Improved safety in ultra-wideband distance measurement systems

Abstract

A method for determining a distance between a transmitter and a receiver, comprising storing, in a memory, first data indicating characteristics of an expected peak value, receiving, by the receiver, a data sequence comprising a first peak value and a second peak value, authenticating, by a processor, the data sequence in response to second data received in the second peak value, determining, by the processor, a distance between a transmitter and the vehicle communication system in response to a difference between a first time of reception of the first peak value and a second time of reception of the second peak value, initiating a determination of the distance in response to the first peak value matching the expected peak value, and activating, by a vehicle control unit, a vehicle control algorithm in response to the distance.

Description

Introduction

[0001] This disclosure relates to computer network security and, in particular, to secure distance measurement technologies. Specifically, aspects of this disclosure relate to systems, methods, and devices for proactively identifying and mitigating potential distance reduction attacks on distance measurement protocols that use ultra-wideband (UWB).

[0002] The increasing complexity and connectivity of modern vehicles have heightened their attractiveness to cyberattacks. The proliferation of features such as infotainment systems, telematics, and autonomous driving capabilities has expanded the attack surface and created new opportunities for malicious actors to exploit it. Furthermore, reliance on complex embedded systems and software can introduce vulnerabilities that attackers can exploit. Weak security measures, shared components, a lack of cybersecurity awareness in the automotive industry, and regulatory challenges further exacerbate the risk of cyberattacks on modern vehicles.

[0003] Phantom spike attacks and other distance measurement system attacks pose a significant security challenge to ultra-wideband (UWB) distance measurement systems. These attacks exploit the unique characteristics of UWB signals—their wide bandwidth and short pulse duration—to inject false signals, or "phantom spikes," into the communication channel. This manipulation can lead to inaccurate distance measurements, potentially compromising the security and reliability of the system. Phantom spike attacks can have devastating consequences, including unauthorized access to restricted areas or equipment, location spoofing, and interference with other devices that rely on UWB signals.Malicious actors can exploit inaccurate distance measurements to gain access to secure areas or equipment normally restricted to authorized personnel, or to manipulate location information provided by UWB distance measurement systems. This could lead to a variety of harmful consequences, such as inaccurate navigation, tracking errors, and even physical harm. Additionally, phantom peak attacks can disrupt the operation of other devices that use UWB signals, such as radar systems, wireless communication networks, and medical equipment. This could have serious repercussions for security, safety, and economic activity.

[0004] To mitigate the risks associated with phantom peak attacks, UWB distance measurement systems typically employ a combination of robust signal processing algorithms, cryptographic techniques, and physical countermeasures. Advanced signal processing techniques can help identify and filter out false signals, improving the accuracy and reliability of distance measurements. Cryptographic methods can protect the integrity and confidentiality of UWB communications, making it more difficult for attackers to inject false signals. Physical measures such as shielding, antenna diversity, and frequency hopping can also be used to reduce the vulnerability of UWB systems to phantom peak attacks.

[0005] Accordingly, it would be desirable to provide systems, methods, and mechanisms to proactively identify and mitigate potential range reduction attacks on UWB range measurements. More specifically, it would be desirable to provide mechanisms for access control, data integrity and source authentication, denial-of-service prevention, and the use of hardware to efficiently implement the aforementioned mechanisms. Furthermore, other desirable features and characteristics of the present disclosure will become apparent from the following detailed description and the accompanying claims, in conjunction with the accompanying drawings and the preceding technical field and background. SUMMARY

[0006] Disclosed herein are vehicle control systems and methods and associated control logic for providing vehicle safety systems, methods for manufacturing and operating such systems, and vehicles equipped with distributed computing systems. As an example, and not as a limitation, various embodiments of systems for providing a secure UWB distance measurement system in a motor vehicle data communication system disclosed herein are presented.

[0007] According to one aspect of an exemplary embodiment, a method for determining a distance between a transmitter and a receiver comprises: storing, in a memory, first data indicating characteristics of an expected peak value; receiving, by the receiver, a data sequence comprising a first peak value and a second peak value; authenticating, by a processor, the data sequence in response to second data received in the second peak value; determining, by the processor, the distance between the transmitter and the receiver in response to a difference between a first time of reception of the first peak value and a second time of reception of the second peak value, wherein a determination of the distance is initiated in response to the first peak value matching the expected peak value; and activating, by a vehicle control system,a vehicle control algorithm in response to distance.

[0008] According to another aspect of an exemplary embodiment, a search time interval is reduced to detect a subsequent first peak value in response to the fact that the first peak value does not match the expected peak value.

[0009] According to another aspect of an exemplary embodiment, wherein the first peak value and the second peak value are part of a pseudorandom spread spectrum time-jump sequence.

[0010] According to another aspect of an exemplary embodiment, wherein the vehicle control algorithm includes unlocking a vehicle and transferring the vehicle between a standby state and an on-powered state.

[0011] According to another aspect of an exemplary embodiment, wherein a search time interval for detecting a subsequent first peak value is reduced in response to the fact that the first peak value does not match the expected peak value, and wherein a duration of the search time interval is determined in response to a vehicle position, a vehicle environment, a weather condition, a user habit and a potential risk level.

