System and method for angle of arrival (AOA) estimation

Abstract

Embodiments of a wireless communication system, an ultra wide band (UWB) system, and a method for wireless communication are described herein. In an embodiment, a wireless communication system includes a processor configured to obtain an angle of arrival (AoA) estimate from a wireless signal; performing a channel impulse response (CIR) analysis; and determining a confidence level for the AoA estimate based on the CIR analysis.

Description

Technical Field

[0001] Embodiments of wireless communication systems, ultra-wideband (UWB) systems, and methods for wireless communication are described herein. Background Art

[0002] Wireless communication techniques may be used to obtain angle and range information, which results in various applications requiring angle (e.g., angle of arrival (AoA)) and range information. However, when a communication system operates in a non-line-of-sight (NLoS) scenario or other multipath environment, the channel and signal phase between devices may experience dramatic changes and thus may produce less accurate AoA estimates. Therefore, there is a need for wireless communication techniques that can distinguish between AoA estimates, such as distinguishing between good and poor AoA estimates and / or distinguishing between accurate and poor AoA estimates. Summary of the invention

[0003] Embodiments of wireless communication systems, ultra-wideband (UWB) systems, and methods for wireless communication are described herein. In an embodiment, a wireless communication system includes a processor configured to obtain an angle of arrival (AoA) estimate from a wireless signal; perform a channel impulse response (CIR) analysis; and determine a confidence level of the AoA estimate based on the CIR analysis.

[0004] In an embodiment, the processor is further configured to perform CIR analysis to obtain multipath component information and phase difference of arrival (PDoA) variance information.

[0005] In an embodiment, the multipath component information includes confidence information of the rising edge of the complex CIR sample and confidence information of the main lobe width.

[0006] In an embodiment, the multipath component information further includes confidence information of the first valid index difference of the complex CIR samples.

[0007] In an embodiment, the multipath component information further includes confidence information of the first path index difference of the complex CIR samples.

[0008] In an embodiment, the multipath component information includes a combination of the confidence level of the rising edge of the complex CIR sample and the confidence level of the mainlobe width, the confidence level of the first valid index difference of the complex CIR sample, and the confidence level of the first path index difference of the complex CIR sample.

[0009] In an embodiment, the PDoA variance information includes signal-to-noise ratio (SNR) variance and PDoA drift information at the PDoA index of the complex CIR samples.

[0010] In an embodiment, the processor is further configured to analyze the complex CIR samples to obtain confidence information of rising edges of the complex CIR samples and confidence information of the main lobe width.

[0011] In an embodiment, the processor is further configured to obtain a PDoA slope value and PDoA ambiguity region information, and determine a confidence level of the AoA estimate based on the PDoA slope value and the PDoA ambiguity region information.

[0012] In an embodiment, the wireless signal comprises a UWB signal.

[0013] In an embodiment, the wireless communication system further includes a multi-antenna UWB receiver configured to receive the UWB signal.

[0014] In an embodiment, a UWB system includes: a multi-antenna UWB receiver arrangement configured to receive a UWB signal; a processor operably connected to the multi-antenna UWB receiver arrangement and configured to: obtain an angle of arrival (AoA) estimate from the UWB signal; perform a channel impulse response (CIR) analysis by analyzing complex CIR samples of a receiver of the multi-antenna UWB receiver arrangement; and determine a confidence level of the AoA estimate based on the CIR analysis; and a storage unit configured to store the AoA estimate and the confidence level of the AoA estimate.

[0015] In an embodiment, the processor is further configured to perform CIR analysis to obtain multipath component information and PDoA variance information.

[0016] In an embodiment, the multipath component information includes confidence information of rising edges of complex CIR samples of the multi-antenna UWB receiver arrangement and confidence information of the main lobe width.

[0017] In an embodiment, the multipath component information further comprises confidence information of the first valid index difference of the complex CIR samples of the multi-antenna UWB receiver arrangement.

[0018] In an embodiment, the multipath component information additionally contains confidence information of the first path index differences of the complex CIR samples of the multi-antenna UWB receiver arrangement.

[0019] In an embodiment, the PDoA variance information includes SNR ratio variance and PDoA drift information at the PDoA index of complex CIR samples of the multi-antenna UWB receiver arrangement.

[0020] In an embodiment, the processor is further configured to obtain a PDoA slope value and PDoA ambiguity area information, and determine a confidence level of the AoA estimate based on the CIR analysis, the PDoA slope value and the PDoA ambiguity area information.

[0021] In an embodiment, a method for wireless communication involves: obtaining an AoA estimate from a wireless signal; performing a CIR analysis; and determining a confidence level of the AoA estimate based on the CIR analysis.

