Distributed detection with ultra-wideband radios

Abstract

A distributed ultra-wideband (UWB) radar system and methods for operating the same are disclosed. The distributed radar system utilizes a plurality of separate UWB radios or nodes that do not utilize a centralized clock or timing source. However, the distributed UWB system is operable to provide radar-like functionality and fine-grained detection capabilities through synchronization based on the processing of line-of-sight (LOS) signals and noise estimates. Synchronized channel impulse responses (CIRs) can be processed for general object detection and tracking, gesture recognition, or even micro-movements, such as for vital sign monitoring.

Description

State of the art

[0001] The present disclosure relates to a distributed ultra-wideband (UWB) sensing technology. More specifically, to an architecture that employs a unique synchronization strategy to synchronize and align channel impulse responses (CIRs) communicated between separate or distributed UWB radios to enable radar-like functionality and fine-grained sensing despite the absence of a centralized clock source. Disclosure of the invention

[0002] Conventionally, object detection and tracking is performed using monostatic systems, such as traditional radar systems. These systems have a centralized clock source. Such limitations generally lead to a centralized or localized system. Brief description

[0003] A detection method is provided that includes transmitting a UWB signal from a first (designated) transmitter node, receiving a UWB signal at a second, separate receiver node, extracting one or more (a plurality) of channel impulse responses (CIRs) from the UWB signals, synchronizing the plurality of CIRs to provide a sequence of synchronized CIRs, and processing the synchronized CIRs to detect an object or activity (e.g., movement) thereof. Synchronization may include upscaling each CIR, aligning the plurality of CIRs, estimating phase noise, and eliminating the phase noise from each received CIR. The phase noise may be based on the phase of a line-of-sight path signal. For example, the phase of the received line-of-sight signal may be compared to the phase of the transmitted signal.

[0004] A method for synchronizing a plurality of ultra-wideband (UWB) nodes for radar-like functionality is also provided. The method includes upscaling each channel impulse response (CIR) of a plurality of CIRs from the UWB signal, applying a rising edge detection algorithm, aligning the plurality of CIRs based on a rising edge, determining a phase of a line-of-sight path signal for each CIR, and eliminating the phase error 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 a plurality of channel impulse responses (CIRs) from the UWB signal, synchronize the plurality of CIRs to provide a sequence of synchronized CIRs, process the synchronized CIRs to detect an object or motion, and, in response to detecting an object or motion, trigger an alarm according to a prediction or perform a task based on the object or motion. Synchronization may include upscaling each CIR, aligning the plurality of CIRs, estimating a phase error based on a phase of a line-of-sight path, and eliminating the phase error. Short description of the drawings Fig. 1A is a schematic view of one embodiment of a monostatic architecture. Fig. Figure 1B is a schematic view of one embodiment of a bistatic architecture. Fig. Figure 1C is a schematic view of one embodiment of a multi-static environment. Fig. Figure 2A is a schematic view of one embodiment of a monostatic system. Fig. Figure 2B is a schematic view of one embodiment of a bistatic system. Fig. Figure 3A is a perspective view of an experimental embodiment of a distributed system. Fig. Figure 3B is a diagram showing CIR taps extracted from a UWB signal received from the distributed system of Fig. 3A was received. Fig. Figure 3C is a phase diagram showing CIR taps extracted from the UWB signal received from the distributed system of Fig. 3A was received. Fig. 4A is a schematic view of one embodiment of a bistatic system with line-of-sight (LOS) and non-line-of-sight (NLOS) paths of a UWB signal. Fig. Figure 4B is a timing diagram of the received CIR taps from the various paths of the embodiment of Fig. 4A. Fig. Figure 5 is a schematic representation of one embodiment of a multi-static system. Fig. 6 is a flowchart illustrating the operation of one embodiment of a distributed UWB sensing system. Fig. Figure 7A-C shows the phase pattern analysis for respiration using DW1000 UWB radios or nodes. Fig. Figure 8 shows a micro-Doppler diagram for a person walking back and forth. Fig. Figure 9 is a flowchart illustrating a detection method. DETAILED DESCRIPTION

