals. More specifically, via a single phase measurement (e.g., the equation τ φ =φ / 2πf), for the received multicarrier at receiving device m, the TOA, τ φ Instead of calculating 1 and n 2 Based on any two phase differences from τ, φ can be calculated. TIFF0007667897000001.tif14170

[0038] In this manner, the system may select τ based on the number of sub-signals in the set of sub-signals included in the ranging signal. φ Multiple estimates of can be calculated, which can be aggregated into a single phase-based TOA estimate, as described below.

[0039] 7. Pairwise gradient arrival time estimates In general, once the system extracts the phase difference Δφ and frequency difference Δf of each sub-signal pair in the set of sub-signal pairs of the ranging signal, the system can calculate a phase-based TOA estimate in blocks S130, S132, S134, and S136. More specifically, in block S130, the system extracts the maximum frequency difference Δf to obtain a subset of sub-signal pairs for calculating the phase-based TOA estimate. maxand for each subsignal pair in this subset of subsignal pairs, in block S132, calculate a phase difference Δφ for each subsignal pair, in block S134, calculate a subsignal TOA estimate in a set of subsignal TOA estimates based on the phase difference corresponding to each subsignal pair, and in block S138, calculate a phase-based TOA estimate based on a representative value of the set of subsignal TOA estimates. To identify the phase-based TOA estimate, the system calculates a slope of a linear fit to the phase of each subsignal for the corresponding set of subsignal frequencies. Because the system does not consider pairs of subsignals characterized by frequency differences that result in an accumulation of phase differences greater than 2π radians, the system can avoid the 2π wraparound effect that may prevent the use of phase differences in calculating TOA estimates.

[0040] In one aspect, the system can select a set of frequencies for a set of sub-signals with Δf based on an estimated range between the transmitter and receiver. For example, in response to estimating a larger range between the receiver and transmitter, the system can decrease Δf for the set of frequencies to reduce the effects of phase wraparound. Alternatively, in response to estimating a shorter range between the receiver and transmitter, the system can increase Δf for the set of frequencies to increase the measurement resolution of the TOA. In this manner, the system can compute an accurate phase-based TOA estimate by evaluating both closely and widely spaced (in frequency) sub-signal pairs.

[0041] To calculate the maximum frequency difference for the subset of sub-signal pairs characterized by an accumulation of a total phase difference of less than 2π radians during transmission of the ranging signal, in one aspect, the system can solve the following equation: TIFF0007667897000002.tif11170, where τ maxis the maximum possible propagation time between the transmitter and the receiver. Specifically, the system can estimate a time-of-flight estimate of the ranging signal to identify a subset of multiplexed sub-signals in the set of multiplexed sub-signals, where the subset of multiplexed sub-signals is characterized by a subset of frequencies in the set of frequencies, and each frequency in the subset of frequencies defines a total phase accumulation of less than 2π radians for the duration of the time-of-flight estimate of block S130. Thus, for any sub-signal pair characterized by a frequency difference less than the maximum frequency difference, the system predicts less than one cycle to occur during the TOF of the ranging signal. Additionally or alternatively, the system can calculate the approximate TOF based on past ranging calculations between the transmitter and the receiver, base station search area constraints and / or signal strength measurements. In another aspect, the system can estimate the approximate TOF by Δf by more than the buffer frequency to provide a margin for wrap-around frequency difference. max A sub-signal pair may be selected that is characterized by a frequency less than

[0042] Upon isolating a subset of sub-signal pairs characterized by a frequency difference less than the maximum frequency difference, the system can generate data pairs indicative of the frequency difference Δf and phase difference Δφ of each sub-signal pair in the subset of sub-signal pairs. In an ideal RF channel (an RF channel free of noise, hardware impairments, or propagation-related multipath fading), the phase difference of each data pair corresponds directly to the propagation delay and / or the physical distance or electrical length between the transmitter and receiver (assuming the system successfully filters out sub-signals exhibiting a phase shift greater than 2π), and thus the system can generate a phase-based TOA estimate τ by evaluating the following equation for any data pair (Δf, Δφ): φ can be calculated. TIFF0007667897000003.tif14170However, in a non-ideal RF channel, each data pair is φMore specifically, for each subsignal pair in the subset of multiplexed subsignals, the system may calculate a subsignal pair time of arrival estimate based on a phase difference of the subsignal pair in block S134, and calculate a phase-based time of arrival estimate based on a representative value of the subsignal pair time of arrival estimates for each subsignal pair in the subset of multiplexed signals. For example, the system may generate different values ​​for τ for each data pair in the set of data pairs. φ Calculate the value of τ for the entire data pair. φ The mean or median of x, y, y, and y can then be calculated, from which a phase-based TOA estimate can be calculated.