[0012] According to another aspect of an exemplary embodiment, wherein a search time interval for detecting a subsequent first peak value is reduced in response to the fact that the first peak value does not match the expected peak value, and wherein a duration of the search time interval is determined in response to a multipath level estimated in response to a transmission of a signal and a detection of a reflection of the signal by a vehicle communication system.

[0013] According to another aspect of an exemplary embodiment, wherein a search time interval for detecting a subsequent first peak is reduced in response to the fact that the first peak does not match the expected peak, and wherein a duration of the search time interval is reduced in response to a length of a pseudorandom spread spectrum time-jump sequence preamble in a time domain.

[0014] According to another aspect of an exemplary embodiment, wherein the first peak value is rejected in response to the fact that the first peak value does not match the expected peak value, and a subsequent peak value is detected between the first peak value and the second peak value, wherein the subsequent peak value is then compared with the expected peak value.

[0015] According to another aspect of an exemplary embodiment, a power difference between the first peak value and the second peak value is used to compare the first peak value with the expected peak value.

[0016] According to another aspect of an exemplary embodiment, a device for determining a distance between a transmitter and a receiver comprises a memory configured to store initial data indicating the characteristics of an expected peak value, the receiver being configured to receive from the transmitter a data sequence comprising a first peak value and a second peak value, a processor authenticating the data sequence in response to second data received in the second peak value, and determining the distance between the transmitter and the receiver in response to a difference between a first time of receiving the first peak value and a second time of receiving the second peak value, with a distance determination being initiated in response to the first peak value matching the expected peak value.and is configured to generate a control signal in response to the distance, and a vehicle control unit to activate a vehicle control algorithm in response to the control signal.

[0017] According to another aspect of an exemplary embodiment, wherein the processor is further configured to reduce a search time interval for detecting a subsequent first peak in response to the fact that the first peak does not match the expected peak.

[0018] According to another aspect of an exemplary embodiment, wherein the processor is further configured to reduce a search time interval for detecting a subsequent first peak in response to the fact that the first peak does not match the expected peak, and wherein a duration of the search time interval is determined in response to a vehicle position, a vehicle environment, a weather condition, a user habit and a potential risk level.

[0019] According to another aspect of an exemplary embodiment, wherein the processor is further configured to reduce a search time interval for detecting a subsequent first peak in response to the first peak not matching the expected peak, and wherein a duration of the search time interval is determined in response to a multipath level estimated in response to a transmission of a signal and detection of a reflection of the signal by a vehicle communication system.

[0020] According to another aspect of an exemplary embodiment, wherein the processor is further configured to reduce a search time interval for detecting a subsequent first peak in response to the fact that the first peak does not match the expected peak, and wherein a duration of the search time interval is reduced in response to a length of a pseudorandom spread spectrum time-jump sequence preamble in a time domain.

[0021] According to another aspect of an exemplary embodiment, wherein the first peak value is rejected in response to the fact that the first peak value does not match the expected peak value, and a subsequent peak value is detected between the first peak value and the second peak value, wherein the subsequent peak value is then compared with the expected peak value.

[0022] According to another aspect of an exemplary embodiment, a power difference between the first peak value and the second peak value is used to compare the first peak value with the expected peak value.

[0023] According to another aspect of an exemplary embodiment, wherein the first peak value and the second peak value are part of a pseudorandom spread spectrum time-jump sequence.

[0024] According to another aspect of an exemplary embodiment, wherein the vehicle control algorithm includes unlocking a vehicle and transferring the vehicle between a standby state and an on-powered state.

[0025] According to another aspect of an exemplary embodiment, a vehicle communication system comprises a transmitter with a first memory for storing a common key and for transmitting a pseudorandom spread-spectrum time-jump sequence comprising a first peak and a second peak, wherein the second peak is generated in response to the common key; a second memory for storing first data indicating several properties of an expected peak, wherein the several properties include a time difference between the first peak and the second peak, a first power difference between the first peak and the second peak, and a second power difference between a leading edge of the first peak and a noise threshold; and a receiver for receiving the pseudorandom spread-spectrum time-jump sequence.comprising the first peak and the second peak, a processor configured to authenticate the pseudorandom spread spectrum time-jump sequence in response to the second peak and the common key for determining a distance between the transmitter and the vehicle communication system in response to a difference between a first time of receiving the first peak and a second time of receiving the second peak, initiating a distance determination in response to the first peak matching the expected peak, and a vehicle control configured to activate a vehicle control algorithm in response to the distance and to control a vehicle in response to the vehicle control algorithm.

