Distributed sensing with ultra wide band radio

Abstract

A distributed ultra wide band (UWB) radar system and a method for operating the same are disclosed. A distributed radar system uses multiple separate UWB radios or nodes, which do not use a centralized clock or time source. However, the distributed UWB system operates to provide radar-like functionality and fineness sensing capabilities through synchronization based on line-of-sight (LOS) signal processing and noise estimation. Synchronized channel impulse responses (CIRs) may be processed for general object detection and tracking, gesture recognition, or even micro-motion, such as monitoring vital signs.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to distributed ultra-wideband (UWB) detection techniques. More specifically, the architecture employs a unique synchronization strategy for synchronizing and aligning channel impulse responses (CIRs) transmitted between separate or distributed UWB radios, despite not having a centralized timing source, to provide radar-like functionality and fine-grained sensing. BACKGROUND

[0002] Conventionally, object detection and tracking is implemented with monostatic systems, such as traditional radar systems. These systems have a centralized clock / timing source. Such a limitation generally leads to centralized or localized systems. SUMMARY

[0003] A detection method is provided, including transmitting a UWB signal from a first (designated) transmitter node, receiving the UWB signal at a second separate receiving node, extracting one or more channel impulse responses (CIRs) from the UWB signal, synchronizing the multiple CIRs to provide a series of synchronized CIRs, and processing the synchronized CIRs to detect an object or its activity (e.g., movement). The synchronization can include upsampling each CIR, aligning the multiple CIRs, estimating a phase noise, and canceling the phase noise from each received CIR. The phase noise can be based on a phase of a line-of-sight path signal. For example, the phase of a received line-of-sight signal can be compared to a phase of a transmitted signal.

[0004] A method of synchronizing multiple ultra-wideband (UWB) nodes to implement radar-like functionality is also provided. The method includes upsampling each of multiple channel impulse responses (CIRs) from a UWB signal, employing a lead edge detection algorithm, and aligning the multiple CIRs based on the lead edge, determining a phase of a line-of-sight path signal for each CIR, and canceling phase errors by subtracting the phase of the line-of-sight signal from each subsequent path signal.

[0005] A distributed ultra-wideband (UWB) radar system is provided. The system includes a transmitting node and a receiving node. The transmitting node is operable to transmit a UWB signal. The receiving node is operable to receive the UWB signal, extract multiple channel impulse responses (CIRs) from the UWB signal, synchronize the multiple CIRs to provide a series of synchronized CIRs, process the synchronized CIRs to detect an object or movement, and in response to detecting the object or movement, initiate a corresponding alert or perform a task based on the object or movement. The synchronization can include upsampling each CIR, aligning the multiple CIRs, estimating a phase error based on a phase of a line-of-sight path, and canceling the phase error. BRIEF DESCRIPTION OF DRAWINGS

[0006] Figure 1Ais a schematic view of an embodiment of a monostable architecture.

[0007] Figure 1B is a schematic view of an embodiment of a bistable architecture.

[0008] Figure 1C is a schematic view of an embodiment of a multi-stable environment.

[0009] Figure 2A is a schematic view of an embodiment of a monostable system.

[0010] Figure 2B is a schematic view of an embodiment of a bistable system.

[0011] Figure 3A is a perspective view of an experimental embodiment of a distributed system.

[0012] Figure 3B is a graph illustrating the CIR taps extracted from the UWB signal received by the distributed system of Figure 3A .

[0013] Figure 3C is a phase graph illustrating the CIR taps extracted from the UWB signal received by the distributed system of Figure 3A .

[0014] Figure 4A is a schematic view of an embodiment of a bistable system with line-of-sight (LOS) and non-line-of-sight paths (NLOS) of UWB signals.

[0015] Figure 4B is a time graph of CIR taps received from different paths of the embodiment of Figure 4A .

[0016] Figure 5 is a schematic illustration of an embodiment of a multi-stable system.