[0043] Alternatively, τ φ Instead of calculating the average or median of the values ​​of τ φ To reduce the effect of phase delay bias on the value of τ obtained from data pairs characterized by larger values ​​of Δf, φ A weighted average can be calculated that weights the

[0044] 7.1 Uncertainty Estimation In general, the system uses a phase-based TOA estimate τ to inform the computation of a hybrid TOA estimate. φ For the phase-based uncertainty δ φ More specifically, the system can calculate the phase-based uncertainty based on the degree of variability of the set of sub-signal pair TOA estimates in block S138. Thus, the system can apply a weight to the phase-based TOA estimates when calculating the hybrid TOA estimate based on the repeatability of the sub-signal pair TOA estimates.

[0045] In one aspect, the system can calculate the phase-based uncertainty by calculating the standard deviation or root-mean-square deviation of the values ​​of τ across the set of data pairs (i.e., a function of the residuals of each data pair). Thus, the system can calculate the phase-based uncertainty based on the distribution of values ​​of τ for the phase-based TOA estimates.

[0046] 8. MUSIC arrival time estimation In general, the system calculates a phase amplitude TOA estimate δ φ,A to identify a set of multipath components of the ranging signal. More specifically, the system can execute a multiple signal classification algorithm to detect a set of multipath components of the ranging signal in block S140, and determine a phase-amplitude based TOA estimate based on the set of multipath components of the ranging signal in block S142. In particular, the system can calculate a third TOA estimate, which can further improve the hybrid TOA estimate and characterize the multipath environment of the RF channel of the ranging signal.

[0047] In particular, the system can generate a received ranging signal vector based on a series of digital samples indicative of each subsignal in the set of subsignals, calculate an autocorrelation matrix of the received ranging signal vector, calculate for each frequency in the set of frequencies an eigenvector of the autocorrelation matrix and a corresponding eigenvalue of the autocorrelation matrix, sort the eigenvectors of each frequency in the set of frequencies based on the corresponding eigenvalue of each frequency in the set of frequencies, thereby identifying a noise subspace of the eigenvectors and a signal subspace of the eigenvectors, evaluate an estimation function over a range of possible arrival times based on the noise subspace of the eigenvectors and the steering vector, and identify peaks of the estimation function as a set of multipath components of the ranging signal, each multipath component in the set of multipath components corresponding to a multipath arrival time. This process is described in further detail below.

[0048] The system applies the MUSIC algorithm (often used for angle of arrival detection) for TOA calculation and multipath characterization according to the receiver and signal model shown in Figure 2. In Figure 2, each multipath component of a signal has a received TOA τ i By S i (k)=α i (k)e j2πfτi where D denotes the number of multipath components and k denotes the digital samples of the ranging signal. The received ranging signal vector x(k) is captured for M sub-signals with different frequencies and is expressed as: TIFF0007667897000004.tif10170 Furthermore, the system implements the MUSIC algorithm based on the following steering matrix A, which describes each multipath component with respect to the subcarrier signal components in terms of TOA τ: TIFF0007667897000005.tif19170And Δf is the difference between two consecutive frequencies. According to the receiver and signal model mentioned above, TIFF0007667897000006.tif28170

[0049] The system then calculates the autocorrelation matrix: TIFF0007667897000007.tif19170

[0050] This system is R xx After calculating, for i∈{1,...,M}, the eigenvector Calculate TIFF0007667897000008.tif9170 and use R xx Then, based on the estimate of the number of multipath signals, the system calculates the corresponding eigenvalues ​​of R xx The eigenspace of the signal subspace Q s =[ν 1 ,ν 2 ,...,ν D ] and the noise subspace Q n =[ν D+1 ,νD+2 ,...,ν M ]. The steering vector a(τ) for the true multipath delay spans the signal subspace and is therefore orthogonal to the eigenvectors in the noise subspace. That is, for i ∈ {D+1,...,M}, a(τ) H ν i = 0. Therefore, the MUSIC estimation function P MU By evaluating (τ), the system generates a pseudospectrum and can identify the peaks in this spectrum as the TOAs of each multipath component of the signal as follows: TIFF0007667897000009.tif17170

[0051] Once the system generates the MUSIC pseudospectrum, it can select the earliest multipath component in the set of multipath components based on the multipath arrival time of each multipath component. More specifically, the system can then select the minimum TOA represented by the peak of the pseudospectrum as the LOS TOA to generate the phase-amplitude-based TOA estimate δ φ,A can be calculated.