[0026] According to another aspect of an exemplary embodiment, wherein a search time interval for detecting a subsequent first peak is reduced in response to the fact that the first peak does not match the expected peak, and wherein a duration of the search time interval is determined in response to a vehicle position, a vehicle environment, a weather condition, a user habit, a potential risk level, a length of a pseudorandom spread spectrum time-jump sequence preamble in a time domain, and a multipath level estimated in response to the transmission of a signal and the detection of a reflection of the signal by the vehicle communication system. Brief description of the drawings

[0027] The present disclosure is described below in conjunction with the following drawing figures, where the same reference numerals denote the same elements and where: Fig. 1 a functional block diagram of a vehicle comprising an exemplary system for providing a secure UWB distance measurement system and a vehicle data communication system according to embodiments of the present disclosure; Fig. 2 a block diagram illustrating an exemplary implementation of a system for providing a secure UWB distance measurement system and a motor vehicle data communication system according to an exemplary embodiment of the present disclosure; Fig. 3 an exemplary functional block diagram of a system that provides a secure UWB distance measurement system and a motor vehicle data communication system in a motor vehicle according to embodiments of the present disclosure; and Fig. 4 shows a flowchart illustrating an exemplary implementation of a method for providing a secure UWB distance measurement system and a motor vehicle data communication system according to embodiments of the present disclosure. Detailed description

[0028] The following detailed description is merely exemplary and is not intended to restrict disclosure, filing, or uses thereof. Furthermore, there is no intention to be bound by any theory presented in the preceding background or in the following detailed description.

[0029] In an automotive application where malicious actors attempt to gain access to wireless vehicle communication and security systems, UWB distance measurement can be used to accurately determine distances between objects, such as the vehicle and a transmitter. For example, UWB distance measurement can be used to detect the distance between a vehicle and a key fob used to unlock and start the vehicle. Phantom spike attacks can exploit the unique characteristics of UWB signals; malicious actors can inject false signals, or "phantom spikes," into the communication channel, resulting in inaccurate distance measurements. This can compromise the security and reliability of the system, potentially enabling unauthorized access to restricted areas, location spoofing, and interference with other UWB-based devices.To mitigate these risks, UWB distance measurement systems typically employ a combination of robust signal processing algorithms, cryptographic techniques, and physical countermeasures to identify and filter out false signals, protect the integrity of communications, and reduce vulnerability to phantom spike attacks.

[0030] With reference to Fig. Figure 1 shows an exemplary system 100 for providing a secure UWB distance measurement system and a vehicle data communication system according to various embodiments. The exemplary system 100 comprises a vehicle 10 with a plurality of sensor devices 40a-40n, a drive system 20, a transmission system 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and a communication system 36.

[0031] As in Fig. As shown in Figure 1, the vehicle 10 generally comprises a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is mounted on the chassis 12 and essentially encloses components of the vehicle 10. The body 14 and the chassis 12 can together form a frame. The wheels 16-18 are each rotatably coupled to the chassis 12 near a respective corner of the body 14.

[0032] In various embodiments, the vehicle 10 is an autonomous vehicle, and the control system 100 is integrated into the autonomous vehicle 10 (hereinafter referred to as the autonomous vehicle 10). The autonomous vehicle 10 is, for example, a vehicle that is automatically controlled to transport passengers from one location to another. In the illustrated embodiment, the vehicle 10 is depicted as a passenger car; however, it is understood that any other vehicle, including motorcycles, trucks, all-terrain vehicles (SUVs), recreational vehicles (RVs), watercraft, aircraft, and so on, can also be used. In one exemplary embodiment, the autonomous vehicle 10 is a so-called Level Four or Level Five automation system.A Level Four system indicates "high automation" and refers to the driving-mode-specific performance of an automated driving system in all aspects of the dynamic driving task, even if a human driver does not respond appropriately to a request for intervention. A Level Five system indicates "full automation" and refers to the full-time performance of an automated driving system in all aspects of the dynamic driving task under all road and environmental conditions that can be handled by a human driver. It is understood that the vehicle 10, in various embodiments, can be a non-autonomous vehicle and is not limited to the examples presented here.

[0033] As shown, the vehicle 10 generally comprises a drive system 20, a transmission system 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one control unit 34, and a communication system 36. The drive system 20 may, in various embodiments, comprise an internal combustion engine, an electric machine such as a traction motor, and / or a fuel cell drive system. The transmission system 22 is configured to transmit power from the drive system 20 to the vehicle wheels 16-18 at selectable speed ratios. According to various embodiments, the transmission system 22 may comprise a gear-ratio automatic transmission, a continuously variable transmission, or another suitable transmission. The braking system 26 is configured to provide braking torque to the vehicle wheels 16-18.The braking system 26 can, in various embodiments, comprise friction brakes, brake-by-wire, a regenerative braking system such as an electric machine, and / or other suitable braking systems. The steering system 24 influences the position of the vehicle wheels 16-18. Although shown for illustrative purposes as comprising a steering wheel, the steering system 24 may not include a steering wheel in some embodiments considered within the scope of this disclosure.

[0034] The sensor system 28 comprises one or more sensor devices 40a-40n that detect observable conditions of the external environment and / or the internal environment of the autonomous vehicle 10. The sensor devices 40a-40n may include, among others, radars, lidar, global positioning systems, optical cameras, thermal cameras, ultrasonic sensors and / or other sensors.

[0035] In various embodiments, the sensor devices 40a-40n are arranged at different locations on the vehicle 10. In exemplary embodiments described herein, one or more of the sensor devices 40-40n are implemented as lidar devices. In this respect, each of the sensor devices 40a-40n can include or integrate one or more lasers, scanning components, optical arrangements, photodetectors, and other components that are suitably configured to scan the environment near the vehicle 10 horizontally and rotatably at a specific angular frequency or rotational speed. In exemplary embodiments described herein, one or more of the sensor devices 40a-40n are implemented as optical cameras configured to capture images of the environment near the vehicle 10.