[0022] Other aspects and advantages of the embodiments of the present invention will become apparent from the following detailed description in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 An embodiment of a UWB communication system according to an embodiment of the present invention is depicted.

[0024] Figure 2A Depiction Figure 1 The line-of-sight (LoS) communication scenario of the UWB communication system depicted in FIG.

[0025] Figure 2B Depiction Figure 1 The multipath communication scenario of the UWB communication system depicted in FIG.

[0026] Figure 3 Depiction Figure 1 An embodiment of a UWB system of a UWB communication system depicted in FIG.

[0027] Figure 4 Describing can be done by Figure 3 The FoM algorithm used by the UWB system depicted in FIG.

[0028] Figure 5 Depiction Figure 3 The clean channel impulse response (CIR) amplitude and phase of the LoS wireless communication channel of the UWB system depicted in FIG.

[0029] Figure 6 Depiction Figure 3 Multipath CIR magnitude and phase for the UWB system depicted in FIG.

[0030] Figure 7 Some examples of plotting CIR magnitude and phase and PDoA curves in LoS.

[0031] Figure 8 Some examples of plotting CIR magnitude and phase and PDoA curves in NLoS.

[0032] Fig. 9 Example of plotting a PDoA curve.

[0033] Fig.10 Depicting some examples of ambiguous regions in PDA curves.

[0034] Fig.11The present invention is a flowchart of a method for wireless communication according to an embodiment of the present invention.

[0035] Throughout the description, similar reference numerals may be used to identify similar elements. DETAILED DESCRIPTION

[0036] It will be readily appreciated that the components of the embodiments as generally described herein and illustrated in the accompanying drawings may be arranged and designed in a variety of different configurations. Therefore, the following more detailed description of the various embodiments represented in the figures is not intended to limit the scope of the present disclosure, but is merely representative of the various embodiments. Although various aspects of the embodiments are presented in the drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0037] The present invention may be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. The described embodiments should be considered in all respects to be illustrative only and not restrictive. Therefore, the scope of the present invention is indicated by the appended claims rather than by the specific embodiments. All changes that appear in the equivalent meanings and scopes of the claims will be included within the scope of the claims.

[0038] References throughout this specification to features, advantages, or similar language do not imply that all features and advantages that may be achieved with the present invention should be or are in any single embodiment of the present invention. Rather, language referring to features and advantages should be understood to mean that a specific feature, advantage, or characteristic described in connection with an embodiment is included in at least one embodiment of the present invention. Thus, discussions of features and advantages and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0039] In addition, the described features, advantages and characteristics of the present invention can be combined in one or more embodiments in any suitable manner. Those skilled in the art will recognize that, in view of the description herein, the present invention can be practiced without one or more specific features or advantages of a particular embodiment. In other cases, additional features and advantages that may not be present in all embodiments of the present invention may be recognized in certain embodiments.

[0040] Reference throughout this specification to "one embodiment," "an embodiment," or similar language means that a particular feature, structure, or characteristic described in connection with the indicated embodiment is included in at least one embodiment of the present invention. Thus, the phrases "in one embodiment," "in an embodiment," and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0041] Ultra-wideband (UWB) refers to a wireless communication technology in which wireless signals are transmitted over a wide-band spectrum. In some cases, UWB systems operate in a frequency range of 3 kHz to 300 GHz.

[0042] Figure 1 An embodiment of a UWB communication system 100 according to an embodiment of the present invention is depicted. Figure 1 In the embodiment depicted in FIG. 1 , the UWB communication system 100 includes a plurality of UWB systems 102-1, ..., 102-N, where N is a positive integer greater than one. The UWB systems 102-1, ..., 102-N communicate with each other via UWB signals. The UWB system may have extensive mobility and portability. In an embodiment, at least one UWB system is a handheld mobile device, such as a cellular phone, a smart phone, a personal digital assistant (PDA), an enterprise digital assistant (EDA), a handheld gaming device, or a wearable device. In another embodiment, at least one UWB system is a portable computing device, such as a laptop computer, a netbook, or a tablet computer. Figure 1 The UWB communication system 100 depicted in the can be used in various applications, such as communication and indoor positioning applications (e.g., in parking lots, shopping malls, airports, train stations, and hospitals). For example, highly accurate angle and range information of devices can be achieved using the large bandwidth provided by UWB technology, which creates a variety of applications that require angle (e.g., AoA) and range information in device-to-device communication, smart homes, smart retail, and smart enterprises, etc. As new applications emerge, the need to have accurate angle and range information becomes more and more important.

[0043] Figure 2A Depiction Figure 1 FIG. 2 is a line-of-sight (LoS) communication scenario 200 of the UWB communication system 100 depicted in FIG. Figure 2A In the LoS communication scenario 200 depicted therein, the UWB system 102-1 communicates with the UWB system 102-2 in LoS.