[0017] Figure 6 is a flowchart illustrating the operation of an embodiment of a distributed UWB detection system.

[0018] Figures 7A-7C illustrates a phase graph analysis of breathing using DW1000 UWB radios or nodes.

[0019] Figure 8 illustrates a micro-Doppler graph of a person walking back and forth.

[0020] Figure 9 is a flowchart illustrating a detection method. DETAILED DESCRIPTION

[0021] Embodiments of the present disclosure are described herein. It is to be understood, however, that the disclosed embodiments are merely examples and other embodiments can take various forms and alternative forms. The drawings are not necessarily drawn to scale. Some features can be exaggerated or minimized to show a specific component detail. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to employ the present embodiments in a variety of ways. As will be understood by one of ordinary skill in the art, various features illustrated and described in connection with any one of the figures can be combined with features illustrated and described in connection with one or more other figures, to produce embodiments that are not explicitly illustrated or described. The combinations of illustrated features that are specifically made possible are just representative of the embodiments that are possible. However, various combinations and modifications of the features consistent with the teachings of the present disclosure can be desired for particular applications or implementations.

[0022] The present application is not limited to the particular embodiments and methods described herein as specific components and / or conditions can vary. Still further, the terminology used herein is for the purpose of describing particular embodiments of the application only and is not intended to be limiting.

[0023] The terms "approximately," "about," or "substantially" can be used to describe the disclosed or claimed embodiments. The terms "approximately," "about," or "substantially" can modify one or more values or relative characteristics disclosed or claimed in the present disclosure to indicate that a value or relative characteristic is within a manufacturing tolerance and / or within ±0%, 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, or 10% of the value or relative characteristic.

[0024] With respect to the terms "comprising," "consisting of," and "consisting essentially of," when one of these three terms is used herein, the presently disclosed and claimed subject matter can include use of either of the other two terms.

[0025] It should also be appreciated that integer ranges explicitly include all intervening integers. For example, an integer range 1-10 explicitly includes 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10. Similarly, a range 1 to 100 includes 1, 2, 3, 4...97, 98, 99, 100. Similarly, when any range is called for, the intervening numbers can be taken as the alternative upper or lower limit in increments of 10 divided by the difference between the upper and lower limit. For example, if the range is 1.1 to 2.1, the following numbers 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, and 2.0 can be chosen as the lower or upper limit.

[0026] Wireless communication is extremely prevalent in modern society. However, new technologies and new uses for old technologies are still being discovered to provide more powerful functionality and efficiency associated with wireless communication systems. For example, wireless protocols can be used to provide traditional data communication, as well as simultaneously used as a sensing or similar radar technology. Among these are UWB radio communications. UWB technology generally has low energy requirements, but provides high bandwidth communication. Due to the low energy requirements and high communication bandwidth, UWB systems are of particular interest to the automotive industry, such as providing additional functionality and automated cars. UWB communications operate through pulse-based coding.

[0027] In UWB systems, a series of pulses can be transmitted to produce a complex channel impulse response (CIR), which can be extracted from a received UWB signal. The CIR can be further processed to provide additional functionality (e.g., to simultaneously provide data communication and radar-like sensing capabilities). Given the high bandwidth of UWB and the complex nature of the CIR, rich or detailed data about the signaling environment can be obtained. For example, the CIR provides information about various propagation paths, as information, for example, can be reflected off of the environment or objects, such as people or cars, and thus can be useful in assessing the environment. U.S. Application No. 16 / 368,994, filed March 29, 2019; U.S. Patent No. 11,402,485, which is derived from U.S. Application No. 16 / 398,571, filed April 30, 2019; and U.S. Patent No. 11,277,166, which is derived from U.S. Application No. 16 / 913,271, filed June 26, 2020, each of which relate to an ultra-wideband sensing system; the disclosure of which is incorporated herein by reference in its entirety.