[0036] The actuator system 30 comprises one or more actuator devices 42a-42n that control one or more vehicle features, such as, among others, the drive system 20, the transmission system 22, the steering system 24, and the braking system 26. In various embodiments, the vehicle features may further include interior and / or exterior vehicle features, such as, among others, doors, a trunk, and cabin features such as air, music, lighting, and so on (not numbered).

[0037] With further reference to Fig. In exemplary embodiments, the communication system 36 is configured to wirelessly communicate information to and from other entities 48, such as, among others, other vehicles (“V2V” communication), infrastructure (“V21” communication), remote systems, personal devices, and / or calibration stations. In one exemplary embodiment, the communication system 36 is a wireless communication system configured to communicate via a wireless local area network (WLAN) using IEEE 802.11 standards or using cellular data communication. However, additional or alternative communication methods, such as a dedicated short-range communication (DSRC) channel, are also considered within the scope of this disclosure.DSRC channels refer to one-way or two-way short-range to medium-range wireless communication channels specifically designed for automotive use and a corresponding set of protocols and standards.

[0038] The data storage device 32 stores data for use in the automatic control of the autonomous vehicle 10. In various embodiments, the data storage device 32 stores defined maps of the navigable environment. In various embodiments, the defined maps can be predefined by and received from a remote system. For example, the defined maps can be compiled by the remote system and communicated to the autonomous vehicle 10 (wirelessly and / or via a wired connection) and stored in the data storage device 32. In various embodiments, the data storage device 32 stores calibrations for use in aligning the sensor devices 40a-40n. In various embodiments, one or more of the calibrations are estimated as extrinsic parameters using the methods and systems described herein.It is understood that the data storage device 32 can be part of the controller 34, separate from the controller 34, or part of the controller 34 and part of a separate system.

[0039] The controller 34 comprises at least one processor 44 and a computer-readable storage device or computer-readable storage medium 46. The processor 44 can be any custom or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among several processors associated with the controller 34, a microprocessor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, any combination thereof, or generally any device for executing instructions. The computer-readable storage device or computer-readable storage medium 46 can, for example, include volatile and non-volatile storage in read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM).KAM is a persistent or non-volatile memory that can be used to store various operating variables while the processor 44 is switched off. The computer-readable storage device or computer-readable storage medium 46 can be implemented using any number of known storage devices, such as PROMs (programmable read-only memory), EPROMs (electrical PROMs), EEPROMs (electrically erasable PROMs), flash memory, or any other electrical, magnetic, optical, or combination storage devices capable of storing data, some of which are executable instructions used by the controller 34 in controlling the autonomous vehicle 10.

[0040] The instructions can comprise one or more separate programs, each containing an ordered list of executable instructions for implementing logical functions. When executed by the processor 44, the instructions receive and process signals from the sensor system 28, perform logic, calculations, procedures, and / or algorithms to automatically control the components of the autonomous vehicle 10, and generate control signals to the actuator system 30 to automatically control the components of the autonomous vehicle 10 based on the logic, calculations, procedures, and / or algorithms. Although in Fig. While only one controller 34 is shown in Figure 1, embodiments of the autonomous vehicle 10 may include any number of controllers 34 that communicate via any suitable communication medium or combination of communication media and that interact to process the sensor signals, perform logic, calculations, procedures and / or algorithms, and generate control signals to automatically control features of the autonomous vehicle 10. In various embodiments, one or more instructions from the controller 34 are embodied in the control system 100 and, when executed by the processor 44, cause the processor 44 to perform the procedures and systems that dynamically align the sensor devices by updating calibrations stored in the data storage device 32, as described in more detail below.

[0041] According to various embodiments, the controller 34 implements an autonomous driving system (ADS). Software and / or hardware components of the controller 34 (for example, processor 44 and computer-readable storage device 46) are used to provide an autonomous driving system that, in conjunction with the vehicle 10, is used, for example, to automatically control various actuators 30 on board the vehicle 10, thereby controlling vehicle acceleration, steering, and braking without human intervention.

[0042] In various embodiments, the instructions of the autonomous driving system 70 can be organized according to function or system. For example, the autonomous driving system can comprise a computer vision system, a positioning system, a guidance system 78, and a vehicle control system 80. It is understood that in various embodiments, the instructions can be organized into any number of systems (for example, combined, further subdivided, and so on), since the disclosure is not limited to the present examples.

[0043] In various embodiments, the computer vision system 74 synthesizes and processes sensor data and predicts the presence, location, classification, and / or path of objects and features in the vehicle's environment 10. In various embodiments, the computer vision system 74 can include information from multiple sensors, including, but not limited to, cameras, lidar, radar, and / or any number of other sensor types. In various embodiments, the computer vision system 74 receives information from the control system 100 described herein and / or processes it.

[0044] The positioning system 76 processes sensor data together with other data to determine a position (for example, a local position relative to a map, a precise position relative to the lane of a road, the vehicle's course, speed, and so on) of the vehicle 10 in relation to its environment. The guidance system 78 processes sensor data together with other data to determine a path for the vehicle 10 to follow. The vehicle control system 80 generates control signals to steer the vehicle 10 according to the determined path.

