Method, apparatus and system for detecting a diffracted radio signal

Abstract

A method, apparatus and system for detecting a diffracted signal include receiving a signal at the receiver, providing a local signal, determining a motion of the receiver, correlating the received signal with the local signal to calculate a correlation signal, calculating at least one phase-compensated correlation signal by providing phase compensation of at least one of the local signal, the received signal and the correlation signal based on the determined motion of the receiver and at least one hypothesis for an angle of arrival of the signal, determining whether the received signal is a diffracted signal based on a model and at least one characteristic of the received signal, and performing an action to improve signal reception at the receiver based on a determination that the received signal is a diffracted signal.

Description

METHOD, APPARATUS AND SYSTEM FOR DETECTING A DIFFRACTED RADIO SIGNALCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims benefit of and priority to U.S. Provisional Patent Application Serial No. 63 / 613,456 filed December 21 , 2023, which is herein incorporated by reference in its entirety.BACKGROUNDField

[0002] Embodiments of the present principles relate to reception of radio signals at a receiver and more particularly to a method, apparatus, and system performed at a receiver for identifying a received diffracted radio signal, and then performing an action based on the identification of the diffracted signal.Description of the Related Art

[0003] Global Navigation Satellite Systems (GNSS) such as GPS, Galileo, Glonass and BeiDou enable receivers to determine their location by using radio signals from orbiting satellites. The radio signals are encoded with a pseudorandom number sequence (PRN), such as a Gold code, to enable a distinction between the radio signals and to enable a calculation of a range (or pseudorange) by correlating the received signal with a corresponding local signal.

[0004] The calculation of a pseudorange is based on a knowledge of the real-time position of the satellite and an assumption that the pseudorange is along a line-of- sight path between the receiver and the satellite. However, this is not always the case. It is known that the received signal can sometimes be reflected from one or more surfaces before the radio signal is detected by the receiver. This reflection is known as multipath interference, and is a notorious problem for positioning using GNSS signals. A technique for mitigating multipath interference and improving radio signal reception using receiver motion compensated signal processing is known as SUPERCORRELATION™ and is described in commonly assigned US patent 9,780,829, issued 3 October 2017; US patent 10,321 ,430, issued 11 June 2019; US patent 10,816,672, issued 27 October 2020; US patent publication 2020 / 0264317, published 20 August 2020; US patent publication 2020 / 0319347, published 8 October2020, and US patent application 18 / 228,378, filed 31 July 2023, which are hereby incorporated herein by reference in their entireties. However, even when reflected signals are taken into account, there can still exist errors in a positioning calculation of a receiver due to the reception of diffracted signals.

[0005] Therefore, there is a need in the art to address the technical problem of inaccurate positioning calculation of a receiver position due to a reception of diffracted signals by providing a technical solution including a method, apparatus, and system for detecting diffracted signals and taking action in response to the detected diffracted signal to, for example, improve signal reception.SUMMARY

[0006] Embodiments of the present principles generally relate to a method, apparatus and system for identifying, at a receiver, a diffracted radio signal, and then performing an action based on the identification of the diffracted radio signal to, for example, improve signal reception.

[0007] Various features and advantages of the present principles may be appreciated from a review of the following detailed description of the present disclosure, along with the accompanying figures.BRIEF DESCRIPTION OF THE DRAWINGS

[0008] So that the manner in which the above recited features of the present principles can be understood in detail, a more particular description of the principles, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments in accordance with the present principles and are therefore not to be considered limiting of its scope, for the principles may admit to other equally effective embodiments.

[0009] Figure 1 is a schematic diagram of a receiver in an urban environment;

[0010] Figure 2 is a functional block diagram of an exemplary receiver suitable for implementing embodiments of the invention;

[0011] Figure 3 is a flow diagram showing a sequence of steps that can be performed by a receiver in at least one embodiment of the invention; and

[0012] Figure 4 is a flow diagram showing a sequence of steps that can be performed by a receiver in at least one embodiment of the invention.

[0013] To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. The figures are not drawn to scale and may be simplified for clarity. It is contemplated that elements and features of one embodiment may be beneficially incorporated in other embodiments without further recitationDETAILED DESCRIPTION

[0014] Embodiments of the present principles generally relate to methods, apparatuses and systems for identifying, at a receiver, a diffracted radio signal, and then performing an action based on the identification of the diffracted radio signal. It should be understood however, that there is no intent to limit the concepts of the present principles to the particular forms disclosed. On the contrary, the intent is to cover all modifications, equivalents, and alternatives consistent with the present principles and the appended claims. For example, although embodiments of the present principles will be described primarily with respect to specific receivers, embodiments of the present principles can be implemented with substantially any radio receiver.

[0015] In the disclosure herein, determining if a signal is a diffracted signal can include determining if a received signal is dominated by, or is at least influenced by, a diffracted signal component.

[0016] According to an embodiment of the present principles, there is provided a method performed at a receiver, comprising the steps of: receiving a signal at the receiver from a first remote source; providing a first local signal; determining a motion of the receiver; correlating the received signal with the first local signal to calculate a correlation signal; calculating at least one phase-compensated correlation signal by providing phase compensation of at least one of the local signal, the received signal and the correlation signal based on the determined motion of the receiver and at least one hypothesis for an angle of arrival of the signal; determining whether the receivedsignal is a diffracted signal based on at least one characteristic of the received signal derived from the phase-compensated correlation signal, the at least one hypothesis for the angle of arrival, an estimate for a current position of the receiver; and performing an action based on a determination that the received signal is a diffracted signal. In some embodiments, the method can include a 3D building model to assist in detecting the diffracted signal.

[0017] That is, diffracted signals can provide an additional source of multipath interference. However, it has previously been difficult to identify these signals, and to distinguish them from line-of-sight or reflected signals. Embodiments of the present principles include methods, apparatuses, and systems for classifying a received radio signal as a diffracted signal. In at least one embodiment, classifying a received radio signal as a diffracted signal can be achieved by comparing the power of the phase- compensated correlation signal (i.e., a characteristic of the received signal) with an expected value for the power of the phase-compensated correlation based on a model of diffraction, such as the Fresnel diffraction model, the Uniform Theory of Diffraction (UTD) model, or any other convenient signal diffraction model. In some embodiments, if use of a 3D building model reveals that a line-of-sight signal is likely to be obscured, then the availability of a signal may be indicative of a diffracted signal.

[0018] Alternatively or in addition, in some embodiments, a receiver of the present principles can include a diffracted signal machine learning model (not shown) in a memory of the receiver. For example, and with reference to at least Figure 2, a diffracted signal machine learning model of the present principles can be included in the memory 214 of the signal processor 206 of the receiver. In at least some embodiments the diffracted signal machine learning model of the present principles can be trained to identify whether or not a received signal is a diffracted signal. For example, in some embodiments a diffracted signal machine learning model of the present principles can be trained using a plurality (e.g., hundreds, thousands, millions) of instances of diffracted signals and environmental conditions (e.g., signal strength, angle of arrival, building and other sources of diffraction in a subject environment, time of day, and the like) to make predictions from signals received at a receiver from a remote source, whether or not a received signal is a diffracted signal. For example, in some embodiments, during training, diffracted signals and associated characteristics(e.g., signal characteristics as well as environmental characteristic and the like) and non-diffracted signals and associated characteristics (e.g., signal characteristics as well as environmental characteristic and the like) can be used to train a diffracted signal machine learning model of the present principles to identify whether or not a received signal is a diffracted signal.