[0052] In applications that perform time difference of arrival (TDOA) localization based on reception of ranging signals by multiple receivers, the system uses the MUSIC estimation function P based on TDOA values ​​instead of TOA values. MU In this aspect, the system can calculate P(Δτ) generated due to various combinations of multipath components received at a pair of receivers. MU Filter out the many peaks in (Δτ) to obtain a phase-based TOA estimate τ φAlternatively, the system can apply a set of geometrical constraints based on the multipath components generated via execution of the MUSIC algorithm to identify a TDOA estimate that best fits the known geometry of the mesh network of transceivers, as described below. For this reason, the system can reject Δτ between multipath components measured at different receivers.

[0053] 8.1 Estimating the Importance of Multipath In general, the system can calculate parameters indicative of the importance of multipaths in the RF channel to inform the combination of TOA estimates when calculating a hybrid TOA estimate. More specifically, the system can calculate a multipath metric (e.g., Rician K-factor, delay spread and / or angle spread) that characterizes the importance of multipaths in the ranging signal channel, such as by comparing the power of line-of-sight components to the power of non-line-of-sight components in the set of multipath components in block S144. Thus, the system can weight the contribution of a phase-based TOA estimate to a phase-amplitude-based TOA estimate when calculating a hybrid TOA estimate for the ranging signal.

[0054] In one embodiment, the system comprises: xx The Rician K-factor is calculated based on the eigenspace of the matrix, which allows the system to calculate a parameter that compares the relative power of the LOS multipath component to the power of other multipath components in the system, thereby characterizing the importance of multipath in the RF channel of the ranging signal.

[0055] 9. Weighted Arrival Time Estimation Generally, the system may calculate a hybrid TOA estimate as a weighted combination of a time-based TOA estimate, a phase-based TOA estimate, and / or a phase-amplitude-based TOA estimate based on a time-based uncertainty, a phase-based uncertainty, and a multipath indicator in block S150. More specifically, the system may calculate a weighted combination of a phase-based TOA estimate and a phase-amplitude-based TOA estimate (wherein the system calculates weights of the phase-based TOA estimate and the phase-amplitude-based TOA estimate based on a multipath indicator), compare the weighted combination with the time-based TOA estimate and the time-based uncertainty, and select the weighted combination as the hybrid TOA estimate in response to detecting that the weighted combination falls within a time-based uncertainty interval. Alternatively, in response to detecting that the weighted combination falls outside the time-based uncertainty interval, the system may select the time-based TOA estimate as the TOA estimate. In this manner, the system weights the combination of the phase-based TOA estimate and the phase-amplitude-based TOA estimate based on a characterization of the system of a multipath environment.

[0056] In general, the system calculates a weighted combination as a weighted average of the phase-based TOA estimate and the phase-amplitude-based TOA estimate, and in response to detection of strong multipath components of the ranging signal (i.e., when the value of the Rician K-factor multipath index is low), the system weights the phase-based TOA estimate relative to the phase-amplitude-based TOA estimate, whereas in response to detection of weak multipath components of the ranging signal (i.e., when the value of the multipath index is high), the system weights the phase-based TOA estimate relative to the phase-amplitude-based TOA estimate. Thus, in view of a strong multipath environment, the system favors a value of the phase-amplitude-based TOA estimate that is more robust against multipath fading, and in view of a weak multipath environment, the system favors a value of the phase-based TOA estimate that is more robust against modeling errors inherent in calibrating hardware imperfections (e.g., non-repeatability and non-linearity).

[0057] Alternatively, the system may calculate an overall measurement uncertainty for the hybrid TOA estimate based on the value of the Rice K factor. For example, a low value of the Rice K factor may indicate a strong multipath environment, which may also reduce the accuracy of the time-based and phase-based TOA estimates. Thus, the system may increase the uncertainty of the hybrid TOA estimate based on the multipath environment characterized by the Rice K factor.