[0045] In various embodiments, the controller 34 implements machine learning techniques to support the functionality of the controller 34, such as feature recognition / classification, obstacle mitigation, route crossing, mapping, sensor integration, ground truth determination, and the like.

[0046] According to some exemplary embodiments, the control system generally shown at 100 is associated with a vehicle 10. In general, the control system 100 selectively aligns two sensors of the vehicle 10 by estimating extrinsic parameters. As discussed in more detail, the estimation is based on a procedure that uses a mathematical optimization problem when a group of lidar-camera control points with a highly flexible 3D-2D agreement requirement is given. In various embodiments, the two sensors comprise a lidar sensor and a camera sensor. It is understood that other sensors can be implemented in different embodiments.

[0047] According to some exemplary embodiments, the plurality of sensor devices 40a - 40n, the drive system 20, the transmission system 22, the steering system 24, the brake system 26, the sensor system 28, the actuator system 30, the at least one data storage device 32, the at least one control 34 and the communication system 36 are communicatively coupled to transmit data to each other.

[0048] With reference to Fig. Figure 2 shows a block diagram illustrating an exemplary implementation of a secure UWB distance measurement system 200 and a vehicle data communication system. The exemplary UWB system 200 may include an antenna 215, a transceiver 210, a UWB demodulator / modulator 220, a processor 230, and a vehicle controller 240. In some exemplary embodiments, the antenna 215 may be an antenna array or the like. In some exemplary embodiments, UWB anchors are stationary devices that use multiple antennas to locate other UWB devices, which are referred to as tags.The exemplary UWB system 200 can be used to proactively identify and mitigate potential HRP-UWB range reduction attacks, such as the ghost spike attack, by learning the characteristic signal shape, power distribution, and interference levels associated with various environmental conditions, including open and closed environments, weather patterns, and individual user behavior, in order to detect anomalies that deviate from the expected patterns, indicating potential range reduction attacks.Although the present description is in the context of automotive applications, secure distance and distance reduction attacks can apply in other areas, such as in the context of smart homes, where certain functions are activated based on the proximity of a phone, for example, to unlock a front door or garage door, turn on the AC or a light, or even to locate a lost or misplaced electronic device within a house or building. The mitigations we present in this disclosure are relevant for UWB-based distance measurement regardless of the application domain, automotive or non-automotive. Under certain conditions, low-rate pulse repetition (LRP) can also be vulnerable to the ghost peak value attack, although LRP generally offers more security protection than high-rate pulse repetition (HRP).In this respect, the mitigations described here focus on the less secure HRP in connection with vehicle access applications, but could also apply to the LRP.

[0049] The UWB signal can be sent and received via a transmitter, such as a key fob or mobile device. Due to its high data rate, low power consumption, and ability to penetrate obstacles, UWB has become a popular choice for vehicle key fobs and mobile phone security applications. However, UWB can be vulnerable to distance reduction attacks, a type of security threat specifically targeting UWB communication systems. By intercepting and manipulating the transmitted UWB signal, malicious actors can introduce delays that cause the receiver to miscalculate the distance between itself and the sender, potentially leading to unauthorized access to restricted areas or devices, as well as other security risks.

[0050] In a UWB system 200, a transmitting device, such as the transmitter 205 or the transceiver 210 in the vehicle 212, can generate a short, wide pulse several times per second to conserve battery power and reduce interference with other devices. The pulse travels through the air and reaches the antenna 215 on the vehicle 212. The received signal can then be amplified, filtered, and demodulated to extract the transmitted data. The system can accurately measure the propagation time it takes for the signal to travel from the key fob to the vehicle. This information is used to calculate the distance between the two devices.

[0051] To determine the propagation time, transmitter 205 first generates a scrambled timestamp sequence (STS) based on a shared key in a specific timeslot to verify the transmitter's identity and prevent unauthorized access. An STS is an encrypted sequence that ensures the accuracy and integrity of distance measurement timestamps. It is a key feature of the IEEE 802.15.4z standard, which enhances data integrity and provides resilience against UWB distance measurement attacks. UWB frame transmission can be performed over a set of frequency channels used by the transmitter and receiver according to the pseudorandom hopping sequence. Typically, once authenticated, transmitter 205 is authorized to unlock vehicle 212 or perform other functions.STS sequences comprise a code used in spread-spectrum communication systems that involve hopping a narrowband signal across a wide range of frequencies in a pseudorandom manner to improve resistance to interference and jamming. The UWB signal can include a front peak of 209 and a mid peak of 208. The front peak of 209 is used for detection and synchronization. It establishes a time reference for the receiver, allowing it to accurately decode the incoming signal, and can also be used for rangefinding, determining the distance between the transmitter and receiver. The Y-axis of the graph of... Fig. 2 corresponds to the cross-correlation between the received signal and the expected signal. The middle peak 208, the highest peak, corresponds to the cross-correlation of the signal obtained from multipath reflections. The leading peak 209 corresponds to the cross-correlation of the signal obtained from the direct path. This is also the peak relevant for distance measurement, as the travel time via the direct path is measured. The middle peak carries the transmitted data and is modulated to carry the transmitted data. Common modulation techniques can include peak amplitude modulation (PAM) and peak position modulation (PPM).