[0019] In some embodiments, a diffracted signal machine learning model of the present principles can implement suitable machine learning techniques to learn commonalities in sequential application programs and for determining from the machine learning techniques at what level sequential application programs can be canonicalized. In some embodiments, machine learning techniques that can be applied to learn commonalities in sequential application programs can include, but are not limited to, regression methods, ensemble methods, or neural networks and deep learning such as ‘Seq2Seq’ Recurrent Neural Network (RNNs)ZLong Short-Term Memory (LSTM) networks, Convolution Neural Networks (CNNs), graph neural networks applied to the abstract syntax trees corresponding to the sequential program application, Transformer networks, and the like. In some embodiments a supervised machine learning (ML) classifier / algorithm could be used such as, but not limited to, Multilayer Perceptron, Random Forest, Naive Bayes, Support Vector Machine, Logistic Regression and the like. In addition, in some embodiments, a diffracted signal machine learning model of the present principles can implement at least one of a sliding window or sequence-based techniques to analyze signals.

[0020] In accordance with the present principles, in some embodiments a comparison of the power of the phase-compensated correlation signal with an expected power for a diffracted signal can help to determine whether the signal is indeed a diffracted signal or is another type of signal such as a reflected signal. Alternatively or in addition, in some embodiments, the characteristic of the received signal that is derived from the phase-compensated correlation signal can include a time of arrival (TOA) of the received signal or a Doppler frequency of the received signal. For example, a diffracted signal has a delayed TOA and can have a Doppler frequency shift compared to a TOA and Doppler frequency of a direct signal. In accordance with the present principles, at least one of these characteristics can be analyzed to determine if a received signal is a diffracted signal.

[0021] The identification of a diffracted signal can be used in a number of advantageous ways in accordance with the present principles. For example, in one embodiment, an identified diffracted signal can be discarded from further processing, since otherwise the diffracted signal could introduce a measurement error due to the additional path length. Alternatively or in addition, in some embodiments an identified diffracted signal can be used in a positioning calculation, using, for example, a 3D building model to establish the additional path length to the first remote source due to the diffraction. Such a technique can advantageously mitigate the effects of diffracted signals and can even improve the accuracy of a position determination for a receiver or the determination of another metric of interest, such as clock error in the receiver.

[0022] In some embodiments, a signal can be received simultaneously along several paths, such as an indirect path (i.e., non-line-of-sight path) due to diffraction and a direct path (i.e., line-of-sight path) through an object. In such embodiments, the determination that the received signal “is” a diffracted signal can include a determination that the received signal is dominated by or includes a non-negligible non-line-of-sight component arising from diffraction.

[0023] In some embodiments, a machine learning model can be trained to classify diffracted signals based on at least one of a phase-compensated correlation signal, a 3D building model, the diffraction model and the angle of arrival, or other criteria indicative of a diffracted signal. In some embodiments, the determination of whether or not a received signal is a diffracted signal can be based on such a model of the present principles.

[0024] In some embodiments, the methods of the present principles can be performed in a positioning system. Alternatively or in addition, the methods can be used to process other types of signals using deterministic codes, such as communication signals, including, but not limited to, WIFI, Bluetooth, and / or mobile phone signals including, for example, 3G, 4G, and / or 5G signals.

[0025] In an exemplary embodiment, at least one hypothesis for the determination of an angle of arrival of a signal at a receiver corresponds to a line-of-sight path between the receiver and a first remote source. In such an embodiment, a method of the present principles can include calculating at least one phase-compensated correlationsignal by providing phase compensation of at least one of the local signal, the received signal and the correlation signal based on the determined motion of the receiver along a direction that is aligned with the line-of-sight direction. A diffracted signal can then be identified by comparing the power of the phase compensated correlation signal with an expected power based on a model of diffraction in accordance with the present principles.

[0026] In some embodiments, a method of the present principles can include using a single hypothesis for an angle of arrival corresponding to the line-of-sight path between a receiver and a first remote source. Such method is more computationally efficient than performing the method for a plurality of hypothesised angles of arrival while providing an acceptable level of accuracy. Alternatively or in addition, in some embodiments the single hypothesis for the angle of arrival can correspond to other directions, such as a building edge directly above the line-of-sight direction to the remote source.

[0027] In an exemplary embodiment, a plurality of phase-compensated correlation signals are calculated for a plurality of hypotheses for angles of arrival of a signal at a receiver, and a method of the present principles includes determining whether the received signal is a diffracted signal based on the power of the plurality of phase- compensated correlation signals, the plurality of hypotheses for the angle of arrival, an estimate for a current position of the receiver, and a 3D building model. Such embodiments of the present principles can include constructing a representation of the power of phase-compensated correlation signals, or other characteristics of the received signals (e.g., TOA and / or Doppler frequency), based on different hypothesised angles of arrival. In such embodiments, phase-compensated correlation signals can be determined based on a hypothesised angle of arrival that is along the line-of-sight and a hypothesised angle of arrival along a diffracted path. If the power of these signals is a good match for the expected power, based on a previously determined model of diffraction and a 3D building model, then the signal can be classified as a diffracted signal with a high degree of confidence.

[0028] In some embodiments, the plurality of hypotheses for angles of arrival of the signal comprise the line-of-sight path between the receiver and a first remote source, and one or more angles of arrival based on higher elevation angles. In suchembodiments, it is expected that diffracted signals are most likely to arrive along angles of arrival that have elevation angles that are higher than the expected line-of- sight, keeping in mind that many embodiments of the present principles are likely to be applied to GNSS signals that are diffracted from horizontal building surfaces, and these signals will have elevation angles higher than the line-of-sight path. This approach can optimise computational efficiency because for many applications it will not be efficient to calculate phase-compensated correlation signals having elevation angles that are lower than the expected line-of-sight path, nor will it be efficient to calculate phase-compensated correlation signals having azimuth angles that are different than the expected line-of-sight path. However, embodiments of the present principles are not limited only for use with GNSS signals and can be applied more broadly to any kind of received radio signal that has a repeatable code that allows it to be correlated with a local signal.

[0029] In some embodiments, a 3D building model is based on an estimate of street width and building height. In such embodiments, a detailed 3D building model may not be required. Instead, a basic 3D building model can be constructed based only on an estimate of building height and street width that would provide a likely surface for diffraction. Such embodiments can be used in a determination step to determine whether the power of the phase-compensated correlation signal is a good enough match for an expected power according to the 3D building model and a model of diffraction. In some embodiments the 3D building model can be stored in a memory of a device that includes the receiver, such as a smartphone.

[0030] Alternatively or in addition, in some embodiments a 3D building model can be downloaded from a wireless link, like a cellular or WIFI network. In some associated methods of the present principles, only a model of surfaces that are likely to be responsible for diffraction are required in such a model. Such models of the present principles can be used to improve computational efficiency by only downloading or obtaining those portions of the 3D building model that are relevant for determining whether a signal includes a diffracted signal in accordance with the present principles. Alternatively, in some embodiments, a 3D building model can include a higher resolution / more detailed 3D building model.

[0031] In some embodiments, an estimate of the current position of a receiver is determined by one or more of: GNSS positioning, cellular positioning, and dead reckoning. As such, other signals can be used to calculate an assumed position for the receiver, without making use of the signal from a first remote source. Such estimated position can be used in the methods of the present principles in the classification of a diffracted signal. Advantageously, the positioning methods of the present principles can be performed in common positioning devices that can include a receiver of the present principles, such as smartphones.