[0058] In one aspect, the system may execute a binary decision tree based on a multipath index threshold (e.g., 10 dB for Rice's K factor) to determine weights for the phase-based TOA estimate and the phase-amplitude-based TOA estimate. In one example, in response to detecting the multipath index exceeding the threshold, the system may set a weight for the phase-based TOA estimate to one and the phase-amplitude-based TOA estimate to zero, thereby setting the hybrid TOA estimate equal to the phase-based TOA estimate. In this example, in response to detecting the Rice's K factor multipath index not exceeding the threshold, the system may set a weight for the phase-based TOA estimate to zero and the phase-amplitude-based TOA estimate to one.

[0059] In another aspect, the system calculates weights for the phase-based TOA estimate and the phase-amplitude-based TOA estimate such that the weights are proportional or inversely proportional to the multipath index, respectively, and calculates a weighted average of the phase-based TOA estimate and the phase-amplitude-based TOA estimate based on previously calculated weights. Thus, the system can include information from both the phase-based TOA estimate and the phase-amplitude-based TOA estimate when calculating a hybrid TOA estimate.

[0060] However, in applications where both the phase-based and phase-amplitude-based TOA estimates are inaccurate, the system can detect their inaccuracies based on a comparison with the time-based TOA estimate. In one aspect, the system can calculate an upper bound of the time-based uncertainty interval and a lower bound of the time-based uncertainty interval by adding and subtracting the time-based uncertainty from the time-based TOA estimate, respectively. The system can then detect whether a weighted combination of the phase-based and phase-amplitude-based TOA estimates described above falls within the calculated time-based uncertainty interval. If so, the system can select the weighted combination as the hybrid TOA estimate.

[0061] In yet another embodiment, the system can calculate a weighted average of a time-based TOA estimate, a phase-based TOA estimate, and a phase-amplitude-based TOA estimate. In this embodiment, the system calculates a weight for each TOA estimate based on a time-based uncertainty, a phase-based uncertainty, and a multipath index, respectively. In this manner, the system can utilize three different TOA estimates to further improve the accuracy of the hybrid TOA estimate.

[0062] In yet another aspect, the system can calculate a threshold interval based on the phase-based uncertainty, detect whether the phase-amplitude-based TOA estimate is within the threshold interval of the phase-based TOA estimate, and calculate a hybrid TOA estimate equal to the phase-amplitude-based TOA estimate in response to detecting a match between the phase-amplitude-based TOA estimate and the phase-based TOA estimate. If the system detects that the phase-amplitude-based TOA estimate is outside the threshold interval of the phase-based TOA estimate, the system can instead calculate a weighted average of the time-based TOA estimate, the phase-based TOA estimate, and / or the phase-amplitude-based TOA estimate.

[0063] 10. Ranging and Multilateration In one aspect, upon calculating the hybrid TOA estimate, the system can calculate the range at which the transmitter transmitted the ranging signal (e.g., the distance between the transmitter and the receiver). More specifically, the system can access the time of departure of the ranging signal from the transmitter, access the relative time bias between the transmitter and the receiver, and calculate a hybrid range estimate for the distance between the transmitter and the receiver based on the hybrid time of arrival estimate, the time of departure of the ranging signal, and the relative time bias between the transmitter and the receiver.

[0064] In one aspect, the system can calculate the TOF of the ranging signal and thus the distance between the transmitter and the receiver by achieving precise clock synchronization between the receiver and the transmitter, compensating for the latency introduced by the application-specific signal processing circuitry, and comparing the hybrid TOA estimate with the known transmission time at the transmitter. The system can then multiply the TOF of the ranging signal by the speed of the ranging signal (e.g., the speed of light, sound, or electricity propagation corresponding to the relevant medium). Thus, multiple receiving nodes in a network of nodes performing method S100 based on the same ranging signal can each calculate the distance between the receiver and the transmitter. The system can then input those distances and the known location of each receiver into a multilateration algorithm to pinpoint the location of the transmitter with high accuracy (e.g., within 10 meters, within 10 centimeters).

[0065] In one aspect, the system may instead include a single receiver that receives multiple ranging signals from multiple transmitters with known locations, in which case the receiver may perform method S100 described above to calculate a hybrid TOA estimate for each ranging signal, calculate the distance between each transmitter and the receiver, and input the distances into a multilateration algorithm to determine the location of the receiver relative to the transmitters with known locations.