[0052] In UWB distance measurement systems, once the maximum peak value from the multipath signal is detected, the received signal uses the lookup window to search for a smaller leading-edge peak value that would correspond to the direct path. Once the smaller leading-edge peak value is found, its position on the time axis provides a timestamp that can be used to estimate the propagation time and thus the distance. The preamble is a specific sequence of peak values ​​or symbols that precedes the actual data transmission. Its primary purpose is to establish synchronization and to estimate transmission channel characteristics, such as attenuation and multipath effects. The leading-edge peak value is often the first peak value or symbol within the preamble.It serves as a strong reference signal for the receiver to detect the presence of the signal and to estimate the time offset between a local clock and the incoming signal. The mid-peak 208 is typically a peak value or symbol within the preamble, following the front peak 209. The mid-peak 208 can be used to provide additional information about channel characteristics, particularly for multipath channels and for fine-tuning time synchronization. The preamble, the front peak 208, and the mid-peak 209 work together to ensure accurate distance measurement in UWB systems. By establishing synchronization, estimating channel characteristics, and capturing the signal, these components enable the receiver to accurately measure the propagation delay and determine the distance to the transmitter.

[0053] For distance measurement, the UWB demodulator 220 is used to demodulate the mid-peak value 208 in order to verify the identity of the transmitter 205. The processor 230 can then perform a backsearch within a search window on the received signal waveform to determine a time interval between the mid-peak value 208 and the leading peak value 209. This time interval is used to determine a distance between the transmitter 205 and the vehicle 212, with the earlier arrival time indicating a shorter distance between the transmitter 205 and the vehicle 212.

[0054] In a phantom attack, a malicious actor sends UWB signals that cause the phantom peak 206 (on the cross-correlation axis) at a time prior to the legitimate peak 209. If the phantom peak 206 falls within the search window, a vulnerable system may mistake it for the leading peak 209. Because this phantom peak 206 is transmitted earlier than the leading peak 209, a vulnerable system would use the phantom peak 206 to determine the distance from transmitter 205 to vehicle 212, assuming that transmitter 205 is much closer to vehicle 212 than the actual transmitter 205. This perceived proximity could then cause the vulnerable system to allow the vehicle to be unlocked or perform other secure actions.

[0055] To mitigate the UWB system against phantom attacks, the exemplary System 200 can be configured to employ an AI algorithm with innovative techniques to proactively identify, detect, and mitigate potential HRP-UWB range reduction attacks, including phantom peak attacks. By analyzing received signals, the algorithm learns the expected waveform, power pattern, and noise levels associated with various factors, including location, weather conditions, and individual user behavior. The algorithm then detects deviations from these expected patterns and flags any discrepancies that may indicate an attack. Key parameters examined include the time and power differences between the highest peak and the leading edge, as well as the power difference between the leading edge and the noise threshold.Any significant deviations from the established standards trigger a security alert indicating a potential range reduction attack. This advanced approach ensures robust security and safeguards against the evolving threats arising from these types of attacks.

[0056] The security flag initiates the algorithm's proactive response to potential HRP-UWB range reduction attacks. Upon detection, the algorithm employs strategic countermeasures, such as skipping the initial suspicious leading edge and moving on to the next, or narrowing the search window to half its default size. After the range measurement session, the algorithm validates its detection results through user actions or input and uses this feedback to refine its model. The acquired knowledge is then shared with a centralized AI server, which updates the model using aggregated training data to improve its overall effectiveness in mitigating HRP-UWB range reduction attacks.

[0057] The AI ​​algorithm employs a dynamic and proactive approach to mitigate HRP-UWB distance reduction attacks. By continuously monitoring environmental factors such as location, weather, user behavior, and potential risk levels, the algorithm can adjust parameters like the search window width and noise threshold in real time. This adaptive strategy helps prevent attackers from exploiting vulnerabilities and ensures the integrity of distance measurements. To assess multipath interference, the algorithm actively emits signals and analyzes reflections, allowing it to refine its mitigation techniques accordingly.

[0058] The advanced AI system employs an algorithm that utilizes novel techniques to proactively detect and mitigate potential HRP-UWB range reduction attacks, such as the phantom peak attack. By analyzing signal patterns, identifying anomalies, and leveraging user-specific data, threats can be identified, and system parameters can be dynamically adjusted to prevent unauthorized access. Post-incident assessments further enhance threat detection capabilities and ensure continuous improvement and adaptation to evolving security challenges. A collaborative learning approach allows shared data across multiple vehicles to improve the system's overall resilience and adaptability. This enables the AI ​​algorithm to identify emerging threats and develop countermeasures more efficiently.Furthermore, the AI ​​system's ability to dynamically adjust the search window and noise threshold ensures optimal performance under various environmental conditions and further strengthens our security posture.

[0059] With reference to Fig. Figure 3 shows a functional block diagram illustrating an exemplary implementation of a secure UWB distance measurement system 300 and a vehicle data communication system according to embodiments of the present disclosure. The diagram illustrates an amplification-based AI algorithm designed to improve the security of UWB distance measurement systems against phantom attacks. This agent-based approach enables the vehicle to dynamically adapt its response strategies to evolving threat scenarios.