[0006] Embodiments of the present disclosure are described below. It should be understood, however, that the disclosed embodiments are merely examples, and other embodiments may take various and alternative forms. The drawings are not necessarily to scale. Some features may be exaggerated or reduced to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for providing guidance to one skilled in the art to variously employ the embodiments of the present invention.As one of ordinary skill in the art will understand, various features illustrated and described with reference to any of the figures may be combined with features illustrated in one or more other figures to create embodiments not explicitly illustrated or described. The illustrated combinations of features provide representative embodiments for typical applications. However, various combinations and modifications of the features consistent with the teachings of the present disclosure may be desired for particular applications or implementations.

[0007] The present invention is not limited to the specific embodiments and methods described below, as specific components and / or conditions may vary. Furthermore, the terminology used herein is for the purpose of describing particular embodiments of the present invention and is not intended to be limiting in any way.

[0008] The terms "substantially," "generally," or "approximately" may be used herein to describe disclosed or claimed embodiments. The terms "substantially," "generally," or "approximately" may modify a value or relative characteristic disclosed or claimed in the present disclosure to be within manufacturing tolerances and / or within ± 0%, 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, or 10% of the value or relative characteristic.

[0009] With respect to the terms "comprising," "consisting of," and "consisting essentially of," when any of these three terms are used herein, the presently disclosed and claimed subject matter may include the use of any of the other two terms.

[0010] It can be appreciated that integer ranges explicitly include all the integers lying within them. For example, the integer range 1-10 explicitly includes 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10. Similarly, the range 1 to 100 includes the numbers 1, 2, 3, 4 ... 97, 98, 99, 100. Similarly, if an arbitrary range is required, numbers lying within it that are increments of the difference between the upper limit and the lower limit divided by 10 can be used as alternative upper or lower limits. 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 selected as the lower or upper limit.

[0011] Wireless communication is extremely widespread in modern society. However, new technologies and new uses for older technologies are still being discovered to provide greater functionality and efficiency for wireless communication systems. For example, wireless protocols can be used both for traditional data communication and simultaneously as sensor- or radar-like technology. One example is UWB radio communication. UWB technology generally has lower power requirements but offers high-bandwidth communication. Due to their low power requirements and high communication bandwidths, UWB systems are of particular interest to the automotive industry, for example, for providing additional functionality and vehicle autonomization. UWB communication uses pulse-based coding.

[0012] In UWB systems, a sequence of pulses can be transmitted to generate complex channel impulse responses (CIRs), which can be extracted from received UWB signals. The CIRs can be further processed to provide additional functionality (e.g., data communication and radar-like detection capabilities simultaneously). Given the high bandwidth of UWB and the complex nature of CIRs, rich or detailed data about the signal environment can be obtained. For example, CIRs provide information about the different propagation paths, which can be useful in assessing an environment, for example, as they are reflected by the surroundings or by objects such as people or vehicles. US application No. 16 / 368,994 filed on March 29, 2019, US patent No. 11,402,485 resulting from US application No. 16 / 398,571 filed on April 30, 2019, and US patent No. 11,277,166 resulting from US application No. 16 / 398,571 filed on April 26, 2019.No. 16 / 913,271, filed on June 14, 2020, each relate to ultra-wideband sensor systems; their disclosures are incorporated herein by reference in their entirety.

[0013] Unlike conventional monostatic recognition technologies, such as Fig. 1A, spatially distributed UWB architectures 100, 100' have a plurality of radio transceiver nodes 102 (e.g., at least two nodes, at least three nodes, or at least four nodes) that are spatially and physically separated or independent, as shown in Fig. 1B-C. Conventional monostatic technologies are spatially and physically connected because they rely on a central clock / clock 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 co-located, while the transmitter 104 and receiver 106 of a bistatic or multistatic transceiver are not co-located. The Fig. For example, the monostatic architecture 10 shown in Figure 1A comprises a transmitter 12, a receiver 14 and a connection 16 therebetween, and the Fig. The 100 / 100' bistatic / multistatic architectures shown in Figures 1B-C have no connection.