[0066] 11. Variations of TDOA and PDOA Ranging Alternatively, in applications where precise clock synchronization between transmitter and receiver cannot be achieved, or where uncalibrated frequency-dependent delays are present in the ranging signal, the system can perform Time Difference of Arrival (hereinafter "TDOA") localization and / or Phase Difference of Arrival (hereinafter "PDOA") to more accurately pinpoint the transmitter's location.

[0067] In one variation shown in FIG. 4, the system generates a time-based TDOA estimate based on cross-correlated ranging signals and / or a phase-magnitude-based TDOA estimate based on complex conjugate multiplication of received complex signals at the correlation peaks of the two receivers, and selects the composite multipath component that is closest to the phase-based TDOA estimate as the phase-magnitude-based TDOA estimate.

[0068] In another variation, upon performing the MUSIC algorithm on the ranging signal as described above, the system generates a MUSIC pseudospectrum for each receiver in the mesh network of receivers. The system can then calculate a hybrid TOA estimate by performing the blocks of method S100 for each receiver in the mesh network. The system can then access the hybrid TOA estimates from the multiple receivers and calculate the hybrid TDOA for each pair of receivers by subtracting the hybrid TOA estimates for each pair of receivers. Having generated a set of TDOA for each pair of transceivers and given the location of each receiver in the mesh network, the system can perform a TDOA multilateration algorithm to calculate the location of the transmitter.

[0069] In the PDOA variation, instead of calculating a TOA estimate at each receiver, the system can access the POA of the ranging signal at each receiver of a pair of receivers in a mesh network of receivers (which may include the relative phase offset of the pair of receivers), calculate the PDOA based on the POA from the pair of receivers by subtracting the relative phase offset of the pair of receivers, and estimate the TDOA via the MUSIC algorithm described above based on the calculated PDOA. However, in this variation, the earliest peak of the MUSIC pseudospectrum may not correspond to the LOS component of the ranging signal of either or both receivers of the pair of receivers. Thus, the system identifies the LOS TDOA from the MUSIC pseudospectrum via other means, which will be described in more detail below.

[0070] In one example of a PDOA variation, the system may select the peak of the MUSIC pseudospectrum with the maximum amplitude as the LOS TDOA. In another example, using the complex phase and amplitude of the ranging signal at the pair of receivers, the system may generate a MUSIC pseudospectrum including multiple peaks for each pair of receivers in the mesh network, such that each peak corresponds to a TDOA estimate. The system may then access a set of TDOAs of the ranging signal at the set of three receivers (i.e., a set of three TDOAs) based on a combination of peaks of the MUSIC pseudospectrum (e.g., by selecting a combination of a peak from the MUSIC pseudospectrum of the first receiver and a peak from the MUSIC pseudospectrum of the second receiver). For any set of three receivers, assuming an ideal channel environment (e.g., exact TDOA in the absence of noise) between the transmitter and receivers, the sum of the TDOAs of the closed graphs of the receivers may be equal to zero. Thus, the system may calculate the sum of the TDOAs and select the combination of multipath components of TDOA whose sum is closest to zero as the LOS TDOA estimate. Thus, in this variation, the system can utilize information from multiple receivers to determine the LOS TDOA for each pair of receivers in the mesh network. The system can repeat this process for each graph of three receivers receiving the ranging signal. Once the three receiver graphs have been iterated to calculate the LOS TDOA for each pair of receivers in the mesh network, the system can perform a TDOA multilateration algorithm to estimate the location of the transmitter relative to the mesh network of receivers.

[0071] In another aspect, the system can perform a geometric analysis of the location of the transmitter relative to the location of the receiver for each combination of multipath components identified by the MUSIC algorithm. For example, the system can calculate a TDOA MUSIC pseudospectrum of the receiver pair of the ranging signal based on the cross-correlation of the ranging signal received at each receiver in the receiver pair, access the known locations of the receivers in the receiver pair or the distance between the receivers in the receiver pair, select TDOA candidates from the peaks of the TDOA MUSIC pseudospectrum, and calculate a 2D location estimate of the transmitter of the ranging signal based on the TDOA candidates. Upon calculating the 2D location estimate of the transmitter, the system can perform a geometric analysis to determine whether the location of the transmitter matches a previously known location of the transmitter and / or a known shape or characteristic of the area containing the transmitter and receiver. If the system is unable to determine a 2D position estimate based on a TDOA candidate (e.g., if the TDOA candidate suggests a distance between the receivers that is greater than the known distance between the receivers) or if the 2D position estimate does not match the known shape of the area and / or the previously known locations of the transmitter or receiver, the system can eliminate the TDOA candidate from the set of likely TDOAs for the ranging signal. By repeating the above steps for each peak in the TDOA MUSIC pseudospectrum that represents a TDOA candidate, the system can reduce the number of TDOA candidates and / or narrow the range of possible TDOA values.