[0028] Unlike conventional monostatic sensing technology, as shown in Figure 1A The spatially distributed UWB architecture 100, 100’ has multiple radio transceiver nodes 102 (e.g., at least two nodes, at least three nodes, or at least four nodes) that are spatially and physically separate or independent, as shown in Figures 1B-1C Conventional monostatic technology are linked spatially and physically, as they rely on a centralized clock / timing source for synchronization. For example, the transmitter 12 and receiver 14 of a monostatic transceiver are typically located in a single device. In other words, the transmitter 12 and receiver 14 of a monostatic transceiver are collocated, whereas the transmitter(s) 104 and receiver(s) 106 of a bistatic or multistatic transceiver are not collocated. For example, the monostatic architecture 10, shown in Figure 1A has a transmitter 12, receiver 14, and link 16 therebetween, whereas the bistatic architecture 100 / multistatic architecture 100’, shown in Figures 1B-1C has no link.

[0029] Distributed UWB architectures 100, 100' such as the bistatic architecture 100 (see Figure 1B ) and the multistatic architecture 100' (see Figure 1C ) do not share a centralized clock / timing source and / or physical link, and thus do not have the same positioning constraints. Instead, the bistatic architecture 100 / multistatic architecture 100' includes separate independent clock / timing sources for the transmitter(s) 104, 104' and receiver(s) 106, 106'. For example, in various embodiments, multiple separate and independent radio nodes 102, 102' (each capable of transmitting and / or receiving UWB signals) are distributed throughout the surveillance area, which generally increases the sensing area and is more robust compared to monostatic radars. Multistatic systems can also be more easily scaled.

[0030] As shown in Figure 1B , in various embodiments, the bistatic architecture 100 includes transmitter nodes 104 that are spatially and physically separate from one or more receiver nodes 106. In refinements, multiple receiver nodes 106 can be used to enhance robustness, such as against specular reflections. In some embodiments, as shown in Figure 1C , the multistatic architecture 100' includes multiple transmitter nodes 104' and multiple receiver nodes 106'. In the bistatic system 100 and / or the multistatic system 100', data fusion algorithms such as Kalman filters, Bayesian decision networks, Dempster-Shafer framework, convolutional neural networks (CNNs), and / or Gaussian processes can be used to process the various CIR streams as an aggregate. For example, Kalman filters can be used for tracking and / or CNNs can be used for activity recognition. In some embodiments, radio nodes 102, 102' can operate as both transmitter nodes 104, 104' and / or receiver nodes 106, 106' by both transmitting UWB signals and receiving UWB signals. However, the separate and independent clock / timing sources cause random sampling offsets and random and / or uncorrelated phase noise between the separate and independent transmitter(s) 104, 104' and receiver nodes 106, 106', which can be problematic and difficult to overcome. Figures 2A-2B This distinction is further illustrated.

[0031] Referring to Figures 2A-2B, showing a monostable transceiver 200 and a bistable transceiver 220. The monostable transceiver 200 includes a transmitter 202 and a receiver 204, both of which are in communication with a shared timing source, such as a local oscillator, crystal oscillator 206, and / or phase-locked loop (PLL) 208. The bistable transceiver 220 includes a transmitter 202' and a receiver 204', but the transmitter 202' is in communication with a first timing source, such as a local oscillator, crystal oscillator 206', and / or PLL 208', and the receiver 204' is in communication with a separate and distinct second timing source, such as a local oscillator, crystal oscillator 206", and / or PLL 208".

[0032] The monostable transmitter 202 can produce an unprocessed (e.g., baseband pulse) signal I(t) that can be upconverted by a mixer 210 to an output (e.g., passband) signal S c (t) having a carrier frequency f tx . The carrier signal can be generated by the PLL 208 using the crystal oscillator 206. The output signal S tx (t) is emitted / transmitted (e.g., step 902) from an antenna of the transmitter 202, which can be characterized by equation (1):

[0033]

[0034] In various embodiments, t is time, j is and θ0is an initial phase. After transmission, the output signal S tx may be reflected, e.g., from a single target, before being received (e.g., step 904) by the receiver 204. The received signal can be characterized by equation (2):