[0014] Distributed UWB architectures 100, 100' such as the bistatic (see Fig. 1B) and the multistatic (see Fig. 1C) Architectures 100, 100' do not share a central clock / clock source and / or physical connection and are therefore not subject to the same local constraints. Instead, the bistatic / multistatic architectures 100, 100' contain separate, independent clock / clock sources for the transmitter(s) 104, 104' and the receiver(s) 106, 106'. In various embodiments, for example, a plurality of separate and independent radio nodes 102, 102', each capable of transmitting and / or receiving a UWB signal, are distributed throughout the surveillance area, which generally increases the detection range and is more robust compared to monostatic radars. The multistatic systems are also much easier to scale up.

[0015] In various embodiments, a bistatic architecture 100 as shown in Fig. 1B, a transmitting node 104 that is spatially and physically separated from one or more receiving nodes 106. In a refinement, a plurality of receiving nodes 106 may be used to increase robustness against, for example, specular reflections. In some embodiments, a multistatic architecture 100', as shown in Fig. 1C, a plurality of transmitting nodes 104' and a plurality of receiving nodes 106'. In the bi- and / or multistatic system 100, 100', a data fusion algorithm such as a Kalman filter, a Bayesian decision network, a Dempster-Shafer framework, convolutional neural networks (CNN), and / or Gaussian processes may be used to process the various CIR streams as an aggregate. For example, a Kalman filter may be used for tracking and / or a CNN may be used for activity detection. In some embodiments, the radio nodes 102, 102' may operate as transmitting nodes 104, 104' and / or as receiving nodes 106, 106' by both transmitting a UWB signal and receiving UWB signals.However, separate and independent clock sources cause random sampling skew and random and / or uncorrelated phase noise between the separate and independent transmitters 104, 104' and receiver nodes 106, 106', which can be problematic and difficult to resolve. This distinction is described in . Fig. 2A-B are shown in more detail.

[0016] Reference is made to Fig. 2A-B, which show the monostatic transceiver 200 and the bistatic transceiver 220. The monostatic transceiver 200 includes a transmitter 202 and a receiver 204, both of which are communicatively coupled to a common clock source, such as a local oscillator, a crystal oscillator 206, and / or a phase-locked loop (PLL) 208. The bistatic transceiver 220 includes a transmitter 202' and a receiver 204', but the transmitter 202' is communicatively coupled to a first clock source (e.g., local oscillator, crystal oscillator 206', and / or PLL 208') and the receiver 204' is communicatively coupled to a second, separate and different clock source (e.g., local oscillator, crystal oscillator 206', and / or PLL 208').

[0017] The monostatic transmitter 202 may generate a raw (e.g., a baseband pulse) signal I(t) which is converted by a mixer 210 into an output (e.g., a passband) signal S tx (t) with a carrier frequency f c The carrier signal can be generated by the PLL 208 using the crystal oscillator 206. The output signal S tx (t) is transmitted / transmitted by an antenna of the transmitter 202 (e.g., step 902), which may be characterized by formula (1): Stx(t)=I(t)ej2πfct+θ0

[0018] In various embodiments, t is the time, j is −1 and θ0 is the initial phase. After transmission, the output signal S tx for example, be reflected at a single target before being received by the receiver 204 (e.g., step 904). The received signal may be characterized by formula (2): Srx(t)=I(t−tTOF)ej2πfc(t−tTOF)+θ0

[0019] In one or more embodiments, t TOF the running time (ie the delay), which can be represented by formula 3: tTOF=2dc

[0020] In various embodiments, d is the distance between the target and the transceiver, and c is the speed of light. The distance to the target is represented by the travel time as in formula (3) above, which is determined from the amplitude spectrum, or by the phase, which is represented by formula (4): 4πdλ