[0060] Phantom attacks using UWB distance spoofing pose a significant threat to the security of UWB systems, exploiting vulnerabilities in the HRP layer to manipulate distance measurements. These attacks, which include the introduction of false signals or the manipulation of legitimate signals, can lead to unauthorized access or compromised security. To counter these threats, an enhanced machine learning algorithm is provided, employing advanced techniques to analyze signal characteristics, detect anomalies, and dynamically adjust system parameters. By proactively identifying and mitigating potential attacks, the integrity and security of UWB-based applications are ensured.

[0061] Vehicle 305 continuously observes signal phase, distance, and / or amplitude 315 and identifies deviations from the expected baseline as potential threats. Based on the observed data, policy 310 determines the optimal attenuation strategy. This policy 310 is learned by a reinforcement learning algorithm 320. The observations 315 are also supplied to the reinforcement learning algorithms 320. The reinforcement learning algorithm iteratively refines the policy based on the results of actions, ensuring continuous improvement. Learning updates are supplied to policy 310 based on the updated forced learning algorithms 320.

[0062] In response to policy 310, the vehicle control system then reacts to the detected threat by initiating appropriate device-vehicle distance measurement threat mitigation measures. Environment 330 represents the external factors that affect the signal and effectiveness of the mitigation measures, including noise, interference, and potential phantom attacks. In response to Environment 330 and Action 350, a reward function evaluates the effectiveness of the vehicle's response based on the reaction time and the phase of the peaks. Timely threat mitigation and accurate phase alignment are prioritized. Through this iterative process, the algorithm learns the optimal policy by interacting with the environment, receiving rewards for successful actions, and incurring penalties for unsuccessful ones.By employing this AI-driven approach, the vehicle can effectively learn from its experiences, adapt to changing threat landscapes, and proactively mitigate phantom attacks, thereby ensuring the integrity and security of UWB rangefinding systems.

[0063] With reference to Fig. Figure 4 shows a flowchart illustrating an exemplary implementation of Method 400 for a secure UWB distance measurement system and a vehicle data communication system. Method 400 is initially effective in storing data 405 that represent properties of an STS peak value from an authenticated transmitter. These properties may include the time difference and power difference between the highest peak value and the leading edge, the power difference between the leading edge and the noise threshold, the shape, maximum amplitude, and power distribution of the leading peak value.

[0064] Method 400 is next effective in receiving an STS sequence from a remote transmitter 410. Method 400 decodes 415 the STS peak value and determines whether the transmitter is an authenticated transmitter. In some exemplary embodiments, the transmitter may be authenticated in response to a common key or the like. If the transmitter is not authenticated 420, Method 400 rejects the STS sequence 460 and returns to receiving a subsequent STS sequence 410.

[0065] If the STS sequence is successfully authenticated (420), procedure (430) compares the leading edge peak of the received STS sequence with the stored leading edge peak. In some exemplary embodiments, the receiver can perform the comparison by cross-correlating the received signal and the expected signal. If the cross-correlation value is high, such as above a certain threshold, this indicates the maximum peak corresponding to the multipath signal. If the cross-correlation does not reach the threshold, this means that the received STS is incorrect or contains too many errors. Once the maximum peak is found, the receiver can use the backsearch window to search for the leading edge peak corresponding to the direct path.Method 400 can compare the peak shape, the differences in signal strength between the leading peak and the middle peak, the power distribution, and / or the power difference between the leading edge and the noise threshold. If the first peak matches the stored first peak 440, Method 400 then determines a distance 445 between the transmitter and the vehicle in response to a timing adjustment of the leading peak. For example, the method can store the received sequence, authenticate the STS sequence in response to data transmitted in the middle peak, and then calculate the time difference between the arrival of the first peak and the middle peak in the sequence. This time difference is directly proportional to the distance between the transmitter and the receiver.In response to the detected time and transmitter authentication, procedure 400 then authorizes 445 actions based on distance, such as unlocking the vehicle, remote starting, transitioning from standby to driving mode, and the like. Procedure 400 can then update the front peak value stored in memory in response to the newly received front peak value. Procedure 405 then stores the updated front peak value in memory for further distance measurement system security.

[0066] If the leading peak and the stored leading peak do not match 440, the method can then take countermeasures to detect a leading peak from the authenticated transmitter. In some exemplary embodiments, the method 400 can reduce the lookback time window 450 for detecting the leading peak. Thus, the method 400 looks back only a reduced time from the time of the authenticated mid-peak to detect the leading peak. This window can be repeatedly reduced until a matching leading peak is detected. When a new matching leading peak is detected 455, the method 400 then determines the distance between the transmitter and the vehicle and authorizes actions based on this distance.If a new matching peak value is not detected 455, the procedure 400 can then reject the STS sequence 460 and return to receiving a next STS sequence 410.