[0032] In some embodiments, a method of the present principles can further include discarding the signal from the first remote source from further processing if it is determined that the received signal is a diffracted signal. This method can reduce the impact of errors caused by an additional path length due to the diffraction. Discarding the signal can be used if it is challenging or computationally intensive to calculate an additional path length.

[0033] Alternatively, a method of the present principles can include calculating an additional path length if the received signal is determined to be a diffracted signal. This can be useful in instances in which calculating the additional path length does not create an undue computational burden, or where there are a limited number of signals from remote sources available at the location of the receiver. In some embodiments, the additional path length is calculated based on a 3D building model and an estimate of the current position of the receiver. In such embodiments, it is possible to calculate the additional path length, compared to the line-of-sight path, for the signal that has been diffracted. The additional path length can then be factored into a positioning calculation so that the diffracted signal can be advantageously used to improve a positioning estimate.

[0034] According to another embodiment of the present principles, there is provided a method performed at a receiver, including the steps of: receiving a signal at the receiver from a first remote source; providing a first local signal using a frequency reference of the receiver; determining a motion of the receiver; correlating the received signal with the first local signal to calculate a correlation signal; calculating at least one phase-compensated correlation signal by providing phase compensation of at least one of the local signal, the received signal and the correlation signal basedon the determined motion of the receiver and at least one hypothesis for an angle of arrival of the signal; selecting an angle of arrival based on the calculated at least one phase-compensated correlation signal; determining whether the received signal is a diffracted signal based on the power of the phase-compensated correlation signal, an estimate of the current position of the receiver and the selected angle of arrival; and performing an action based on a determination that the received signal is a diffracted signal.

[0035] In such embodiments, the presence of a diffracted signal can be determined by making a hypothesis of an angle of arrival of the signal that is based on a possible diffraction event. For example, the hypothesised angle of arrival can be based on an elevation angle that is slightly higher than the elevation angle of the line-of-sight path between the receiver and a first remote source. Such a hypothesised angle of arrival would be characteristic of a diffracted signal in an urban environment from a GNSS remote source. If the power of the phase-compensated correlation signal for such a hypothesis is a good match for an expected power of a diffracted signal, then a determination that the signal is a diffracted signal can be made.

[0036] Embodiments of the present principles are not only suitable for use with GNSS remote sources in urban environments, but can also be applied to any kind of received radio signal that can be correlated with a local signal, and which can have experienced diffraction from a remote source to the receiver.

[0037] A machine learning model of the present principles can be trained to classify diffracted signals based on, for example, but not limited to, a phase-compensated correlation signal, an angle of arrival, a line-of-sight direction to a remote source, the receiver’s current position, and the like, or various combinations of the foregoing. In accordance with the present principles, a determination of whether the received signal is a diffracted signal can be based on such a model.

[0038] In some embodiments, a method of the present principles can further include calculating a plurality of phase-compensated correlation signals by providing phase compensation of at least one of a local signal, a received signal and a correlation signal based on the determined motion of the receiver and a plurality of hypotheses for determining an angle of arrival of the signal, and by selecting an angle of arrivalbased on the calculated plurality of phase-compensated correlation signals. In such embodiments, a search across a plurality of hypotheses can be performed for the angle of arrival. For each hypothesis, it can be determined that a signal is received from the corresponding angle of arrival if the phase-compensated correlation signal satisfies a specific criterion, such as exceeding a threshold power. In some cases, multiple copies of a signal can be received simultaneously from different angles of arrival, for example a line-of-sight signal, and / or one or more reflected signals or diffracted signals. In such cases, each angle of arrival corresponding to a received signal can then be selected and analysed to determine whether the received signal is likely to be a diffracted signal. Such embodiments can further include a 3D building model to further assist in determining whether or not the received signal is a diffracted signal. For example, a received signal can be determined to be a diffracted signal if the selected angle of arrival is directed towards the edge of a building, based on the receiver’s current position and the 3D building model. The determination can also be based on the determined power of the phase-compensated correlation signal, and a comparison with an expected power for a known model of diffraction.

[0039] The angle of arrival of a diffracted signal is likely to be similar, but not equal to, an expected angle of arrival for a line-of-sight path between a remote source and a receiver. A difference between the angle of arrival can be attributed to a change in the path of the diffracted signal due to the diffraction. Such difference can be detected and, combined with a weak signal strength, can be used as an indicator that a received signal is a diffracted signal.

[0040] In some embodiments, to reduce a computational load, searches for a diffracted signal can focus on hypotheses for the angle of arrival that have a higher elevation angle than the line-of-sight angle of arrival (i.e., assuming the diffracted signals occur from the tops of buildings).

[0041] Figure 1 depicts a schematic diagram of a receiver 2 in an urban environment 100. The receiver 2 of Figure 1 is depicted as a smartphone but in alternate embodiments can be any kind of electronic device having an integrated radio receiver such as, but not limited to, a wearable device, a car or a drone, to name a few examples. In the embodiment of Figure 1 , the receiver 2 is positioned at street level between a first building 12 and a second building 14. In the embodiment of Figure 1 ,a communications transmitter 10, such as a cellular transmitter, is located on top of the first building 12, and many other signal transmitters may be provided locally including, but not limited to, Bluetooth, WIFI and other communications transmitters with which the receiver 2 can communicate. In the embodiment of Figure 1 , a number of GNSS satellites 4, 6, 8 are present in the sky above the receiver 2. Although only three satellites 4, 6, 8 are depicted in the embodiment of Figure 1 , a skilled person would appreciate that a larger number of satellites can be visible at a given location. In some embodiments, signals received from four or more GNSS satellites are used to provide a three-dimensional position fix.

[0042] In the embodiment of Figure 1 , the receiver 2 can receive a line-of-sight (LOS) signal 16 (also referred to as a direct or straight-line signal) from a first satellite 4 and a reflected signal 18, which can also be referred to as a non-line-of-sight (NLOS) or indirect signal. In the embodiment of Figure 1 , the reflected signal 18 is received following a reflection from the second building 14. The receiver 2 can also receive a line-of-sight signal from the second satellite 6, which is in an overhead position. The receiver 2 can also receive signals from a third satellite 8. In the embodiment of Figure 1 , a reflected signal 22 is received following a reflection from the first building 12. The line-of-sight path 26 is blocked by the second building 14, and the receiver 2 may or may not be able to detect this signal depending on the amount by which the signal is attenuated. Furthermore, in the embodiment of Figure 1 , the receiver 2 receives a diffracted signal 24, which is another type of NLOS signal. In the embodiment of Figure 1 , the radio signal that is emitted by the third satellite 8 travels as a wave on an everexpanding sphere, centred on the point of transmission. The radio wave undergoes diffraction when it encounters a sharp edge, such as the edge 28 of the second building 14. The diffraction causes the signal to change direction, such that it can provide an additional path from the third satellite 8 to the receiver 2. In the embodiment of Figure 1 , the diffracted signal 24 has a signal strength that is much lower than the signal strength of an unattenuated line-of-sight signal 20. In the embodiment of Figure 1 , there can be other signal paths that are not shown in the schematic such as, the diffracted signal 24 can also be reflected from the first building 12. Additionally, the signal from the second satellite 6 can be reflected off one or more of the first and second buildings 12, 14.