[0021] In various embodiments, λ is the wavelength. After receiving the reflected signal, it is converted back into a raw (baseband pulse) signal by the mixer 212. The transmitter and receiver are co-located so that the raw (baseband pulse) signal matches the original raw signal (i.e., same frequency and phase). The CIR can be extracted (e.g., in step 906) by cross-correlating the original raw signal I(t) with the received baseband signal, which provides details about the environment. The CIR is characterized by formula (5): hmono(t)=δ(t−tTFO)ej2πfc(t−tTOF)=δ(t−2dc)ej2πfc(−2dc)=δ(t−2dc)e−j4πdλ

[0022] In one or more embodiments, δ is the Dirac delta function. The resolution based on amplitude is limited by the bandwidth, as shown in formula (6): dres=c2B

[0023] In various embodiments, the res The range resolution, c is the speed of light, and B is the bandwidth. However, when analyzing phase data, the resolution is much finer and is on the same order of magnitude as the wavelength (λ). Accordingly, monitoring phase changes over time can provide finer-grained analysis, such as smaller target displacements and / or velocity estimates. This better resolution can enable the detection of gestures, falls, or even vital sign monitoring.

[0024] With a bistatic radar according to Fig. 2B has the carrier signal (S tx ) a frequency (f tx ) for upconversion and has the carrier signal (S rx ) a frequency (f rx ) for downconversion. The frequencies (f tx and f rx) are different. In other words, they can have a slightly different center frequency. The frequency offset (Δf) can be defined by Formula 7: Δf=ftx−frx

[0025] Furthermore, the upconversion and downconversion signals have uncorrelated initial phases, resulting in a phase offset (Δθ) that can be defined by Formula 8: Δθ=θtx−θrx

[0026] In one or more embodiments, θ tx the initial phase of the transmitted signal and is θ rx the initial phase of the received signal. Accordingly, the transmitted signal can be represented by Formula 9: Stx(t)=I(t)ej2πjtxt+θtx

[0027] The received signal can be represented by formula 10: Srx(t)=I(t−tTOF)ej2πfrx(t−tTOF)+θrx

[0028] The CIR of the bistatic architecture 220 can be characterized by the formula (11): hbi(t)=δ(t−tToF)ej2πΔft−2πjtxtTOF+Δθ=hmono(t)ejπΔft+Δθ

[0029] Accordingly, the bistatic architecture 220 additionally considers the frequency offset (Δf) and the phase offset (Δθ) (e.g., step 908). The frequency offset may result in a linear phase drift over time. The phase offset, or noise, may be considered a random number during characterization.

[0030] Reference is made to Fig. 3A; the bistatic architecture 300 includes transmitters 302 and receivers 304 that are not physically connected and synchronized with each other and do not share a common clock source. For example, a pair of Qorvo DW1000 transceivers can be used as transmitters 302 and receivers 304. Fig. 3B-C show three adjacent CIR scans obtained from the bistatic architecture of Fig. 3A were obtained in a generally static environment. The CIRs of the Fig. 3B and Fig. 3C are not aligned. For example, each sample is offset along the x-axis, indicating a misalignment in the time domain. This is related to the sampling offset. The frequency offset (Δf) described above is not shown, as conventional commercial UWB transceivers can already account for or estimate the frequency offset. This functionality can be useful, for example, for performing coherent demodulation.

[0031] The random phase shift associated with different initial phases is also used in Fig. 3C, because h(0), h(1), and h(2) are each randomly offset along the y-axis. The randomness of this offset presents a particular challenge. For example, pre-computed estimation strategies may be ineffective. But the phase noise associated with unsynchronized nodes must be identified and distinguished from the phase changes associated with the target for finer-grained or more specific processing, such as 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 shown in Fig. 4A. Although the LOS path is described here as the first path, under certain circumstances, a first non-line-of-sight (NLOS) path may also be used to estimate and suppress the phase noise of subsequent paths. Generally, CIRs consist of an LOS path 402 and several reflected NLOS paths 404 and 406, which may be characterized by Formula 12: hbi(t)=hLOS(t)+∑ihNLOS,i(t) =αLOSδ(t−dLOSc)e−j2πdLOSλ+Δθ+∑iαiδ(t−2dic)e−j4πdiλ+Δθ