[0044] Figure 2B Depiction Figure 1 The multipath communication scenario 280 of the UWB communication system 100 depicted in FIG. Figure 2B, the UWB system 102-1 communicates with the UWB system 102-2 in both LoS and NLoS. Specifically, the reflector 264 may reflect the UWB signal from the UWB system 102-1 to the UWB system 102-2, and vice versa. The reflector 264 may be an object surface, such as a frequency selective surface (FSS), which is a repeating surface configured to reflect, transmit, or absorb electromagnetic fields on a frequency basis. The UWB system 102-1 may communicate directly with the UWB system 102-2 in LoS without reflection. In some embodiments, the UWB system 102-1 communicates with the UWB system 102-2 only in NLoS, and does not communicate directly with the UWB system 102-2 in LoS without reflection. When the UWB systems 102-1, 102-2 operate in an NLoS scenario or a multipath rich environment, the communication channel and signal phase between the UWB systems 102-1, 102-2 may experience dramatic changes, and thus may produce less accurate AoA estimates. In some embodiments, the UWB systems 102-1, 102-2 distinguish between good / accurate AoA estimates and poor / imprecise AoA estimates. The UWB systems 102-1, 102-2 may distinguish between good / accurate AoA estimates and poor / imprecise AoA estimates by having a confidence level about each AoA estimate. In some embodiments, at least one of the UWB systems 102-1, 102-2 uses a figure of merit (FoM) algorithm to evaluate communication channel conditions and antenna characteristic curves, and provide a confidence level about the AoA estimate. For example, at least one of the UWB systems 102-1, 102-2 uses the FoM algorithm to identify poor / imprecise AoA estimates (e.g., AoA estimates with lower AoA FoM confidence levels) and filters out the poor / imprecise AoA estimates, thereby improving the accuracy of the average AoA estimate. Each AoA estimate may have an AoA FoM confidence level between zero and one, with one being the highest confidence level. By combining the FoM algorithm with the AoA algorithm, the reliability of the AoA result is improved.

[0045] Figure 3 Depiction Figure 1 An embodiment of the UWB systems 102-1, ..., 102-N of the UWB communication system 100 is depicted in FIG. Figure 3 In the embodiment depicted in FIG. 3 , UWB system 302 includes a dual-antenna UWB receiver arrangement or array 322 , a processor 324 operatively connected to the dual-antenna receiver, and a memory unit 326 for storing information related to UWB system 302 . Figure 3 The UWB system 302 depicted in FIG. Figure 1One possible embodiment of the UWB systems 102-1, ..., 102-N depicted in FIG. However, Figure 1 The UWB systems 102-1, . . . , 102-N depicted in FIG. Figure 3 . The UWB system 302 may be implemented in hardware, firmware, and / or a combination of hardware, software, and / or firmware. In an embodiment, the UWB system 302 or components of the UWB system 302 are packaged as independent integrated circuit (IC) chips. For example, the dual antenna UWB receiver arrangement 322 may be implemented in at least one substrate, such as at least one semiconductor wafer. In an embodiment, the dual antenna UWB receiver arrangement is packaged as an independent IC chip. In another example, the processor 324 may be implemented in at least one substrate, such as at least one semiconductor wafer. In an embodiment, the processor 324 is packaged as an independent IC chip.

[0046] exist Figure 3 In the embodiment depicted in , the dual antenna UWB receiver arrangement 322 includes a first antenna 334-1, a second antenna 334-2, and an RF modem 338 configured to process (eg, decode) UWB signals received from the first and second antennas. Figure 3 In the embodiment depicted in , the RF modem 338 includes two receivers RX0, RX1 connected to the first antenna 334-1 and the second antenna 334-2, respectively, to process (e.g., decode) the UWB signals received from the first and second antennas. In some embodiments, the dual-antenna UWB receiver arrangement 322 also acts as a transmitter, and the RF modem 338 is configured to decode the UWB signals received from the first and second antennas and to encode the UWB signals to be transmitted by the first and second antennas. In some embodiments, the dual-antenna UWB receiver arrangement 322 includes at least one switch, and the at least one switch is configured to switch the first antenna and / or the second antenna between transmitting outgoing UWB signals and receiving incoming UWB signals. In some embodiments, the dual-antenna UWB receiver only has the ability to receive UWB signals and does not include one or more switches to switch the first antenna and / or the second antenna between transmitting outgoing UWB signals and receiving incoming UWB signals. Although the UWB system 302 is in Figure 3 302 as including two antennas 334-1, 334-2, but in other embodiments, the UWB system 302 includes more than two antennas. Figure 3 3. The UWB system 302 is shown as including two receivers RX0, RX1, but in other embodiments, the UWB system 302 includes more than two receivers.