[0043] As described in detail below, the receiver 2 of Figure 1 can implement a SUPERCORRELATION™ technique as described in commonly assigned: US patent 9,780,829, issued 3 October 2017; US patent 10,321 ,430, issued 11 June 2019; US patent 10,816,672, issued 27 October 2020; US patent publication 2020 / 0264317, published 20 August 2020; US patent publication 2020 / 0319347, published 8 October 2020, and US patent application 18 / 228,378, filed 31 July 2023, which are hereby incorporated herein by reference in their entireties. The SUPERCORRELATION™ technique can be used to determine a direction of arrival (DoA), or angle of arrival, of received signals. In such embodiments, motion information relating to the receiver 2 can be used to perform motion compensated correlation of received signals. From a motion compensated correlation process, the DoA of received signals can be estimated.

[0044] Figure 2 depicts a high-level block diagram of the receiver 2 of Figure 1 in accordance with at least one embodiment of the present principles. The receiver 2 of Figure 2 illustratively comprises a mobile platform 200, an antenna 202, a receiver front end 204, a signal processor 206, and a motion module 228. In some embodiments, the receiver 2 of Figure 2 can form a portion of a devices including but not limited to, a laptop computer, a mobile phone, a tablet computer, an Internet of Things (loT) device, an unmanned aerial vehicle, a mobile computing system in an autonomous vehicle, a human operated vehicle, and the like.

[0045] In the receiver 2 of Figure 2, the mobile platform 200 and the antenna 202 are an indivisible unit in which the antenna 202 moves with the mobile platform 200. The operation of the SUPERCORRELATION™ technique is based upon determining the motion of the signal receiving antenna. Any mention of motion herein refers to the motion of the antenna 202. In some embodiments, the antenna 202 can be separate from the mobile platform 200. In such embodiments, the motion estimate used in the motion compensated correlation process is the motion of the antenna 202. In most embodiments, the motion of the mobile platform 200 is the same as the motion of the antenna 202 and, as such, the following description will assume that the motion of the platform 200 and the antenna 202 are the same.

[0046] In the embodiment of Figure 2, the mobile platform 200 comprises a receiver front end 204, a signal processor 206 and a motion module 228. In the embodimentof Figure 2, the receiver front end 204 downconverts, filters, and samples (digitizes) the received signals in a manner that is well-known to those skilled in the art and, as such, will not be described in detail herein. The output of the receiver front end 204 is a digital signal containing data. The data of interest is a deterministic training or acquisition code, e.g., a Gold code.

[0047] In the embodiment of Figure 2, the signal processor 206 comprises at least one processor 210, support circuits 212 and memory 214. The at least one processor 210 can include any form of processor or combination of processors including, but not limited to, central processing units, microprocessors, microcontrollers, field programmable gate arrays, graphics processing units, digital signal processors, and the like. Alternatively or in addition, in some embodiments, the functionality of a signal processor 206 of the present principles can be performed remotely on a server. In such an embodiment, the receiver 2 can communicate wirelessly with the server and position inferences are remotely computed.

[0048] Referring back to Figure 2, the support circuits 212 can include well-known circuits and devices facilitating functionality of the processor(s). In the embodiment of Figure 2, the support circuits 212 can include one or more of, or a combination of, power supplies, clock circuits, analog-to-digital converters, communications circuits, cache, displays, and / or the like.

[0049] In the embodiment of Figure 2, the memory 214 comprises one or more forms of non-transitory computer readable media including one or more of, or any combination of, read-only memory or random-access memory. The memory 214 stores software and data including, for example, signal processing software 216, positioning software 208 and data 218. In some embodiments, the data 218 can include 3D building models 220, which can be downloaded from a communications network or stored in the memory 214.

[0050] In the embodiment of Figure 2, the motion module 228 generates a motion estimate for the receiver 2. The motion module 228 can include an inertial navigation system (INS) 230 as well as a global navigation satellite system (GNSS) receiver 226 such as GPS, GLONASS, GALILEO, BEIDOU, etc. The INS 230 can include one or more of, but not limited to, a gyroscope, a magnetometer, an accelerometer, and thelike. To facilitate motion compensated correlation, the motion module 228 produces motion information (sometimes referred to as a motion model) comprising at least a velocity of the antenna 202 in the direction of an emitter of interest (i.e. , an estimated direction of a source of a received signal). In some embodiments, the motion information can also include estimates of platform orientation or heading including, but not limited to, pitch, roll and yaw of the platform 200 / antenna 202.

[0051] In the embodiment of Figure 2, the receiver 2 also includes a local oscillator 232 that can generate a local frequency reference. In a receiver, such as a smartphone, the local oscillator 232 can be based on a quartz crystal, although other kinds of oscillators can also be used.

[0052] Figure 3 depicts a flow diagram of a method for detecting a diffracted signal, which can be performed by the receiver 2, in accordance with at least one embodiment of the present principles. The method of Figure 3 can be performed for each of n remote sources from which a radio signal is received by the receiver 2. For example and with reference to Figure 1 , the remote sources can include the first, second and third satellites 4, 6, 8. It should be noted that methods of the present principles are not limited to signals from satellites but can be applied to other radio signals that are encoded with a ranging / acquisition / synchronization code, including, but not limited to, cellular telecommunications signals such as 3G, 4G, or 5G signals, WIFI signals, Bluetooth signals, and the like.

[0053] The method can begin at 300 during which a signal is received from a first remote source. The method can proceed to 302.

[0054] At 302, the signal processor 206 generates a local signal for the corresponding received signal, using a frequency reference from the local oscillator 240. The method can proceed to 304.

[0055] At 304, the received signal is correlated with the local signal to generate a correlation signal. The method can proceed to 306.

[0056] At 306, the signal processor 206 is configured to calculate a phase- compensated correlation signal for a particular angle of arrival hypothesis (i), based on the motion of the antenna 202 along an axis that is aligned with the hypothesisedangle of arrival. Calculating the phase compensated correlation signal can be performed by adjusting the local signal, the received signal, or the correlation signal to account for the component of motion of the antenna 202 in the hypothesised direction of arrival by, in some embodiments, implementing the SUPERCORRELATION™ technique described in the commonly assigned patents and patent applications referenced above. In other embodiments, a number of techniques can be used at 306 to generate the one or more hypothesised angles of arrival.

[0057] For example, in one embodiment, a single hypothesised angle of arrival can be generated that corresponds to the LOS direction from the antenna 202 to a remote source. In some embodiments, this direction can be determined using GNSS ephemeris, for GNSS positioning, or using stored positions of cellular towers, for example, together with an initial estimate or knowledge of the position of the antenna 202. The position of the antenna 202 can be inferred from a previous or less accurate positioning calculation, dead reckoning, or any other suitable method.

[0058] Alternatively or in addition, in some embodiments hypothesised angles of arrival can be generated based on the expected angle of arrival of a line-of-sight signal, and a few angles around the expected angle of arrival. The receiver’s position can be determined using techniques such as GNSS positioning or positioning using other signals of opportunity. Such an approach can help first to identify the angle of arrival of direct, line-of-sight signals. Once these signals have been identified the process can be stopped. In some embodiments, the searching can continue based on other hypothesised angles of arrival in, for example, a brute-force search.

[0059] Alternatively or in addition, in some embodiments the hypothesised angles of arrival can be generated using data from a 3D building model 220. In such embodiments, the generated data can be used, together with knowledge or an initial estimate of the receiver’s position and the positions of the remote sources to determine the likely angles of arrival for received signals based on the available LOS paths, and possible NLOS paths, which includes reflected paths and diffracted paths. In such embodiments, a few hypothesised angles of arrival can be selected around each possible LOS and NLOS path. This enables the process to test differenthypotheses. Each hypothesis can be tested by generating a phase-compensated correlation signal for that hypothesis in accordance with the present principles.