[0072] If there are multipath components in the ranging signals transmitted between the receivers (e.g., in a mesh network deployment), the system can also iterate through the candidate ranges between the receivers as well as the TDOA candidates of the ranging signals received at each receiver in the receiver pair. By evaluating each possible combination of TDOA and ranging candidates for the receiver pair, the system can eliminate combinations that do not match the known geometric relationship between the transmitter and receiver pair.

[0073] The systems and methods described herein may be embodied and / or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions may be executed by a computer-executable component integrated with an application, an applet, a host, a server, a network, a website, a communication service, a communication interface, a hardware / firmware / software element of a user computer or mobile device, a wristband, a smartphone, or any suitable combination thereof. Other systems and methods of embodiments may be embodied and / or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions may be executed by a computer-executable component integrated with the above-mentioned types of devices and networks. The computer-readable medium may be stored in any suitable computer-readable medium, such as a RAM, a ROM, a flash memory, an EEPROM, an optical device (CD or DVD), a hard drive, a floppy drive, or any suitable device. The computer-executable component may be a processor, although any suitable dedicated hardware device may (alternatively or additionally) execute the instructions.

[0074] As will be appreciated from the above detailed description and drawings, and the appended claims, those skilled in the art can make modifications and variations to the embodiments of the invention without departing from the scope of the invention, which is defined in the following claims.

Claims

1. Accessing a ranging signal transmitted from a transmitter and received by a receiver, the ranging signal being transmitted by a transmitter and comprising: a set of multiplexed sub-signals, each multiplexed sub-signal being characterized by a frequency within a set of frequencies; - generating a time-based representation of said ranging signal; - calculating a first time of arrival estimate based on a time-based representation of the ranging signal; - calculating a set of phase differences of sub-signal pairs within a subset of multiplexed sub-signals of the set of multiplexed sub-signals; - calculating a second time of arrival estimate based on the set of phase differences of the sub-signal pairs; characterizing a hybrid time of arrival estimate based on a combination of the first time of arrival estimate and the second time of arrival estimate; - calculating a distance between the transmitter and the receiver based on the hybrid time of arrival estimate.

2. 10. The method of claim 1 , calculating the first time of arrival estimate includes calculating a time-based time of arrival estimate based on the time-based representation; - A method according to claim 1, wherein calculating the second time of arrival estimate comprises calculating a phase-based time of arrival estimate based on a set of phase differences of sub-signal pairs in the subset of multiplexed sub-signals.

3. 10. The method of claim 1 , Capturing a time-based representation of the ranging signal includes capturing a series of time-domain digital samples indicative of the ranging signal; A method, wherein calculating the first time of arrival estimate comprises calculating the first time of arrival estimate based on the series of time domain digital samples.

4. 3. The method of claim 2, ·moreover, o calculating a first uncertainty of the time-based time of arrival estimate based on a sampling frequency associated with the receiver; o calculating a second uncertainty of the phase-based time of arrival estimate based on a degree of variation characteristic of the phase difference of each sub-signal pair in the subset of multiplexed sub-signals; characterizing the hybrid time of arrival estimate based on a combination of the first time of arrival estimate and the second time of arrival estimate comprises calculating the hybrid time of arrival estimate as a weighted combination of the time-based time of arrival estimate and the phase-based time of arrival estimate based on the first uncertainty and the second uncertainty.

5. 5. The method of claim 4, calculating the first uncertainty includes calculating a time-based uncertainty of the time-based time of arrival estimate based on a sampling frequency associated with the receiver; - A method characterized in that calculating the second uncertainty includes calculating a phase-based uncertainty of the phase-based time of arrival estimate based on a degree of variation characteristic of the phase difference of each sub-signal pair in the subset of multiplexed sub-signals.

6. 10. The method of claim 1 , Calculating the second time of arrival estimate - identifying a subset of the multiplexed sub-signals based on a threshold frequency difference, the threshold frequency being based on a threshold propagation time of a ranging signal from a transmitter to a receiver; - for each sub-signal pair in the subset of multiplexed sub-signals, determining a time of arrival estimate for the sub-signal pair based on a phase difference of the sub-signal pair; - calculating, for each sub-signal pair in the subset of multiplexed sub-signals, a representative value indicative of an estimated time of arrival of the sub-signal pair; - calculating the second time of arrival estimate based on the representative value.