[0032] In various embodiments, H LOS (t) for the LOS element and h NLOS (t) for NLOS elements. By identifying the phase noise associated with LOS path 402, the phase noise can be estimated and ignored, suppressed, and / or removed from the other NLOS paths. LOS path 402 runs directly from transmitter 404 to receiver 406, as shown in Fig. 4A. Accordingly, the phase change for the LOS element corresponds to the phase noise (Δθ), as this may, for example, be the sole or primary cause. In the same CIR, the NLOS elements are subject to the same phase noise (Δθ) as the LOS elements. Thus, the phase of the LOS element is used to estimate the phase error associated with the missing synchronization or unsynchronized carriers. This estimate can be used to adjust and / or correct the phase of NLOS (reflected) paths.

[0033] The LOS path 402 has the shortest distance (as in Fig. 4A), so that the LOS path signal can be easily separated or extracted from the CIR based on the time domain (e.g., the first or leading tap is the LOS path), as in Fig. 4B. For example, leading edge detection (LED) techniques, such as an LED algorithm, can be used to identify the first tap. In refinement, the phase of the leading edge is determined and subtracted from all subsequent CIR taps to eliminate the phase noise (Δθ), as shown in Fig. 5. The sampling offset can also be eliminated or normalized by temporally aligning the CIR samples based on the rising edge. For example, a synchronized CIR can be characterized by equation (13): hbi−sync(t)∑ihNLOS,i(t)hLOS(dLOSc)=∑iδ(t−zdic)e−jπdiλ+Δθe−j2πdLOSλ+Δθ∑iδ(t−2dic)e−j4πdiλ+2πdLOSλ

[0034] As shown above, the phase noise (Δθ) is eliminated. In one or more embodiments, d LOS constant, since the distance of the LOS path does not change, so the entire term 2πdLOSλ is merely a constant that does not affect the signal processing used to estimate relative motion. In other words, only the phase change is relevant for such processing.

[0035] In various embodiments, the synchronization of a sequence or plurality of raw CIR samples is merely one (e.g., first or initial) step in the process. For example, the radar-like detection methods may include one or more of (1) CIR synchronization 610, (2) signal processing 630, and (3) fusion and / or decision making 640, as described in Fig. 6 shown.

[0036] Reference is again made to Fig. 6; each step may include a number of substeps. For example, synchronization 610 may include upscaling 612, rising edge detection 614 to determine the first path and / or the 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 compensation 620. Signal processing is additional processing beyond synchronization, such as task-specific processing of different data representations. In various embodiments, the signal processing step may include numerous processing techniques (e.g., step 910), such as phase pattern analysis 632, range Doppler estimation 634, and / or micro Doppler estimation 636.In various embodiments, the processing step may be followed by a step 640 that includes fusion 642, providing a prediction 644, making a decision, and / or triggering performance 646 (e.g., steps 912 and 914).

[0037] For example, phase pattern analysis 632 may be used to detect micro-movements of a target, such as breathing, heart rate monitoring, and / or vibrations. In one refinement, micro-movements may refer to movements that extend over a distance of no more than 5 cm, more preferably no more than 1 cm, or even more preferably no more than 0.5 cm. Phase pattern analysis may involve analyzing the frequency domain of the synchronized CIRs. The phase may be transformed into the frequency domain, for example, using Fourier transform algorithms such as the fast Fourier transform (FFT). After transformation into the frequency domain, deviations may be detected and associated with movement. In one refinement, larger deviations may signal a duress or trigger a warning or alarm. Fig. For example, Figures 7A-C show the phase pattern analysis when monitoring respiration with a pair of DW1000 UWB radios. Fig. 7A shows a fast time-tap amplitude profile that can be used to identify the initial pathway for synchronization and the breathing pattern. Fig. Figure 7B illustrates a slow time-tap phase profile showing periodic breathing patterns. Fig. 7C is an autocorrelation of the phase sequence in Fig. 7B for more sophisticated respiratory pattern recognition. For example, the high correlation across multiple delays is representative of the periodicity of the phase, which can be representative of a respiratory pattern and / or respiratory rate.