[0047] exist Figure 3, the processor 324 is configured to determine angle of arrival (AoA) information from UWB signals received from two antennas 334-1, 334-2, and distinguish between good / accurate AoA estimates and poor / imprecise AoA estimates. In some cases, the AoA information relates to the angle between the corresponding USB system with antenna 354 and the UWB system 302 relative to a reference line (e.g., a horizontal line or a vertical line). In some embodiments, the processor distinguishes between good / accurate AoA estimates and poor / imprecise AoA estimates by having a confidence level about each AoA estimate. In some embodiments, the processor uses a figure of merit (FoM) algorithm that may be stored in the storage unit 326 to evaluate the wireless communication channel conditions and antenna characteristic curves of the UWB system 302, and provides confidence levels about the AoA estimates. For example, the processor uses the FoM algorithm to identify poor / inaccurate AoA estimates (e.g., AoA estimates with lower AoA FoM confidence levels), and filters out the poor / inaccurate AoA estimates, and thereby improves the average AoA estimate accuracy. In some embodiments, each AoA estimate has an AoA FoM confidence level presented as a number from zero to one, with zero being the lowest confidence level and one being the highest confidence level. In some embodiments, the confidence levels may be represented by different numerical ranges, such as a numerical range between one and one hundred, but other numerical representations of confidence levels are possible. In some embodiments, each AoA estimate has a confidence level presented as a non-numeric scale. For example, the AoA estimate may have a confidence level represented as high, medium, or low. In other embodiments, the confidence levels may be characterized in some other manner, such as by color (e.g., green for high confidence, yellow for medium confidence, and red for low confidence). By combining the FoM algorithm and the AoA algorithm, the reliability of the AoA result is improved. In some embodiments, the processor is a microcontroller, a digital signal processor (DSP), or a central processing unit (CPU). In some embodiments, the processor includes a DSP processor configured to execute the FoM algorithm and a hardware processor configured to calculate the final FoM result (e.g., a reduced instruction set computer (RISC) processor, such as an advanced RISC machine (ARM) processor). In an example operation of the UWB system 302, the angle θ1 between the corresponding UWB system having the antenna 354 and the UWB system 302 relative to the reference horizontal line 380 is estimated by the processor based on the difference between the time when the UWB radio pulse from the antenna 354 of the corresponding UWB system arrives at the first antenna 334-1 of the dual-antenna UWB receiver and the time when the UWB radio pulse from the antenna 354 of the corresponding UWB system arrives at the second antenna 334-2 of the dual-antenna UWB receiver.

[0048] In some embodiments, the processor 324 is configured to obtain an AoA estimate from the UWB signal received at the first antenna 334-1 and the second antenna 334-2, and determine the confidence level of the AoA estimate based on the FoM algorithm. In some embodiments, the processor 324 obtains the AoA estimate by calculating, generating and / or producing the AoA estimate. In other embodiments, the processor 324 obtains the AoA estimate by receiving or requesting the AoA estimate from other sources (e.g., RF modem 338). In some embodiments, the processor is further configured to perform a channel impulse response (CIR) analysis by analyzing the complex CIR samples of the receivers RX0, RX1 of the dual antenna UWB receiver arrangement 322, and determine the confidence level of the AoA estimate based on the CIR analysis. In some embodiments, the processor is further configured to perform a CIR analysis to obtain multipath component information and arrival phase difference (PDoA) variance information. In some embodiments, the multipath component information includes confidence information of the rising edge of the complex CIR sample of the dual antenna UWB receiver arrangement 322 and confidence information of the main lobe width. In some embodiments, the multipath component information further includes confidence information of the first effective index difference of the complex CIR sample of the dual antenna UWB receiver arrangement 322. In some embodiments, the multipath component information further includes confidence information of the first path index difference of the complex CIR sample of the dual antenna UWB receiver arrangement 322. In some embodiments, the multipath component information includes a combination of the confidence level of the rising edge of the complex CIR sample and the confidence level of the main lobe width, the confidence level of the first effective index difference of the complex CIR sample, and the confidence level of the first path index difference of the complex CIR sample. In some embodiments, the PDoA variance information includes signal-to-noise ratio (SNR) variance at the PDoA index of the complex CIR sample and PDoA drift information.