[0035]

[0036] In one or more embodiments, t TOF is the time of flight (i.e., delay), which can be expressed in equation (3):

[0037]

[0038]

[0039] In various embodiments, d is the distance between the target and the transceiver, and c is the speed of light. The distance of the target is expressed by the time of flight as shown in equation (3) above, which is determined from the amplitude spectrum or from the phase, which is expressed by equation (4):

[0040]

[0041] ​In various embodiments, λ is a wavelength. When receiving the reflected signal, the mixer 212 down-converts it back to the unprocessed (baseband impulse) signal. The transmitter and receiver are collocated such that the unprocessed (baseband impulse) signal is identical to the original unprocessed signal (i.e., same frequency and phase). The CIR can be extracted by cross-correlating the original unprocessed signal I(t) with the received baseband signal (e.g., step 906), providing detailed information about the environment. The CIR is characterized by equation (5):

[0042]

[0043] In one or more embodiments, δ is a Dirac delta function. The resolution in amplitude is limited by the bandwidth, as shown in equation (6):

[0044]

[0045] In various embodiments, d res is the distance resolution, c is the speed of light and B is the bandwidth. However, when analyzing the phase data, the resolution is much finer and is the same as the wavelength (λ) in amplitude. Thus, monitoring the change in phase over time can provide a more fine-grained analysis, such as smaller displacements and / or velocity estimates of a target. Such better resolution can enable gesture recognition, fall detection, or even vital sign monitoring.

[0046] In a bistatic radar according to Figure 2B , the carrier signal (S tx ) generated at the transmitter 202' has a frequency (f tx ) for up-conversion, and the carrier signal (S rx ) received at the receiver 204' has a frequency (f rx ) for down-conversion. The frequencies (f tx and f rx ) are different. In other words, they can have slightly different center frequencies. The frequency offset (Δf) can be defined by equation 7:

[0047] Δf = f tx - f rx (7).

[0048] Further, the up-converted and down-converted signals have an uncorrelated initial phase, resulting in a phase offset (Δθ), which can be defined by equation 8:

[0049] Δθ = θ tx - θ rx (8).

[0050] In one or more embodiments, θ tx is the initial phase of the transmitted signal, θrx is the initial phase of the received signal. Thus, the transmitted signal can be expressed in equation 9:

[0051]

[0052] The received signal can be expressed in equation 10:

[0053]

[0054] The CIR from the bistatic architecture 220 can be characterized by equation (11):

[0055]

[0056] Thus, the bistatic architecture 220 additionally accounts for frequency offset (Af) and phase offset (A0) (e.g., step 908). The frequency offset can cause the phase to linearly drift over time. The phase offset or noise can be treated as a random number in the characterization.

[0057] Reference Figure 3A , the bistatic architecture 300 includes a transmitter 302 and a receiver 304 that are not physically linked, synchronized, and do not include a shared timing source. For example, a pair of Qorvo DW 1000 transceivers can be used as the transmitter 302 and the receiver 304. Figures 3B-3C illustrates three adjacent CIR samples obtained from the bistatic architecture 300 under a general steady-state environment. Figure 3A Figure 3B and Figure 3C The CIRs of and are not aligned. For example, each sample is shifted along the x-axis, thereby indicating time-domain misalignment. This is associated with sampling offset. The frequency offset (Af) described above is not shown because a conventional commercial UWB transceiver can already account for or estimate the frequency offset. For example, this functionality can be useful for performing coherent demodulation.