[0038] Additionally or alternatively, a range-Doppler estimate may be used, for example, to locate or track an object. The range-Doppler estimation may include a short-time Fourier transform (STFT) for each CIR sample to generate a range-Doppler representation. Various techniques may be used to identify a target. For example, a constant false alarm rate (CFAR) algorithm may be used to identify a target. The CFAR algorithm can help distinguish targets from general noise. This technique can be used to determine a target's speed. In one or more embodiments, a Kalman filter may be used for tracking based on this information.

[0039] In yet another example, micro-Doppler estimates can be used to process and understand the environment based on the received signal. In one or more embodiments, micro-Doppler estimates can be used to analyze a sequence of movements, such as gesture recognition. Similar to range-Doppler estimation, a short-time Fourier transform of the CIRs can be performed to obtain a velocity profile over time. Fig. Figure 8 shows velocity profiles from micro-Doppler signal processing of a forward and backward moving target. For example, at approximately two seconds, a forward velocity of approximately 23-70 centimeters per second is detected, and at approximately four seconds, a backward velocity of approximately 28-51 centimeters per second is detected.

[0040] Several possible design architectures are conceivable. For example, one receiver and multiple transmitters (processing pipeline at a receiver), or multiple receivers and multiple transmitters can be used, with each device transmitting alternately while all others receive (round-robin approach). The collected data can be transmitted to a central device for processing as raw data, after phase cancellation, or after task-specific processing. In other words, each node can perform the processing or part of it individually, or the nodes can send the raw or partially processed data directly to a central processing unit, which can be a single node or a separate computing device. In another embodiment, all devices could perform the entire processing pipeline and inform a central device of a prediction / decision for actuation.In various embodiments, machine learning may be used for additional or further processing, for example in the central device.

[0041] In one or more embodiments, the functions, steps, and / or algorithms described herein may be performed by a controller (not shown). The controller may include a processor, memory, and / or non-volatile memory. The processor may comprise one or more devices selected from high-performance computing systems, 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 located in memory.The storage may comprise a single storage device or a series of storage 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 memory may comprise one or more persistent data storage devices, such as a hard disk drive, an optical drive, a tape drive, a non-volatile solid-state device, cloud storage, or any other device capable of persistently storing information.

[0042] The processor may be configured to read computer-executable instructions from non-volatile memory into memory and execute them. Executable instructions may be embodied in a software module. The software module may include operating systems and applications. The software module may be compiled or interpreted by a computer program developed using a variety of programming languages ​​and / or technologies, including, without limitation, Java, C, C++, C#, Objective C, Fortran, Pascal, Java Script, Python, Perl, and PL / SQL, alone or in combination.

[0043] When executed by the processor, the computer-executable instruction of the software module causes the computer platform to implement one or more of the functions, steps, and / or algorithms disclosed herein. Non-volatile memory may also contain data that supports the functions, features, calculations, and processes. It should further be understood that numerous other devices may utilize certain components described herein and may interoperate with other components.

[0044] Although exemplary embodiments are described above, these embodiments are not intended to describe all possible forms covered by the claims. The terms used in this specification are merely descriptive and non-limiting, and it is understood that various changes may be made without departing from the scope of the disclosure. As described above, the features of various embodiments may be combined to form further embodiments of the invention not expressly described or illustrated herein.While various embodiments could have been described as advantageous or preferred over other prior art embodiments or implementations with respect to one or more desired characteristics, those of ordinary skill in the art will recognize that one or more features or characteristics may be compromised to achieve desired overall system attributes depending on the particular application and implementation. These attributes include, but are not limited to, cost, strength, durability, life cycle cost, marketability, appearance, packaging, size, maintainability, weight, manufacturability, ease of assembly, etc.As such, to the extent that embodiments are described as being less desirable with respect to one or more characteristics relative to other embodiments or prior art implementations, those embodiments are not outside the scope of the disclosure and may be desirable for certain applications. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] US 16 / 368,994