[0060] In embodiments in which it is desirable to improve computational efficiency, the hypotheses can be limited to angles of arrival that have elevation angles that are higher than the elevation angle of the LOS path. Such embodiments can be applied, for example, in an urban environment, where one might assume that signals diffracted over the tops of buildings make up the majority of the diffracted signals received by the receiver. As such, such embodiments can advantageously focus computational resources on hypotheses that are most likely to correspond to the angle of arrival of a diffracted signal. The method can proceed to 308.

[0061] At 308, the above process can then be repeated for one or more hypotheses for the angle of arrival. In some embodiments, the hypothesis incrementation of 308 can be skipped in the case where only one hypothesis for the angle of arrival is generated.

[0062] In some embodiments, when using the SUPERCORRELATION™ technique as described above, the phase-compensated correlation signals will generally have a relatively higher power when the corresponding hypothesised direction of arrival and an actual direction of arrival of the received signal are closely aligned. Equally, the phase-compensated correlation result will have a relatively lower power when the hypothesised direction of arrival and an actual direction of arrival of the received signal do not correspond. Repeating the calculation at 306 for a plurality of hypotheses allows the power of a signal coming from different directions of arrival (for example, from directions corresponding to a line-of-sight signal, or one or more reflected or diffracted signals) to be mapped on the sky. However, as discussed further below, determining whether a given angle of arrival corresponds to a diffracted signal can be performed using only one hypothesised direction of arrival and a 3D building model, which may be more computationally efficient than performing phase compensation for a large number of angle of arrival hypotheses.

[0063] When 306 and 308 have been completed, phase-compensated correlation signals have been calculated for one or more hypotheses of the angle of arrival of the signal from a particular remote source. Each phase-compensated correlation signalcan then be used to derive a signal characteristic of the received signal that indicates the received signal is a diffracted signal. In one embodiment, each phase- compensated correlation signal has a signal power for each hypothesised angle of arrival, and these values can be used to populate a dataset, which can represent a power distribution on the sky for a given emitted signal. Each local maximum of signal power values in the dataset is likely to correspond to an angle of arrival in which a signal is received. This can correspond to a LOS signal or a NLOS signal. There can be one or more local maximum, depending on the environment for that particular signal.

[0064] Alternatively or in addition to a determined angle of arrival of a received signal, in some embodiments of the present principles, the at least one characteristic of the received signal that can be derived from the phase-compensated correlation signal can include a time of arrival (TOA) of the received signal or a Doppler frequency of the received signal. A diffracted signal has a delayed TOA and can have a Doppler frequency shift compared to a TOA and Doppler frequency of a direct signal. In accordance with the present principles, at least one of these characteristics can be analyzed to determine if a received signal is a diffracted signal. The method can proceed to 310.

[0065] At 310, the signal processor 206 is configured to determine whether a received signal is likely to correspond (or be dominated by) a diffracted signal. In some embodiments, such a calculation is performed based on a number of inputs. In some embodiments, a first input for performing a calculation can include a power of the phase-compensated correlation signal along the selected angle of arrival. Such an input includes is a measure of the power of the phase-compensated correlation signal along the relevant path. Unattenuated LOS signals are generally expected to have the highest power; diffracted signals, on the other hand, are NLOS signals that are likely to have a lower power due to the way in which a diffracted wave propagates into the geometric shadow of the obstacle that caused the diffraction.

[0066] In some embodiments, signal strength alone cannot identify a diffracted signal, however, because some LOS signals can have a low signal power if they are significantly attenuated due to one or more intervening objects, like the canopy of a tree, or a part of a building. Reflected signals are also likely to have a lower signalpower depending on the properties of the surface(s) from which they have reflected, the number of reflections that have occurred, and whether there are any intervening objects that could cause attenuation of the signal (before or after reflection).

[0067] As such, in some embodiments, a second input, at 310, can include the selected angle of arrival and / or a third input, at 310, can include an estimate for the current position of the receiver, using methods described above. In some embodiments, a fourth input for determining whether or not a signal is a diffracted signal can include a 3D building model 220 of the environment of the receiver 2.

[0068] Using such inputs, a determination can be performed, in some embodiments, using a model of diffraction enabling an estimation of how the power of a diffracted signal disperses over an area from one or more points of diffraction.

[0069] In some embodiments, a calculation / analysis of the present principles can be performed using a single phase-compensated correlation signal corresponding to the LOS direction to the remote source. In such an embodiment, a 3D building model can be used to determine whether the LOS direction to the remote source is blocked by a building, as illustrated by LOS signal 26 in Figure 1 . The power of such a signal would be expected to be relatively low due to attenuation by the building. If it is determined that the power of the phase compensated correlation signal corresponding to the LOS direction is higher than expected, this could indicate that the received signal corresponds to a diffracted signal. In some embodiments, the power of the phase- compensated correlation signal corresponding to the LOS direction can be analysed using a suitable model of diffraction, such as the Fresnel diffraction model, the Uniform Theory of Diffraction (UTD) or any other convenient model, and the 3D building model. If the diffraction model predicts, based on the environment and position of the receiver 2, that the antenna 202 would receive a signal power from the remote source consistent with the power determined at 306, the signal received at 300 can be determined to be a diffracted signal.

[0070] In some embodiments, a plurality of phase-compensated correlation signals corresponding to a plurality of directions of arrival can be calculated / analyzed and stored in a dataset. In such embodiments, analyzing the power of the phase- compensated correlation signals at 310 can include analyzing the dataset todetermine whether the distribution of power over the angles of arrival is consistent with a prediction of the distribution of power based on a 3D building model and / or a diffraction model to enable received signals to be classified as diffracted signals or a signal dominated by a diffracted component.

[0071] In some embodiments, a calculation / analysis of 310 can be performed based on other hypothesised angles of arrival. For example, a phase-compensated correlation signal for a single hypothesised angle of arrival can be calculated at 306 corresponding to the nearest building edge above the LOS direction. Estimates of the position of the receiver 2 and the remote source (n) together with the 3D building model can be input to the diffraction model to predict the power that a diffracted signal originating from this direction should have. This power can be compared to the power of the received signal of 300 and / or the power of the phase-compensated correlation signal of step 306 to establish whether the received signal of 300 is or is dominated by a diffracted signal. Such an embodiment can similarly be expanded to analyse phase-compensated correlation signals for additional directions of arrival.

[0072] As such, at 310, at least one of the above described first, second, third and fourth inputs can be provided, and a determination can be made as to whether the signal is a diffracted signal or is dominated by a diffracted component. As described above, in some embodiments such a determination can also be made with predetermined threshold values. That is, a machine learning model can be trained to classify diffracted signals based on the above criteria. The method can proceed to 312.

[0073] At 312 the signal processor 206 is configured to perform an action based on the classification of a signal as a diffracted signal. The classification can be used in a number of advantageous ways in subsequent processing operations. For example, in one embodiment, the diffracted signal can be discarded for further processing, since otherwise it could introduce a measurement error due to the additional path length. Alternatively, in other embodiments, the diffracted signal can be used in a position calculation, for example using the 3D building model to establish the additional path length to the first remote source due to the diffraction. Such determined additional path length can be used to determine a position of a receiver as well. That is, in some embodiments, the processor 210 can determine a position of the receiver 2 based onthe signals received by the receiver 2 using the positioning software 208. The method can proceed to 314.