[0058] Figure 3C Random phase offsets associated with different initial phases are also demonstrated because h(0), h(l), h(2) are each randomly shifted along the y-axis. The randomness of such shifts is particularly challenging. For example, a pre-computed estimation strategy can be ineffective. However, the phase noise associated with a non-synchronized node must be identified and distinguished from the phase variation associated with a target for more nuanced or specific processing, such as involved in gesture recognition and vital sign monitoring. This can be achieved by focusing on the first path (such as the line-of-sight (LOS) path 402), as Figure 4A ​The first path is described herein as a LOS path, it should be understood that a non-line-of-sight (NLOS) first path can be used in certain cases to estimate and cancel phase noise associated with subsequent paths. Generally, the CIR is composed of a LOS path 402 and multiple reflected NLOS paths 404 and 406, which can be characterized by equation 12:

[0059]

[0060] In various embodiments, h LOS (t) is a LOS element, and h NLOS (t) represents a NLOS element. By identifying the phase noise associated with the LOS path 402, the phase noise can be estimated and ignored, cancelled, and / or removed from the other NLOS paths. As Figure 4A illustrated, the LOS path 402 travels directly from the transmitter 404 to the receiver 406. Thus, the phase change of the LOS element corresponds to the phase noise (Δθ) as this can be, for example, the only or primary cause. In the same CIR, the NLOS elements suffer the same phase noise (Δθ) as the LOS element. Thus, the phase of the LOS element is used to estimate the phase error associated with the lack of synchronization or asynchronization of the carrier. This estimate can be used to adjust and / or correct the phase of the NLOS (reflected) paths.

[0061] The LOS path 402 involves the shortest distance (as Figure 4A illustrated), thus the LOS path signal is easily separated or extracted from the CIR based on the time domain (e.g., the first or leading tap is the LOS), as illustrated by Figure 4B For example, a leading edge detection (LED) technique, such as the LED algorithm, can be used to identify the first tap. In a refinement, the phase of the leading edge is obtained and subtracted from all subsequent CIR taps to cancel the phase noise (Δθ), as illustrated by Figure 5 The sampling offset can also be cancelled or normalized by aligning the CIR samples over time based on the leading edge. For example, the synchronized CIR can be characterized by equation (13):

[0062]

[0063] As illustrated above, the phase noise (Δθ) is cancelled. In one or more embodiments, since the distance of the LOS path is constant, d LOS is constant, such that the entire term is just a constant and does not affect the signal processing used to estimate the relative movement. In other words, only the phase change is significant for such processing.

[0064] In various embodiments, synchronizing a series or multiple unprocessed CIR samples is just a step (e.g., a first step or a starting step) in the process. For example, radar-like sensing techniques can include one or more of: (1) CIR synchronization 610, (2) signal processing 630, and (3) fusion and / or making decisions 640, as shown in Figure 6

[0065] Again, referring to Figure 6 , each step can have a series of sub-steps. For example, synchronization 610 can include upsampling 612, leading edge detection 614 to determine a first path and / or tap index, alignment 616 based on the first path, phase noise estimation 618 based on the phase of the first path, and / or phase error cancellation 620. Signal processing is additional processing beyond synchronization, such as task-specific processing on, for example, various data representations. In various embodiments, signal processing steps can include numerous processing techniques (e.g., steps 910), such as phase pattern analysis 632, range-doppler estimation 634, and / or micro-doppler estimation 636. In various embodiments, processing steps can be followed by step 640, which includes fusion 642, providing predictions 644, making decisions, and / or initiating performance 646 (e.g., steps 912 and 914).

[0066] Phase pattern analysis 632 can be used, for example, to sense micro-motions of a target, such as respiration, heartbeat monitoring, and / or vibration. In refinements, micro-motions can refer to movements across a distance of no more than 5 cm, or more preferably no more than 1 cm, or even more preferably no more than 0.5 cm. Phase pattern analysis can involve analyzing the frequency domain of the synchronized CIR. For example, the phase can be transformed to the frequency domain by a Fourier transform algorithm, such as a Fast Fourier Transform algorithm (FFT). Once transformed to the frequency domain, deviations can be detected and correlated to motion. In refinements, significant deviations can signal duress or trigger an alarm or alert. For example, Figures 7A-7C Phase pattern analysis using a pair of DW1000 UWB radios to monitor respiration is shown. For example, Figure 7A A fast-time tap amplitude curve is shown, which can be used to identify a first path for synchronization and a respiration pattern. Figure 7B A slow-time tap phase curve is shown, which shows a periodic respiration pattern. Figure 7C is Figure 7B autocorrelation of the phase sequence in