[0049] exist Figure 3 In the embodiment depicted in , the storage unit 326 is configured to store information related to the UWB system 302 (e.g., communication information and / or location information of the UWB system 302). In some embodiments, the storage unit stores the FoM algorithm and the AoA estimate and the FoM information of the AoA estimate. The storage unit may be implemented as a read-only memory (ROM), a flash memory, a random access memory (RAM), a cache, or other types of storage devices. Although the illustrated storage unit is shown as being separate from the processor, in some embodiments, the storage unit is implemented within the processor.

[0050] In some embodiments, the FoM algorithm used by the UWB system 302 (e.g., processor 324) takes into account the results of the AoA algorithm (e.g., AoA estimates). In addition, the FoM algorithm may take into account the calibration of the PDoA curve. In addition, the FoM algorithm may also take into account the channel impulse response (CIR) at each receiver RX0 or RX1, which provides information about the signal-to-noise ratio (SNR) and signal phase. The FoM algorithm may combine the above information to rate each sample as good / accurate or poor / imprecise (e.g., based on a predefined threshold) at each receive antenna, and thus provide a confidence level based on the above three metrics.

[0051] Figure 4 Describing can be done by Figure 3 The FoM algorithm 400 used by the UWB system 302 depicted in FIG. For example, the FoM algorithm 400 may be stored in the storage unit 326 and executed by the processor 324. Figure 4 , the FoM algorithm 400 includes a CIR analysis stage 402 that considers CIR analysis information, which includes multipath component information and PDoA variance information. In addition, the FoM algorithm includes an AoA stage 404 that considers information from an AoA algorithm, which includes calibration of the PDoA curve and AoA estimates for a corresponding antenna pair (e.g., the first antenna 334-1 and the second antenna 334-2 of the UWB system 302). The FoM algorithm can combine the above information at a combination stage 406 to provide a confidence level 408 for the AoA estimate.

[0052] In the CIR analysis phase 402, complex CIR samples (e.g., CIR0 for receiver RX0 and CIR1 for receiver RX1 of the UWB system 302) may be used to obtain amplitude and phase information. In some embodiments, the CIR analysis phase is a signal quality check phase. In the CIR analysis phase, the obtained CIR samples are evaluated to determine whether the signal quality is good or poor.

[0053] Multipath component information (MPC):

[0054] In the CIR analysis stage 402 , multipath component information analysis may be used, for example, by the processor 324 to determine problematic multipath scenarios that may produce large AoA errors due to multipath effects in the wireless communication channel of the UWB system 302 . Figure 5 Depiction Figure 3 The clear CIR amplitude and phase of the LoS wireless communication channel of the UWB system 302 depicted in FIG. Figure 6 Depiction Figure 3 The multipath CIR amplitude and phase of the UWB system 302 depicted in FIG.

[0055] I. Pulse characteristics:

[0056] In some embodiments, the pulse characteristics are obtained, for example, by processor 324. Each receiver has an associated CIR. Based on the pulse being used for communication between UWB systems, the main lobe width and rising edge of the pulse are known. For example, Figure 5 In the figure, the main lobe width Δt (n) m is about 3.3 nanoseconds (ns) and the rising edge Δt (n) re is approximately 2.5 nanoseconds (ns). However, in problematic multipath scenarios, the mainlobe width and rising edge deviate from these numbers for CIR1, which can be seen in Figure 6 As can be seen in Figure 1, the main lobe for CIR0 is between 767ns and 770ns, but the main lobe for CIR1 is between 765ns and 767ns. The confidence of the rising edge that can be calculated based on CIR can be expressed as:

[0057]

[0058] and 0≤RisingEdgeConf≤1, where N is the number of CIRs, and

[0059] RisingEdgeDeviation n =abs(RisingEdgeRef-RisingEdge n ). (2)

[0060] In the example, based on the pulse shape used, N=2 and RisingEdgeRef=2.5. Figure 5 (LoS case), for both CIR0 and CIR1, RisingEdge = 2.5. Therefore, RisingEdgeConf = 1. However, in Figure 6 In the NLoS case, the RisingEdge of CIR1 deviates greatly from the RisingEdgeRef, which causes the RisingEdgeConf to be closer to 0. The confidence of the main lobe can be calculated based on the CIR as:

[0061]

[0062] and 0≤MainLobeConf≤1, where N is the number of CIRs, and

[0063] MainLobeDeviation n =abs(MainLobeWidthRef-MainLobeWidth n ). (4)

[0064] For this example, MainLobeWidthRef may be 3.3ns.