[0074] At 314, the method can be repeated to consider the next signal that is received by the receiver. As such, signals can be analysed in accordance with the present principles for signals from each remote source. The method of Figure 3 of the present principles can be used to identify and mitigate the effects of any diffracted signal that is received as described herein. Although in the embodiment of Figure 3, 314 is presented as a loop, in some embodiments the processing of signals from other remote sources could also be undertaken in parallel.

[0075] Figure 4 depicts a flow diagram of a method for detecting a diffracted signal, which can be performed by the receiver 2, in accordance with at least one alternate embodiment of the present principles. The method of Figure 4 can be performed for each signal received from n remote sources. The embodiment of Figure 4 is discussed with reference to the first, second and third satellites 4, 6, 8 in Figure 1 , but it will be appreciated that there can be many more satellites in alternate embodiments. Additionally, it should be appreciated that the present principles are not limited to signals from satellites and in some embodiments can be applied to other radio signals that are encoded with a ranging / acquisition / synchronization code, including, but not limited to, cellular telecommunications signals such as 3G, 4G, or 5G signals, WIFI signals, Bluetooth signals, and the like.

[0076] The method of Figure 4 begins at 400 during which a signal is received from a first remote source. The method can proceed to 402.

[0077] At 402, the signal processor 206 generates a local signal for the corresponding received signal, using a frequency reference from the local oscillator 240. The method can proceed to 404.

[0078] At 404, the received signal is correlated with the local signal to generate a correlation signal. The method can proceed to 406.

[0079] At 406, the signal processor 206 is configured to calculate a phase- compensated correlation signal for a particular angle of arrival hypothesis (i), based on the motion of the antenna 202 along an axis that is aligned with the hypothesisedangle of arrival. Calculating the phase compensated correlation signal can be performed by adjusting the local signal of 302, the received signal of 300, or the correlation signal of 304 of Figure 3 to account for the component of motion of the antenna 202 in the hypothesised direction of arrival, in some embodiments by implementing the SUPERCORRELATION™ technique. The calculation can then be repeated for each of a plurality of hypotheses for the angle of arrival. This is illustrated in Figure 4 by the hypothesis increment at 408.

[0080] In the embodiment of Figure 4, a number of techniques can be used at 406 to generate each of the plurality of hypothesised angles of arrival. In one embodiment, the signal processor 206 can be configured to perform a brute-force search of all possible angles of arrival in a spherical search space or hemi-spherical search space. In practice, a hemi-spherical search space is preferred by only searching for hypothesised angles of arrival in the sky that are above the receiver 2. In some embodiments, to reduce a computational load, the hypothesised angles of arrival can be generated based on the receiver’s position and a knowledge of the positions of the remote sources. The hypothesised angles of arrival can correspond to only the expected angle of arrival of a line-of-sight signal, and a few angles around the expected angle of arrival.

[0081] In some embodiments, the receiver’s position can be determined using techniques such as GNSS positioning or positioning using other signals of opportunity. The remote sources’ positions can be determined using GNSS ephemeris, for GNSS positioning, or using stored positions of cellular towers. Such embodiments of the present principles enable first, an identification of the angle of arrival of direct, line-of- sight signals. Once these signals have been identified, the process can be stopped. Alternatively, in some embodiments the searching can continue based on other hypothesised angles of arrival in a brute-force search.

[0082] In some embodiments, the hypothesised angles of arrival can be generated using data from a 3D building model 220. Such hypothesised angles of arrival can be used, together with a knowledge of the receiver’s position and the positions of the remotes sources, to determine the likely angles of arrival for received signals, based on the available line-of-sight (LOS) paths, and possible non-line-of-sight (NLOS) paths, which includes reflected paths and diffracted paths. In such embodiments, afew hypothesised angles of arrival can be selected around each possible LOS and NLOS path. This enables the testing of different hypotheses. Each hypothesis can be tested by generating a phase-compensated correlation signal for that hypothesis.

[0083] In embodiments in which it is desirable to improve computational efficiency, the hypotheses can be limited to angles of arrival that have elevation angles that are higher than the elevation angle of the LOS path. Such embodiments can be applied, for example, in an urban environment, where one might assume that signals diffracted over the tops of buildings make up the majority of the diffracted signals received by the receiver. Thus, this approach can advantageously focus computational resources on hypotheses that are most likely to correspond to the angle of arrival of a diffracted signal.

[0084] When the operations at 406 and 408 have been completed, phase- compensated correlation signals have been calculated for a plurality of hypotheses of the angle of arrival of the signal from at least one remote source. Each phase- compensated correlation signal can be used in accordance with the present principles to derive at least one signal characteristic of the received signal that indicates the received signal is a diffracted signal. For example, in some embodiments, each phase-compensated correlation signal includes a signal power for each hypothesised angle of arrival, and these values can be used to populate a dataset. Each local maximum in the signal power value is likely to correspond to an angle of arrival in which a signal is received, which can correspond to a LOS signal or a NLOS signal. It should be noted that in embodiments of the present principles, there can be one or more local maximum, depending on the environment for that particular signal.

[0085] Alternatively or in addition, in some embodiments, the characteristic of the received signal that is derived from the phase-compensated correlation signal can include a time of arrival (TOA) of the received signal or a Doppler frequency of the received signal. That is, a diffracted signal has a delayed TOA and can have a Doppler frequency shift compared to a TOA and Doppler frequency of a direct signal. At least one of these characteristics (e.g., power, TOA, Doppler and the like) can be analyzed in accordance with the present principles to determine if a received signal is a diffracted signal. The method can proceed to 410.

[0086] At 410, an angle of arrival is selected from a dataset for local maxima in the phase-compensated correlation signals. In the embodiment of Figure 1 , these local maxima can correspond to the LOS signal 16 and the NLOS signal 18 for the first satellite 4. In the scenario including the second satellite 6, there can be a single LOS signal 20, and in the scenario including the third satellite 8, there can be two NLOS signals 22, 24 for a reflected signal and a diffracted signal, respectively. In such embodiments, the selected angle of arrival can correspond to the hypothesised angle of arrival that corresponds to the highest signal power value in the phase- compensated correlation signals. That is, a selected angle of arrival can be based on an identified peak in average signal power values. As such, in some embodiments a smoothing function can be applied to the signal power values in two-dimensions before a peak is identified. The method can proceed to 412.

[0087] At 412, the signal processor 206 is configured to determine whether a received signal for a selected angle of arrival is likely to correspond to a diffracted signal. This calculation / analysis is performed based on a number of possible inputs. For example, in some embodiments, a first input can include the power of the phase-compensated correlation signal along a selected angle of arrival. This is a measure of the power of the phase-compensated correlation signal along the relevant path. Typically, unattenuated LOS signals are generally expected to have the highest power. Diffracted signals, on the other hand, are NLOS signals that are likely to have a lower power due to the way in which a diffracted wave propagates into the geometric shadow of the obstacle that caused the diffraction.

[0088] In some embodiments, however, signal strength alone cannot identify a diffracted signal because some LOS signals can have a low signal power if they are significantly attenuated due to one or more intervening objects, like the canopy of a tree, or a part of a building. Reflected signals are also likely to have a lower signal power, depending on the properties of the surface(s) from which they are reflected, a number of reflections that have occurred, and whether there are any intervening objects that could cause attenuation of the signal (before or after reflection). In some embodiments of the present principles, a minimum signal strength threshold can be provided and candidate diffracted signals can be selected from those signals that have a signal power below the threshold.