[0067] ​Additionally or alternatively, range Doppler estimation can be used, such as for localization or tracking of objects. Range Doppler estimation can include performing a short-time Fourier transform (STFT) on each CIR tap to produce a range Doppler map. Various techniques can be used to identify targets. For example, a constant false alarm rate (CFAR) algorithm can be used to identify targets. CFAR algorithms can assist in distinguishing targets from general noise. This technique can be used to determine the velocity of a target. In one or more embodiments, a Kalman filter can be used to track based on this information.

[0068] In still another example, micro-Doppler estimation can be used to process and understand the environment through the received signal. In one or more embodiments, micro-Doppler estimation can be used to analyze a series of motions such as for gesture recognition. Similar to range Doppler estimation, a short-time Fourier transform can be performed on the CIR to provide a velocity profile versus time. Figure 8 Velocity profiles from micro-Doppler signal processing of a target walking forward and backward are shown. For example, a forward velocity of approximately 23-70 cm per second is detected at approximately two seconds, and a backward velocity of approximately 28-51 cm per second is detected at approximately four seconds.

[0069] A variety of possible design architectures are possible. For example, one receiver - multiple transmitters (processing pipeline at one receiver) or multiple receivers - multiple transmitters can be used, where each device can transmit while all other devices receive (round robin fashion). The collection can be transmitted to a central device after phase cancellation or after task-specific processing for processing as unprocessed data. In other words, each node can perform processing or a portion of processing individually, or the nodes can send unprocessed data or partially processed data directly to a central processing unit, which can be a single node or a separate computing device. Thus, in still another embodiment, all devices can perform the entire processing pipeline and inform the central device of the prediction / decision for activation. In various embodiments, machine learning can be employed for additional or further processing, such as at the central device.

[0070] In one or more embodiments, the functions, steps and / or algorithms described herein can be carried out by a controller (not shown). The controller can include a processor, a memory, and / or a non-volatile storage device. The processor can include one or more devices selected from a high-performance computing system including high-performance cores, microprocessors, microcontrollers, digital signal processors, microcomputers, central processing units, field programmable gate arrays, programmable logic devices, state machines, logic circuits, analog circuits, digital circuits, or any other device that manipulates (analog or digital) signals based on computer-executable instructions resident in the memory. The memory can include a single memory device or a number of memory devices, including but not limited to random access memory (RAM), volatile memory, non-volatile memory, static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, cache memory, or any other device capable of storing information. The non-volatile storage device can include one or more persistent data storage devices, such as a hard drive, an optical drive, a tape drive, a non-volatile solid-state device, cloud storage, or any other device capable of persistently storing information.

[0071] The processor can be configured to read the memory and execute computer-executable instructions of the non-volatile storage device. The executable instructions can reside in software modules. The software modules can include an operating system and applications. The software modules can be compiled or interpreted from computer programs created using a variety of programming languages and / or technologies, including, without limitation, and either alone or in combination, Java, C, C++, C#, Objective C, Fortran, Pascal, Java Script, Python, Perl, and PL / SQL.

[0072] When executed by the processor, the computer-executable instructions of the software modules cause the computing platform to implement one or more of the functions, steps, and / or algorithms disclosed herein. The non-volatile storage device can also include data that supports the functions, features, calculations, and processes. It should also be understood that numerous other devices can employ certain components as described herein and cooperate with other components.