[0065] II. First valid index difference:

[0066] In some embodiments, the first valid index difference is obtained, for example, by the processor 324. In these embodiments, the first valid index is the first index where the CIR sample magnitude is above a predefined threshold. In some embodiments, the predefined threshold is determined based on a noise variance calculation and is referred to as a noise variance threshold. The first valid index may be determined for two receivers (e.g., two receivers RX0, RX1 of the UWB system 302) using the corresponding CIR. In some embodiments, if this first valid index difference between the two receivers is greater than a predetermined threshold, it indicates the presence of a problematic multipath component. For example, in Figure 5 (LoS scenario), the first valid index of the two CIRs is the same index, i.e., 833ns. Figure 6 (NLoS case), the first valid index of CIR0 is 767ns, and the first valid index of CIR1 is 766ns, which indicates a problematic multipath environment.

[0067] FirstValidIndexConf=1-min(FirstValidDiff / FirstValidIdxThr,1) (5)

[0068] and 0≤FirstValidIdxConf≤1,

[0069] FirstValidDiff = abs(FirstValidIdx cir0 -FirstValidIdx cir1 ) (6)

[0070] If FirstValidIdxThr = 3, then for Figure 6 (NLoS case), FirstValidIndexConf=1-min(1 / 3,1).

[0071] III. First path index difference:

[0072] In some embodiments, the first path index difference is obtained, for example, by the processor 324. In these embodiments, the first path index is the index of the peak of the main lobe of the pulse in the CIR. In some embodiments, if the first path index difference of two receivers (e.g., the two receivers RX0, RX1 of the UWB system 302) is above a predetermined threshold, then this indicates a problematic multipath situation. For example, in Figure 5In , the estimated first path of both CIRs is 834.6ns. Figure 6 , the estimated first path index of CIR0 is 769, while the value of the estimated first path index of CIR1 is 766. This indicates that the estimated values ​​of the two CIRs are 3 ns apart, indicating a problematic multipath environment.

[0073] FirstPathIdxConf = 1 - min(FirstPathIdxDiff / FirstPathIdxThr,1) (7)

[0074] and 0≤FirstPathIdxConf≤1

[0075] FirstPathIdxDiff = abs(FirstPathIdx cir0 -FirstPathIdx cir1 ) (8)

[0076] If FirstPathIdxThr = 2, then for Figure 6 (NLoS case), FirstPathIdxConf=1-min(3 / 2,1).

[0077] The multipath component information may be obtained, for example, by the processor 324, which is a combination of the confidence level of the CIR rising edge, the confidence level of the CIR main lobe width, the confidence level of the first effective index difference, and the confidence level of the first path index difference. In some embodiments, the multipath component information MPC FOM may be expressed as:

[0078] MPC FOM=α1RisingEdgeConf+α2MainLobeConf+

[0079] α3FirstValidIdxConf+α4FirstPathIdxConf(9)

[0080] Among them, α1+α2+α3+α4=1.

[0081] Based on the above four factors, the multipath component information has a value between zero and one, where zero indicates the worst multipath environment and one indicates a clear LoS environment. This value is further integrated into the PDoA variance calculation.

[0082] PDoA variance information:

[0083] In some embodiments, the PDoA variance information is obtained, for example, by the processor 324. In some embodiments, the PDoA variance information mainly depends on two factors: the signal-to-noise ratio variance at the PDoA index of the complex CIR samples of the multi-antenna UWB receiver arrangement (e.g., the dual-antenna UWB receiver arrangement 322) and the PDoA drift information.

[0084] I. SNR variance at PDoA index:

[0085] In some embodiments, the phase difference between the CIR samples (CIR) of the two receivers is calculated for the rising edge of the pulse. The SNR at the PDoA index of the two CIRs provides a good indication of the wireless communication channel as seen by the two receivers (e.g., the two receivers RX0, RX1 of the UWB system 302). When the wireless communication channel between the UWB systems is line-of-sight (LoS), the SNR at the PDoA index of the two CIRs is high and the same. Problematic multipath scenarios may cause at least one CIR to be distorted and thus produce a low SNR. In some embodiments, the SNR variance at the PDoA index may be given as:

[0086]

[0087] II.PDoA drift:

[0088] In some embodiments, the PDoA drift is the phase variation on indices around the PDoA index. In problematic multipath scenarios, the phase variation on consecutive samples is high, and the high variation may lead to incorrect PDoA estimates and result in high AoA errors. Therefore, the PDoA drift may help identify such scenarios where the results are definitely incorrect. In some embodiments, the PDoA drift is given by the following formula:

[0089]

[0090] Among them, Δθ PDOA_IDX =θ[PDOA_IDX,CIR1]-θ[PDOA_IDX,CIR0] (12)

[0091] Δθ PDOA_IDX-1 =θ[PDOA_IDX-1,CIR1]-θ[PDOA_IDX-1,CIR0] (13)

[0092] Therefore, the total PDoA variance is given as the sum of the SNR at the PDoA index and the PDoA drift.