[0089] In some embodiments, at 412 a second input can include a selected angle of arrival, and whether the selected angle of arrival is directed towards the edge of a building, based on a knowledge of the receiver’s position and a 3D building model. In such embodiments, the signal processor 206 can be configured to perform 3D ray tracing based on an initial estimate of the receiver’s position and the 3D building model. If a diffracted signal is present, then the selected angle of arrival is likely to point towards the edge of a building, which is likely to be a long, straight edge (as would typically be found in a city environment).

[0090] In some embodiments, at 412 a third input can include information regarding whether the selected angle of arrival is similar to, but not equal to, the expected angle of arrival for a LOS signal. In the embodiment of Figure 1 , the angle of arrival of the NLOS signal 24 is not equal to the angle of arrival of the LOS signal 26 because the NLOS signal 24 has been diffracted around the edge 28 of the second building 14. However, the angle of arrival of the NLOS signal 24 needs to be within a threshold range of the angle of arrival of the LOS signal if the signal is to have a high enough signal strength to be detectable.

[0091] As such, at 412 the above mentioned first, second and third inputs can be provided, and a determination can be made as to whether the signal is a diffracted signal. As described herein, in some embodiments the determination can be made based on a comparison with predetermined threshold values. Alternatively or in addition, in some embodiments a machine learning model can be trained to classify diffracted signals based on the above criteria. The method can proceed to 414.

[0092] At 414 the signal processor 206 is configured to perform an action based on the classification that a received signal is a diffracted signal in accordance with embodiments of the present principles. That is, in some embodiments, the classification can be used in a number of advantageous ways in subsequent processing steps. In one embodiment, the diffracted signal can be discarded for further processing, since otherwise the diffracted signal could introduce a measurement error due to the additional path length. In some embodiments, the diffracted signal can be used further in a positioning calculation of, for example at least one of a receiver receiving the signal or a remote source of the diffracted signal, byusing the 3D building model to establish the additional path length to the first remote source due to the diffraction. The method can end or proceed to optional 416.

[0093] That is, in some embodiments at 416 the method can be repeated to consider the next signal that is received. As such, signals can be analyzed for each remote source in turn. Such embodiments can be used to identify and mitigate the effects of any diffracted signal that is present. Although in the embodiment of Figure 4 the operation at 416 is presented as a loop, alternatively or in addition, in some embodiments processing other signals from other remote sources could also be undertaken in parallel.

[0094] Following completion of the method of Figure 4, the processor 210 can calculate a position of the receiver 2 based on a determination of the n received signals received at 400 of the method of Figure 4 using the positioning software 208, in embodiments in which the remote sources are positioning sources. The method of Figure 4 can then be ended.

[0095] In some embodiments of the present principles a method performed at a receiver includes receiving a signal at the receiver from a remote source, providing a local signal, determining a motion of the receiver, correlating the received signal with the local signal to calculate a correlation signal, calculating at least one phase- compensated correlation signal by providing phase compensation of at least one of the local signal, the received signal and the correlation signal based on the determined motion of the receiver and at least one hypothesis for an angle of arrival of the signal, determining whether the received signal is a diffracted signal based on a model and at least one characteristic of the received signal derived from at least one of the phase- compensated correlation signal, the at least one hypothesis for the angle of arrival, or an estimate for a current position of the receiver, and performing an action to improve signal reception at the receiver based on a determination that the received signal is a diffracted signal.

[0096] In some embodiments, the calculating of the at least one phase-compensated correlation signal is performed using a SUPERCORRELATION™ technique.

[0097] In some embodiments, the received signal is determined to be a diffracted signal if the at least one characteristic of the derived signal matches a known characteristic of a diffracted signal identified using the model.

[0098] In some embodiments, the model includes at least one of a Fresnel diffraction model, a Uniform Theory of Diffraction (UTD) model, a building model, or a machine learning model.

[0099] In some embodiments, the action includes removing an identified diffracted signal from a calculation to determine a position of the receiver.

[0100] In some embodiments, the action includes using characteristics of the diffracted signal along with a building model to determine a position of the receiver.

[0101] In some embodiments of the present principles, an apparatus for detecting a diffracted signal includes at least one processor and a memory accessible to the processor. In some embodiments, the memory has stored therein at least one of programs or instructions, which when executed by the processor, configures the apparatus to receive a signal at the receiver from a remote source, provide a local signal, determine a motion of the receiver, correlate the received signal with the local signal to calculate a correlation signal, calculate at least one phase-compensated correlation signal by providing phase compensation of at least one of the local signal, the received signal and the correlation signal based on the determined motion of the receiver and at least one hypothesis for an angle of arrival of the signal, determine whether the received signal is a diffracted signal based on a model and at least one characteristic of the received signal derived from at least one of the phase- compensated correlation signal, the at least one hypothesis for the angle of arrival, or an estimate for a current position of the receiver, and perform an action to improve signal reception at the receiver based on a determination that the received signal is a diffracted signal.

[0102] In some embodiments of the present principles, a system for detecting a diffracted signal includes at least one transmitter and a receiver including at least one processor and a memory accessible to the processor. In such embodiments, the memory has stored therein at least one of programs or instructions, which when executed by the processor configure the receiver to receive a signal at the receiverfrom the at least one transmitter, provide a local signal, determine a motion of the receiver, correlate the received signal with the local signal to calculate a correlation signal, calculate at least one phase-compensated correlation signal by providing phase compensation of at least one of the local signal, the received signal and the correlation signal based on the determined motion of the receiver and at least one hypothesis for an angle of arrival of the signal, determine whether the received signal is a diffracted signal based on a model and at least one characteristic of the received signal derived from at least one of the phase-compensated correlation signal, the at least one hypothesis for the angle of arrival, or an estimate for a current position of the receiver, and perform an action to improve signal reception at the receiver based on a determination that the received signal is a diffracted signal.

[0103] Those skilled in the art will appreciate that, while various items are illustrated as being stored in memory or on storage while being used, these items or portions of them can be transferred between memory and other storage devices for purposes of memory management and data integrity. Alternatively, in other embodiments some or all of the software components can execute in memory on another device and communicate with the illustrated computer system via inter-computer communication. Some or all of the system components or data structures can also be stored (e.g., as instructions or structured data) on a computer-accessible medium or a portable article to be read by an appropriate drive, various examples of which are described above. In some embodiments, instructions stored on a computer-accessible medium separate can be transmitted to a computing device via transmission media or signals such as electrical, electromagnetic, or digital signals, conveyed via a communication medium such as a network and / or a wireless link. Various embodiments can further include receiving, sending or storing instructions and / or data implemented in accordance with the foregoing description upon a computer-accessible medium or via a communication medium. In general, a computer-accessible medium can include a storage medium or memory medium such as magnetic or optical media, e.g., disk or DVD / CD-ROM, volatile or non-volatile media such as RAM (e.g., SDRAM, DDR, RDRAM, SRAM, and the like), ROM, and the like.

[0104] The methods and processes described herein may be implemented in software, hardware, or a combination thereof, in different embodiments. In addition,the order of methods can be changed, and various elements can be added, reordered, combined, omitted or otherwise modified. All examples described herein are presented in a non-limiting manner. Various modifications and changes can be made as would be obvious to a person skilled in the art having benefit of this disclosure. Realizations in accordance with embodiments have been described in the context of particular embodiments. These embodiments are meant to be illustrative and not limiting. Many variations, modifications, additions, and improvements are possible. Accordingly, plural instances can be provided for components described herein as a single instance. Boundaries between various components, operations and data stores are somewhat arbitrary, and particular operations are illustrated in the context of specific illustrative configurations. Other allocations of functionality are envisioned and can fall within the scope of claims that follow. Structures and functionality presented as discrete components in the example configurations can be implemented as a combined structure or component. These and other variations, modifications, additions, and improvements can fall within the scope of embodiments as defined in the claims that follow.