[0067] In some exemplary embodiments, if it is determined that the initial front peak is not a match 440, the method 400 can employ pattern recognition and self-learning, such as from signal shape, integrity, and properties associated with unique location / events / user-specific characteristics, to detect a match of the front peak with the stored front peak. The method 400 can utilize location and site characteristics, such as open sky, dense urban area, enclosed space, weather conditions, user habits, and potential risk for predicted multipath signals and possible threat conditions. In some exemplary embodiments, the lookback window depends on the maximum delay between the multipath signal and the direct path signal.This delay, in turn, depends on the environment in which the measurement is taken, such as open sky, dense urban area, weather conditions, temperature, humidity, and so on. The optimal width of the search window for each of these cases can be learned by an AI model and adjusted to the correct / optimal value by the vehicle and the key fob / phone, or more generally, the transmitter and receiver, during a distance measurement session. The Procedure 400 can employ threat detection, such as detecting threats using the training set and inconsistencies with patterns specifically designed for user / location / time of day / weather conditions, and threat mitigation by dynamically adjusting system parameters to defend against attacks. Threat assessment is achieved by using post-incident actions to determine whether a threat was credible.Additionally, the Method 400 can employ collaborative learning or crowdsourcing to upload threat data to a centralized database accessible to all vehicles. The Method 400 can proactively and dynamically adjust the width of the search window, modify the noise threshold, and sensing the environment and environmental conditions to determine optimal / safer system parameters. The Method 400 can proactively and dynamically adjust the noise threshold based on factors such as the extent of fading and multipath interference. In some exemplary embodiments, the Method 400 can estimate a level of multipath interference by sensing its environment, emitting a signal, and measuring the reflected signal characteristics.

[0068] While at least one exemplary embodiment has been presented in the foregoing detailed description, it is understood that a large number of variations exist. It is also understood that the exemplary embodiment or embodiments are merely examples and are not intended to limit the scope, applicability, or configuration of this disclosure in any way. Rather, the foregoing detailed description provides those skilled in the field with a suitable roadmap for implementing the exemplary embodiment or embodiments. It is understood that various modifications to the function and arrangement of elements can be made without deviating from the scope of the disclosure as set forth in the appended claims and their legal equivalents.

Claims

[1] Method for determining a distance between a sender and a receiver, comprising: Storing, in a memory, initial data that indicates the characteristics of an expected peak value; Receiving, by the receiver, a data sequence that includes a first peak and a second peak; Authentication, by a processor that processes the data sequence in response to second data received in the second peak value; Determine, by the processor, the distance between the transmitter and the receiver in response to a difference between a first time of receiving the first peak and a second time of receiving the second peak, initiating a determination of the distance in response to the first peak matching the expected peak; and Activate, by a vehicle control system, a vehicle control algorithm in response to distance. [2] Method for determining the distance between the transmitter and the receiver according to claim 1, wherein a search time interval is reduced to detect a subsequent first peak value in response to the fact that the first peak value does not match the expected peak value. [3] Method for determining the distance between the transmitter and the receiver according to claim 1, wherein the first peak value and the second peak value are part of a pseudorandom spread spectrum time-jump sequence. [4] Method for determining the distance between the transmitter and the receiver according to claim 1, wherein the vehicle control algorithm comprises unlocking a vehicle and transferring the vehicle between a standby state and an on state. [5] Method for determining the distance between the transmitter and the receiver according to claim 1, wherein a search time interval for detecting a subsequent first peak value is reduced in response to the fact that the first peak value does not match the expected peak value, and wherein a duration of the search time interval is determined in response to a vehicle position, a vehicle environment, a weather condition, a user habit and a potential risk level. [6] Method for determining the distance between the transmitter and the receiver according to claim 1, wherein a search time interval for detecting a subsequent first peak value is reduced in response to the fact that the first peak value does not match the expected peak value, and wherein a duration of the search time interval is determined in response to a multipath level estimated in response to a transmission of a signal and a detection of a reflection of the signal by a vehicle communication system. [7] Method for determining the distance between the transmitter and the receiver according to claim 1, wherein a search time interval for detecting a subsequent first peak value is reduced in response to the fact that the first peak value does not match the expected peak value, and wherein a duration of the search time interval is reduced in response to a length of a pseudorandom spread spectrum time-jump sequence preamble in a time domain. [8] Method for determining the distance between the transmitter and the receiver according to claim 1, wherein the first peak value is rejected in response to the fact that the first peak value does not match the expected peak value, and a subsequent peak value is detected between the first peak value and the second peak value, wherein the subsequent peak value is then compared with the expected peak value. [9] Method for determining the distance between the transmitter and the receiver according to claim 1, wherein a power difference between the first peak value and the second peak value is used to compare the first peak value with the expected peak value. [10] Device for determining a distance between a transmitter and a receiver, comprising: a memory configured to store initial data, displaying the properties of an expected peak value; wherein the receiver is configured by the sender to receive a data sequence comprising a first peak value and a second peak value; a processor configured to authenticate the data sequence in response to second data received in the second peak, to determine the distance between the sender and the receiver in response to a difference between a first time of receiving the first peak and a second time of receiving the second peak, initiating a distance determination in response to the first peak matching the expected peak, and to generate a control signal in response to the distance; and a vehicle control unit for activating a vehicle control algorithm in response to the control signal; the pseudorandom spread spectrum time-jump sequence in response to the second peak and the common key for determining a distance between the transmitter and the vehicle communication system in response to a difference between a first time of reception of the first peak and a second time of reception of the second peak, wherein a determination of the distance is initiated in response to the first peak matching the expected peak; and a vehicle control system configured to activate a vehicle control algorithm in response to distance and to control a vehicle in response to the vehicle control algorithm.

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