[0093] σ 2 PDOA =σ 2 SNR +φ2 漂移 (14)

[0094] As follows respectively Figure 7 and Figure 8 Some examples of CIR are shown in . Specifically, Figure 7 Some examples of plotting CIR magnitude and phase and PDoA curves in LoS. Since the CIR magnitude is high, the PDoA SNR is high and the phase is flat around the PDoA index. Figure 8 Some examples of plotting CIR magnitude and phase and PDoA curves in NLoS. Since the CIR magnitude is low, the PDoA SNR is low and the phase variation is large around the PDoA index.

[0095] Information from the AoA algorithm:

[0096] In the AoA stage 404, a calibrated PDoA curve and an AoA estimate for the antenna pair are obtained, for example, by the processor 324. The AoA estimate is the result of the AoA algorithm, which is also used as an input to the FoM algorithm.

[0097] In some embodiments, the PDoA curve for a given device depends on antenna characteristics, which are collected in offline mode by sweeping through azimuth angles from -60 degrees to 60 degrees in steps of 12 degrees. The AoA algorithm uses these calibration curves to get the final AoA result. These curves may also have two possible error areas based on the antenna characteristics, which are the slope of the PDoA curve at a given point and the ambiguity area in the PDoA curve.

[0098] In some embodiments, when sweeping across a UWB system (e.g., UWB system 302) to collect a characteristic PDoA curve at each point, the PDoA value is constantly changing. However, the rate of change of the PDoA may not be uniform, i.e., the PDoA curve between two azimuth angles may change independently of the change between any other two points. Fig. 9 An example of a PDoA curve 900 is depicted. For example, the rate of change of the PDoA value from 48 degrees to 60 degrees is less than the rate of change of the PDoA value from 0 degrees to 12 degrees. Fig. 9 As shown, since the change in PDoA between 48 degrees azimuth and 60 degrees azimuth is small, a small error in PDoA will cause the AoA azimuth result to be in a new region. Therefore, these regions where the PDoA changes gradually are areas that can potentially cause high AoA estimation errors and need to be considered to provide confidence level calculations.

[0099] In some embodiments, there are ambiguous regions in the PDoA curve that may result in multiple AoA values ​​for the same PDoA, which may lead to ambiguity in the AoA estimate due to its large AoA error. Fig.10 Depict some examples of ambiguous regions in the PDA curve 1000. Fig.10 As shown, regions 1010, 1020 may be ambiguous regions. For example, a PDoA value of 50 degrees may have two possible AoA estimates, one between -54 degrees and -48 degrees and the other between -48 degrees and -42 degrees. In this case, the AoA values ​​are derived based on probability. If there are n = 0, 1, 2, N such ambiguous regions, then their probabilities are the inverse of their slope values, i.e.,

[0100]

[0101] Each possible AoA value can be multiplied by its associated probability and added together, ie,

[0102] Some or all of the factors described above may be combined to obtain the FoM confidence level, for example, by the processor 324. For example, the following parameters may be combined to obtain the FoM confidence level:

[0103] a) MPC component: a value between zero and one (if zero, the value is reset to 0.01);

[0104] b) PDoA variance;

[0105] c)PDoA slope value;

[0106] d) PDoA does not specify the region information.

[0107] Assuming that the AoA error follows a normal distribution around the mean, the first three factors a), b), c) can be combined to obtain the standard deviation, and the fourth factor is the bias.

[0108] Standard deviation = sqrt(((1 / MPC)*PDoA variance)) / (β*PDoA slope) (16) Deviation = abs(AoA estimate - AoAValue), where β is a tunable constant. (17)

[0109] Using the obtained standard deviation and deviation, the final confidence interval value can be obtained by passing these values ​​to a normal distribution lookup table or any numerical approximation based on the CDF. This approximation can be designed to obtain confidence intervals of + / -3 degrees, + / -5 degrees. Each sample can have a FoM value between 0 and 1. A cutoff value can be used to filter out samples. For example, all samples with FoM>=0.9 can be rated as good samples, and the AoA estimate can be used with high confidence. All samples with FoM<0.9 are rejected as bad samples. In some embodiments, the FoM algorithm is executed in a DSP processor, and a hardware processor (e.g., an ARM processor) calculates the final FoM result.

[0110] In some embodiments, the FoM algorithm 400 is tested in a wireless AoA setting with two UWB ranging devices, which are placed, for example, at a certain distance from each other (e.g., about two meters). One UWB device acts as an initiator and the other UWB device acts as a responder. The initiator reports the AoA and the corresponding FoM result. The FoM results can be obtained by post-processing the AoA results obtained from the automated setting. For example, the initiator can be connected to a dual robot, where one robot performs an azimuth scan and the other robot performs an elevation scan. AoA estimates can be obtained by scanning different azimuth and elevation combinations, and the PDOA can be post-processed by the FoM algorithm to obtain confidence level results for each sample. Compared to the LoS scenario, in the NLoS scenario, due to the multipath effect, the FoM algorithm evaluates fewer good / accurate samples and the FoM algorithm evaluates more bad / imprecise samples.