7. The method according to claim 4 or 5, ·moreover, o detecting a set of multipath components characteristic of the ranging signal; o calculating a third time of arrival estimate based on a set of multipath components characteristic of the ranging signal; o calculating a multipath index characteristic of a power ratio between line-of-sight and non-line-of-sight components in a set of multipath components characteristic of said ranging signal, - A method, characterized in that characterizing the hybrid time of arrival estimate includes calculating a hybrid time of arrival estimate based on a combination of the time-based time of arrival estimate, the second time of arrival estimate, and the third time of arrival estimate.

8. 8. The method of claim 7, - detecting a set of multipath components characteristic of the ranging signal includes detecting a set of multipath components characteristic of the ranging signal based on a signal classification function; calculating the third time of arrival estimate includes calculating a phase-amplitude based time of arrival estimate based on a set of multipath components characteristic of the ranging signal; - A method according to claim 1, wherein calculating the hybrid time of arrival estimate comprises calculating the hybrid time of arrival estimate as a weighted combination of the first time of arrival estimate, the second time of arrival estimate and the third time of arrival estimate based on the first uncertainty, the second uncertainty and the multipath index.

9. 8. The method of claim 7, calculating the multipath signature characteristic comprises calculating a K-factor characteristic of a power ratio between an earliest arriving component and a subsequent component in a set of multipath components characteristic of the ranging signal; - A method according to claim 1, wherein calculating the hybrid time of arrival estimate comprises calculating the hybrid time of arrival estimate as a weighted combination of the first time of arrival estimate, the second time of arrival estimate and the third time of arrival estimate based on the first uncertainty, the second uncertainty and the K factor.

10. 10. The method of claim 1 , determining a distance between the transmitter and the receiver based on the hybrid time of arrival estimate; - accessing a time of departure of said ranging signal from said transmitter; - accessing a time bias between the transmitter and the receiver; calculating a hybrid ranging estimate of a distance between the transmitter and the receiver based on the hybrid time of arrival estimate, the time of departure of the ranging signal and the time bias.

11. Accessing a ranging signal transmitted from a transmitter and received by a receiver, the ranging signal being transmitted by a transmitter and comprising: a set of multiplexed sub-signals, each multiplexed sub-signal being characterized by a frequency within a set of frequencies; Capturing a time-based representation of the ranging signal; - calculating a time-based time of arrival estimate based on a series of time-based samples; - detecting a set of multipath components characteristic of said ranging signal; calculating a phase-amplitude based time-of-arrival estimate based on a set of multipath components characteristic of the ranging signal; characterizing a hybrid time of arrival estimate based on a combination of the time-based time of arrival estimate and the phase-amplitude-based time of arrival estimate; - calculating a distance between the transmitter and the receiver based on the hybrid time of arrival estimate.

12. 12. The method of claim 11, Detecting a set of multipath components characteristic of the ranging signal includes detecting a set of multipath components characteristic of the ranging signal based on a multiple signal classification function; the method further comprising calculating a time-based uncertainty based on a sampling frequency associated with the receiver; Detecting a set of multipath components characteristic of the ranging signal, o calculating a multipath index characteristic indicative of a ratio between an earliest arriving component and other components in the set of multipath components; - A method characterized in that characterizing the hybrid time of arrival estimate includes calculating a hybrid time of arrival estimate as a weighted combination of the time-based time of arrival estimate and the phase-amplitude-based time of arrival estimate based on the time-based uncertainty and the multipath signature characteristics.

13. 13. The method of claim 12, ·moreover, o calculating a set of phase differences of sub-signal pairs corresponding to sub-carrier frequency pairs within a subset of multiplexed sub-signals of the set of multiplexed sub-signals; o calculating a phase-based time of arrival estimate based on the set of phase differences of the sub-signal pairs; o calculating a phase-based uncertainty of the phase-based time of arrival estimate based on a distribution associated with the set of phase differences of the sub-signal pairs; - A method according to claim 1, wherein calculating the hybrid time of arrival estimate comprises calculating the hybrid time of arrival estimate as a weighted combination of the time-based time of arrival estimate, the phase-based time of arrival estimate and the phase-amplitude-based time of arrival estimate based on the time-based uncertainty, the phase-based uncertainty and the multipath signature characteristic.