[0073] While the example embodiments have been described above, these embodiments are not intended to describe all possible forms of the disclosure. The words used in the specification are words of description rather than limitation, and it is understood that various changes can be made without departing from the scope of the disclosure. As previously described, features of various embodiments can be combined to form further embodiments of the present disclosure that can not be expressly described or illustrated. While various embodiments can have been described as providing advantages or being superior to other embodiments or prior implementations, proper implementation of the one or more features can depend on the particular application and requirements. By way of example, some configurations can be more highly desired for a particular implementation, and therefore, may

Claims

1. A detection method comprising: Transmitting UWB signals from designated transmitter nodes; receiving the UWB signal at a separate receiving node; extracting a plurality of channel impulse response (CIR) signals from the UWB; The multiple CIRs are synchronized to provide a series of synchronized CIRs by: Upsample each CIR; aligning the plurality of CIRs; Phase noise estimation based on the phase of the line-of-sight path signal; as well as removing the phase noise from each CIR; as well as The synchronized CIRs are processed to detect objects or activities. 2 . The detection method of claim 1 , further comprising notifying a user of a prediction in response to detecting the object or activity. The detection method of claim 1 , further comprising performing a task in response to detecting the object or activity.

4. The detection method according to claim 1, wherein Processing includes phase pattern analysis of the synchronized CIRs.

5. The detection method according to claim 4, wherein Processing includes detecting micro-movements.

6. The detection method according to claim 5, wherein The micro-movement is breathing or heartbeat.

7. The detection method according to claim 1, wherein Processing includes range-Doppler estimation.

8. The detection method according to claim 1, wherein Processing includes short-time Fourier transform.

9. The detection method according to claim 1, wherein Processing involves identifying targets using a constant false alarm rate algorithm.

10. The detection method according to claim 1, wherein Aligning the plurality of CIRs includes employing a leading edge detection algorithm and aligning the CIRs based on a first path signal.

11. The method according to claim 1, wherein Estimating the phase noise includes separating the line-of-sight path signals.

12. A method for synchronizing a plurality of ultra-wideband (UWB) nodes to achieve radar-like functionality, the method comprising: Upsampling each of a plurality of channel impulse responses (CIRs) from UWB; Adopting a leading edge detection algorithm and aligning the plurality of CIRs based on the leading edge; Determine the phase of the line-of-sight path signal for each CIR; as well as Phase errors are removed by subtracting the phase of the line-of-sight path signal from each subsequent path signal.

13. The method according to claim 12, wherein: Leading edge detection is used to determine the line-of-sight path signal.

14. A distributed ultra-wideband (UWB) radar system comprising: a transmitting node operable to transmit a UWB signal; A receiving node operable to: receiving the UWB signal; extracting a plurality of channel impulse response (CIR) signals from the UWB; The multiple CIRs are synchronized to provide a series of synchronized CIRs by: Upsample each CIR; aligning the plurality of CIRs; Phase error of phase estimation based on line-of-sight path; as well as Eliminating the phase error; processing the synchronized CIR to detect an object or movement; as well as In response to detecting the object or movement, an alert corresponding to the prediction is initiated or a task is performed based on the object or movement.

15. The system according to claim 14, wherein: The transmission node is one of a plurality of transmission nodes.

16. The system according to claim 15, wherein: The receiving node is one of a plurality of receiving nodes.

17. The system of claim 14, wherein: The receiving node is one of a plurality of receiving nodes.

18. The system according to claim 17, wherein: The plurality of receiving nodes communicate with a central processing unit to extract a plurality of channel impulse responses (CIRs) from the UWB signal, synchronize the plurality of CIRs to provide a series of synchronized CIRs, process the synchronized CIRs to detect an object or movement, and / or initiate an alarm corresponding to a prediction or perform a task based on the object or movement.

19. The system according to claim 17, wherein: The plurality of receiving nodes are in communication with a controller operable to employ a data fusion algorithm.

20. The system of claim 14, wherein: The receiving node communicates with a controller operable to extract a plurality of channel impulse responses (CIRs) from the UWB signal, synchronize the plurality of CIRs to provide a series of synchronized CIRs, process the synchronized CIRs to detect an object or movement, and initiate an alarm corresponding to a prediction or perform an execution task based on the object or movement.

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