[0111] Fig.11 1 is a process flow chart of a method for wireless communication according to an embodiment of the present invention. At block 1102, an AoA estimate is obtained from a wireless signal. At block 1104, a CIR analysis is performed. At block 1106, a confidence level of the AoA estimate is determined based on the CIR analysis.

[0112] Although the operations of the methods are shown and described in a particular order herein, the order of the operations of each method may be changed so that a particular operation can be performed in reverse order, or so that a particular operation can be performed at least partially simultaneously with other operations. In another embodiment, instructions or sub-operations of different operations can be implemented in an intermittent and / or alternating manner.

[0113] It should also be noted that at least some operations of the method can be implemented using software instructions stored on a computer-usable storage medium that are executed by a computer. For example, an embodiment of a computer program product includes a computer-usable storage medium for storing a computer-readable program that, when executed on a computer, causes the computer to perform operations as described herein.

[0114] A computer usable or computer readable medium may be an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system (or apparatus or device), or a propagation medium. Examples of computer readable media include semiconductor or solid-state memory, magnetic tape, removable computer disk, random access memory (RAM), read-only memory (ROM), hard disk, and optical disk. Current examples of optical disks include compact disk with read only memory (CD-ROM), compact disk with read / write (CD-R / W), digital video disk (DVD), and Blu-ray disk.

[0115] In the above description, specific details of various embodiments are provided. However, some embodiments may be implemented without all of the details of these specific details. In other cases, for the sake of brevity and clarity, certain methods, operating steps, components, structures and / or functions are described in detail only to enable various embodiments of the present invention.

[0116] Although specific embodiments of the invention have been described and illustrated, the invention is not to be limited to the specific forms or arrangements of parts so described and illustrated. The scope of the invention should be defined by the claims appended hereto and their equivalents.

Claims

1. A wireless communication system, characterized in that: include: A processor configured to: Obtaining angle of arrival (AoA) estimates from multiple wireless signals; performing a channel impulse response (CIR) analysis; and A confidence level of the AoA estimate is determined based on the CIR analysis.

2. The wireless communication system according to claim 1, characterized in that: The processor is further configured to perform the CIR analysis to obtain multipath component information and phase difference of arrival (PDoA) variance information.

3. The wireless communication system according to claim 2, characterized in that: The multipath component information includes confidence information of rising edges of a plurality of complex CIR samples and confidence information of main lobe width.

4. The wireless communication system according to claim 3, characterized in that: The multipath component information further includes confidence information of the first effective index difference of the complex CIR sample.

5. The wireless communication system according to claim 2, characterized in that: The multipath component information includes a combination of the confidence levels of rising edges and main lobe widths of multiple complex CIR samples, the confidence levels of first valid index differences of the complex CIR samples, and the confidence levels of first path index differences of the complex CIR samples.

6. The wireless communication system according to claim 2, characterized in that: The PDoA variance information includes signal-to-noise ratio (SNR) variance and PDoA drift information at PDoA indexes of multiple complex CIR samples.

7. The wireless communication system according to claim 1, characterized in that: The processor is further configured to analyze a plurality of complex CIR samples to obtain confidence information of rising edges of the complex CIR samples and confidence information of main lobe widths.

8. The wireless communication system according to claim 1, characterized in that: The processor is further configured to obtain a phase difference of arrival (PDoA) slope value and PDoA ambiguity region information, and determine the confidence level of the AoA estimate based on the PDoA slope value and the PDoA ambiguity region information.

9. An ultra-wideband (UWB) system, characterized in that: include: a multi-antenna UWB receiver arrangement configured to receive a plurality of UWB signals; a processor operably connected to the multi-antenna UWB receiver arrangement, wherein the processor is configured to: obtaining an angle of arrival (AoA) estimate from the UWB signal; performing a channel impulse response (CIR) analysis by analyzing a plurality of complex CIR samples of a plurality of receivers of the multi-antenna UWB receiver arrangement; as well as determining a confidence level of the AoA estimate based on the CIR analysis; as well as A storage unit configured to store the AoA estimate and the confidence level of the AoA estimate.

10. A method for wireless communication, characterized in that: include: Obtaining angle of arrival (AoA) estimates from multiple wireless signals; Perform channel impulse response (CIR) analysis; as well as A confidence level of the AoA estimate is determined based on the CIR analysis.

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