[0012] US 11,402,485

[0012] US 16 / 398,571

[0012] US 11,277,166

[0012] US 16 / 913,271

[0012]

Claims

[1] Detection method, comprising: Transmitting a UWB signal from a designated transmitting node; Receiving the UWB signal at a separate receiving node; Extracting a plurality of channel impulse responses (CIRs) from the UWB signal; Synchronizing the plurality of CIRs to obtain a sequence of synchronized CIRs by: Upscaling each CIR; Aligning the majority of CIRs; Estimating a phase noise based on a phase of a line-of-sight path signal; and Eliminating phase noise from each CIR; and Processing the synchronized CIRs to detect an object or activity. [2] The detection method of claim 1, further comprising notifying a user of a prediction in response to detecting the object or activity. [3] The detection method of claim 1, further comprising performing a task in response to detecting the object or activity. [4] The detection method of claim 1, wherein the processing includes a phase pattern analysis of the synchronized CIRs. [5] The detection method of claim 4, wherein the processing includes detecting a micro-motion. [6] The detection method according to claim 5, wherein the micro-movement is breathing or a heartbeat. [7] The detection method of claim 1, wherein the processing includes a range Doppler estimation. [8] A recognition method according to claim 1, wherein the processing includes a short-time Fourier transform. [9] The detection method of claim 1, wherein the processing includes using a constant false alarm rate algorithm to identify a target. [10] The detection method of claim 1, wherein aligning the plurality of CIRs includes using a rising edge detection algorithm and aligning the CIRs based on a first path signal. [11] The method of claim 1, wherein estimating the phase noise includes separating the line-of-sight path signal. [12] A method for synchronizing a plurality of ultra-wideband (UWB) nodes for radar-like functionality, the method comprising: Upscaling each channel impulse response (CIR) of a plurality of the plurality of CIRs from a UWB signal; applying an algorithm to detect a rising edge and align the plurality of CIRs based on a rising edge; Determining a phase of a line-of-sight path signal for each CIR; and Eliminate the phase error by subtracting the phase of the line-of-sight path signal from each subsequent path signal. [13] The method of claim 12, wherein the line-of-sight path signal is determined by means of rising edge detection. [14] Distributed ultra-wideband (UWB) radar system comprising: a transmitting node operable to transmit a UWB signal; a receiving node capable of: to receive the UWB signal; extract a plurality of channel impulse responses (CIRs) from the UWB signal; to synchronize the plurality of CIRs to obtain a sequence of synchronized CIRs by: Upscaling each CIR; Aligning the majority of CIRs; Estimating a phase error based on a phase of a line-of-sight path signal; and Eliminating the phase error; Processing the synchronized CIRs to detect an object or movement; and in response to detecting an object or movement, triggering an alarm according to a prediction, or performing a task based on the object or movement. [15] The system of claim 14, wherein the transmitting node is one of a plurality of transmitting nodes. [16] The system of 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 of claim 17, wherein the plurality of receiving nodes communicate with a central unit to extract a plurality of channel impulse responses (CIRs) from the UWB signal, synchronize the plurality of CIRs to provide a sequence of synchronized CIRs, process the synchronized CIRs to detect an object or movement, and / or trigger an alarm according to a prediction or perform a task based on the object or movement. [19] The system of claim 17, wherein the plurality of receiving nodes are communicatively coupled to a controller operable to apply a data fusion algorithm. [20] The system of claim 14, wherein the receiving node is in communication 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 sequence of synchronized CIRs, process the synchronized CIRs to detect an object or motion, and trigger an alarm according to a prediction or perform a task based on the object or motion.

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