14. 14. The method of claim 13, ·moreover, o calculating a first weight for the time-based time of arrival estimate based on the time-based uncertainty; o calculating a second weight for the phase-based time of arrival estimate based on the phase-based uncertainty; o calculating a third weight for the phase-amplitude based time-of-arrival estimate based on the multipath index; 4. The method of claim 3, wherein calculating the hybrid time of arrival estimate comprises calculating the hybrid time of arrival estimate as a weighted average of the time-based time of arrival estimate, the phase-based time of arrival estimate, and the phase-amplitude-based time of arrival estimate based on the first weight, the second weight, and the third weight.

15. Accessing a ranging signal transmitted from a transmitter and received by a receiver, the ranging signal being transmitted by a transmitter and comprising: a set of multiplexed sub-signals, each multiplexed sub-signal being characterized by a frequency within a set of frequencies; Capturing a time-based representation of the ranging signal; - calculating a set of phase differences of sub-signal pairs within a subset of multiplexed sub-signals of the set of multiplexed sub-signals; - calculating a phase-based time of arrival estimate based on a set of phase differences of sub-signal pairs within the subset of multiplexed sub-signals; - detecting a set of multipath components characteristic of the ranging signal based on a multiple signal classification function; calculating a phase-amplitude based time-of-arrival estimate based on a set of multipath components characteristic of the ranging signal; characterizing a hybrid time of arrival estimate based on a combination of the phase-based time of arrival estimate and the phase-amplitude-based time of arrival estimate; - calculating a distance between the transmitter and the receiver based on the hybrid time of arrival estimate.

16. 16. The method of claim 15, ·moreover, o calculating a phase-based uncertainty of the phase-based time of arrival estimate based on a distribution associated with a phase difference of each sub-signal pair in the subset of multiplexed sub-signals; calculating the phase-amplitude based time-of-arrival estimate comprises calculating a multipath metric characteristic of a power ratio between line-of-sight and non-line-of-sight components in a set of multipath components characteristic of the ranging signal; - A method characterized in that characterizing the hybrid time of arrival estimate includes calculating the hybrid time of arrival estimate as a weighted combination of the phase-based time of arrival estimate and the phase-amplitude-based time of arrival estimate based on the phase-based uncertainty and the multipath index.

17. 17. The method of claim 16, ·moreover, in response to detecting a multipath index above a multipath index threshold; - assigning a first weight to the phase-based time of arrival estimate; assigning a second weight to the phase-amplitude based time of arrival estimate; in response to detecting a multipath index below the multipath index threshold, - assigning said first weight to said phase-amplitude based time-of-arrival estimate; assigning the second weight to the phase-based time of arrival estimate; - The method, characterized in that characterizing the hybrid time of arrival estimate includes calculating the hybrid time of arrival estimate as a weighted combination of the phase-based time of arrival estimate and the phase-amplitude-based time of arrival estimate based on the first weight and the second weight.

18. 16. The method of claim 15, ·moreover, o assigning a first weight to said phase-based time of arrival estimate; o assigning a second weight to the phase-amplitude based time of arrival estimate; o decreasing the first weight relative to the second weight in response to detecting a multipath index below a multipath index threshold; o increasing the first weight relative to the second weight in response to detecting a multipath index above the multipath index threshold; - The method, characterized in that characterizing the hybrid time of arrival estimate includes calculating the hybrid time of arrival estimate as a weighted average of the phase-based time of arrival estimate and the phase-amplitude-based time of arrival estimate based on the first weight and the second weight.

19. 16. The method of claim 15, Calculating a distance between the transmitter and the receiver - accessing a time of departure of said ranging signal from said transmitter; - accessing a time bias between the transmitter and the receiver; calculating a hybrid ranging estimate of a distance between the transmitter and the receiver based on the hybrid time of arrival estimate, the time of departure of the ranging signal and the time bias.

20. 20. The method of claim 19, accessing a set of ranging signals transmitted from a set of transmitters and received by a receiver, the set of transmitters characterizing a known location; - calculating a set of hybrid time-of-arrival estimates for the set of ranging signals; - calculating a set of distances between said receivers and corresponding transmitters in said set of transmitters; and performing a multilateration function on the set of distances to determine a position of the receiver relative to the set of transmitters.

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