[0105] In the foregoing description, numerous specific details, examples, and scenarios are set forth in order to provide a more thorough understanding of the present disclosure. It will be appreciated, however, that embodiments of the disclosure can be practiced without such specific details. Further, such examples and scenarios are provided for illustration, and are not intended to limit the disclosure in any way. Those of ordinary skill in the art, with the included descriptions, should be able to implement appropriate functionality without undue experimentation.

[0106] References in the specification to “an embodiment,” etc., indicate that the embodiment described can include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is believed to be within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly indicated.

[0107] Embodiments in accordance with the disclosure can be implemented in hardware, firmware, software, or any combination thereof. Embodiments can also be implemented as instructions stored using one or more machine-readable media, which may be read and executed by one or more processors. A machine-readable medium can include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device or a “virtual machine” running on one or more computing devices). For example, a machine-readable medium can include any suitable form of volatile or non-volatile memory.

[0108] In addition, the various operations, processes, and methods disclosed herein can be embodied in a machine-readable medium and / or a machine accessible medium / storage device compatible with a data processing system (e.g., a computer system), and can be performed in any order (e.g., including using means for achieving the various operations). Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. In some embodiments, the machine-readable medium can be a non-transitory form of machine-readable medium / storage device.

[0109] Modules, data structures, and the like defined herein are defined as such for ease of discussion and are not intended to imply that any specific implementation details are required. For example, any of the described modules and / or data structures can be combined or divided into sub-modules, sub-processes or other units of computer code or data as can be required by a particular design or implementation.

[0110] In the drawings, specific arrangements or orderings of schematic elements can be shown for ease of description. However, the specific ordering or arrangement of such elements is not meant to imply that a particular order or sequence of processing, or separation of processes, is required in all embodiments. In general, schematic elements used to represent instruction blocks or modules can be implemented using any suitable form of machine-readable instruction, and each such instruction can be implemented using any suitable programming language, library, applicationprogramming interface (API), and / or other software development tools or frameworks. Similarly, schematic elements used to represent data or information can be implemented using any suitable electronic arrangement or data structure. Further,some connections, relationships or associations between elements can be simplified or not shown in the drawings so as not to obscure the disclosure.

[0111] While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.

Claims

CLAIMS:1 . A method performed at a receiver, comprising the steps of: receiving a signal at the receiver from a remote source; providing a local signal; determining a motion of the receiver; correlating the received signal with the local signal to calculate a correlation signal; calculating at least one phase-compensated correlation signal by providing phase compensation of at least one of the local signal, the received signal and the correlation signal based on the determined motion of the receiver and at least one hypothesis for an angle of arrival of the signal; determining whether the received signal is a diffracted signal based on a model and at least one characteristic of the received signal derived from at least one of the phase-compensated correlation signal, the at least one hypothesis for the angle of arrival, or an estimate for a current position of the receiver; and performing an action to improve signal reception at the receiver based on a determination that the received signal is a diffracted signal.

2. The method of claim 1 , wherein the calculating of the at least one phase- compensated correlation signal is performed using a SUPERCORRELATION™ technique.

3. The method of claim 1 , wherein the received signal is determined to be a diffracted signal if the at least one characteristic of the derived signal matches a known characteristic of a diffracted signal identified using the model.

4. The method of any of claims 1 , 2 and 3, wherein the model comprises at least one of a Fresnel diffraction model, a Uniform Theory of Diffraction (UTD) model, a building model, or a machine learning model.

5. The method of claim 4, wherein the machine learning model is pre-trained to determine if a received signal is a diffracted signal.

6. The method of any of claims 1 , 2 and 3, wherein the action comprises removing an identified diffracted signal from a calculation to determine a position of the receiver.

7. The method of any of claims 1 , 2 and 3, wherein the action comprises using characteristics of the diffracted signal along with a building model to determine a position of the receiver.

8. An apparatus for detecting a diffracted signal, comprising: at least one processor; and a memory accessible to the processor, the memory having stored therein at least one of programs or instructions executable by the processor to configure the apparatus to: receive a signal at the receiver from a remote source; provide a local signal; determine a motion of the receiver; correlate the received signal with the local signal to calculate a correlation signal; calculate at least one phase-compensated correlation signal by providing phase compensation of at least one of the local signal, the received signal and the correlation signal based on the determined motion of the receiver and at least one hypothesis for an angle of arrival of the signal; determine whether the received signal is a diffracted signal based on a model and at least one characteristic of the received signal derived from at least one of the phase-compensated correlation signal, the at least one hypothesis for the angle of arrival, or an estimate for a current position of the receiver; and perform an action to improve signal reception at the receiver based on a determination that the received signal is a diffracted signal.

9. The apparatus of claim 8, wherein the calculating of the at least one phase- compensated correlation signal is performed using a SUPERCORRELATION™ technique.

10. The apparatus of claim 8, wherein the received signal is determined to be a diffracted signal if the at least one characteristic of the derived signal matches a known characteristic of a diffracted signal identified using the model.11 . The apparatus of any of claims 8, 9, and 10, wherein the model comprises at least one of a Fresnel diffraction model, a Uniform Theory of Diffraction (UTD) model, a building model, or a machine learning model.

12. The apparatus of any of claims 8, 9, and 10, wherein the action comprises removing an identified diffracted signal from a calculation to determine a position of the receiver.

13. The apparatus of any of claims 8, 9, and 10, wherein the action comprises using characteristics of the diffracted signal along with a building model to determine a position of the receiver.

14. The apparatus of any of claims 8, 9, and 10, wherein the apparatus comprises at least one of a server remote from the receiver or an integrated component of the receiver.

15. A system for detecting a diffracted signal, comprising: at least one transmitter; a receiver, comprising: at least one processor; and a memory accessible to the processor, the memory having stored therein at least one of programs or instructions executable by the processor to configure the receiver to: receive a signal at the receiver from the at least one transmitter; provide a local signal; determine a motion of the receiver; correlate the received signal with the local signal to calculate a correlation signal;calculate at least one phase-compensated correlation signal by providing phase compensation of at least one of the local signal, the received signal and the correlation signal based on the determined motion of the receiver and at least one hypothesis for an angle of arrival of the signal; determine whether the received signal is a diffracted signal based on a model and at least one characteristic of the received signal derived from at least one of the phase-compensated correlation signal, the at least one hypothesis for the angle of arrival, or an estimate for a current position of the receiver; and perform an action to improve signal reception at the receiver based on a determination that the received signal is a diffracted signal.

16. The system of claim 15, wherein the calculating of the at least one phase- compensated correlation signal is performed using a SUPERCORRELATION™ technique.

17. The system of claim 15, wherein the received signal is determined to be a diffracted signal if the at least one characteristic of the derived signal matches a known characteristic of a diffracted signal identified using the model.

18. The system of any of claims 15, 16, and 17, wherein the model comprises at least one of a Fresnel diffraction model, a Uniform Theory of Diffraction (UTD) model, a building model, or a machine learning model.

19. The system of any of claims 15, 16, and 17, wherein the action comprises removing an identified diffracted signal from a calculation to determine a position of the receiver.

20. The system of any of claims 15, 16, and 17, wherein the action comprises using characteristics of the diffracted signal along with a building model to determine a position of the receiver.

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