Systems and methods for velocity estimation using smart reflective surfaces and media

Abstract

Velocity estimation using smart reflective surfaces (e.g., using computerized tools) is achieved. For example, a system may include: at least one processor; and at least one memory storing executable instructions that, when executed by the at least one processor, cause execution of operations. The operations may include determining a second signal between a receiver and a target object based on a first signal between the receiver and the target object, where the first signal includes a direct signal, and where the second signal includes an indirect signal transmitted via a smart reflective surface; determining the Doppler frequency of the second signal; and determining a velocity estimate of the target object based on the Doppler frequency.

Description

[0001] Cross - reference to related applications

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 604,907, filed on December 1, 2023, and entitled "SYSTEM AND METHOD FOR VELOCITY ESTIMATION USING MULTIPLE INTELLIGENT REFLECTING SURFACES", which is incorporated herein by reference in its entirety. Field of the Invention

[0003] This application relates to the field of velocity estimation, and more particularly to systems and methods for velocity estimation using intelligent reflecting surfaces and corresponding non - transitory machine - readable media. Background Art

[0004] Velocity information (e.g., of a vehicle) has many applications, such as in vehicle - to - vehicle communication and intelligent traffic management. For example, by accurately estimating the velocity of a vehicle (and / or other moving objects such as pedestrians or cyclists), connected vehicles can anticipate potential collision risks and take preventive measures in real - time. Additionally, velocity information plays a role in traffic management. By collecting and analyzing real - time velocity data from multiple vehicles, traffic authorities can gain valuable insights into traffic patterns, congestion hotspots, and overall traffic flow. For this purpose, integrated sensing and communication (ISAC) provides a platform for estimating velocity via cellular networks. However, for example, due to the limited information provided by the target - base - station link, traditional single - static sensing systems cannot accurately estimate velocity. Moreover, due to complex environments, the direct link between the sensing node and the target may be blocked or may become blocked, thus hindering the estimation of velocity.

[0005] The background related to velocity estimation described above is only intended to provide a general overview of some current problems and is not intended to be exhaustive. Other context information may become more apparent after reviewing the following detailed description. Summary of the Invention

[0006] According to a first aspect of the present application, there is provided a system, comprising: at least one processor; and at least one memory storing executable instructions that, when executed by the at least one processor, facilitate the performance of operations, the operations including: determining a second signal between a receiver and a target object based on a first signal between the receiver and the target object, wherein the first signal includes a direct signal, and wherein the second signal includes an indirect signal transmitted via an intelligent reflecting surface; determining a Doppler frequency of the second signal; and determining an estimated velocity of the target object based on the Doppler frequency.

[0007] According to a second aspect of the present application, there is provided a non-transitory machine-readable medium comprising executable instructions that, when executed by at least one processor, facilitate the performance of operations, the operations including: determining a second link between a base station and a target object based on a first link between the base station and the target object, wherein the first link includes a direct link, and wherein the second link includes an indirect link transmitted via an intelligent reflecting surface; determining a Doppler frequency of the second link; and determining an estimated velocity of the target object based on the Doppler frequency.

[0008] According to a third aspect of the present application, there is provided a method, comprising: determining, by a system including at least one processor, a second communication between a receiver device and a target object device based on a first communication between the receiver device and the target object device, wherein the first communication includes a direct communication, and wherein the second communication includes an indirect communication transmitted via an intelligent reflecting surface; determining, by the system, a Doppler frequency of the second communication; and calculating, by the system, an estimated velocity of the target object device based on the Doppler frequency. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a block diagram of a non-limiting example system in accordance with one or more example embodiments described herein.

[0010] Figure 2 is a block diagram of a non-limiting example computer-executable module in accordance with one or more example embodiments described herein.

[0011] Figure 3 is a schematic diagram of an example intelligent reflecting surface-assisted vehicle-to-infrastructure system in accordance with one or more example embodiments described herein.

[0012] Figure 4Schematic diagram of an example intelligent reflecting surface assisted vehicle-to-vehicle system according to one or more example embodiments described herein.

[0013] Figure 5 Schematic diagram of an example intelligent reflecting surface assisted indoor integrated sensing and communication system according to one or more example embodiments described herein.

[0014] Figure 6a and Figure 6b Illustrates the Doppler effect on different links according to one or more example embodiments described herein.

[0015] Figure 7 Shows the normalized root mean square error as a function of target speed at different signal-to-noise ratios according to one or more example embodiments described herein.

[0016] Figure 8 Shows the root mean square error performance of direct and indirect methods where the target moves at different speeds according to one or more example embodiments described herein.

[0017] Figure 9 Flowchart of a process associated with speed estimation using an intelligent reflecting surface according to one or more example embodiments described herein.

[0018] Figure 10 Flowchart of a process associated with speed estimation using an intelligent reflecting surface according to one or more example embodiments described herein.

[0019] Figure 11 Flowchart of a process associated with speed estimation using an intelligent reflecting surface according to one or more example embodiments described herein.

[0020] Figure 12 An example non-limiting computing environment in which one or more embodiments described herein may be implemented.

[0021] Figure 13 An example non-limiting network environment in which one or more embodiments described herein may be implemented. Detailed Description

[0022] The present disclosure is now described with reference to the accompanying drawings, in which like reference numerals are used throughout to refer to like elements. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be apparent, however, that the present disclosure may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing the present disclosure.

[0023] As mentioned above, speed estimation can be improved in various ways, and various example embodiments are described herein for this and / or other purposes.

[0024] Speed estimation has many applications and uses. By analyzing the Doppler shift of the observed signal, the speed of a mobile device (e.g., user equipment) or a target object can be estimated. However, traditional speed estimation methods can only estimate the radial projection of the speed on the line connecting the receiver (e.g., base station (BS)) and the target, which results in significant estimation errors. In addition, due to the complex environment, the direct link between the target and the receiver (e.g., BS) may be blocked.

[0025] For this and various other purposes, intelligent reflecting surfaces (IRSs) can be used in the various embodiments described herein to create additional links between the target and the receiver, thus enabling the solution to overcome the challenges brought by the complex environment and improving the connectivity between the BS and the target. The IRSs herein can include, for example, passive metasurfaces having the ability to reflect incident signals without using a dedicated energy source. The embodiments herein utilize the IRSs to create additional links for accurate estimation of the speed of the target object. The embodiments herein also enable the process to estimate the speed of a maneuvering target with the assistance of the IRS, for example, in scenarios where the direct link may not exist. The embodiments herein implement two effective speed estimation processes by utilizing the additional link(s) created by the IRS herein. In various embodiments, the speed can thus be accurately estimated.

[0026] According to an example embodiment, a system may include: at least one processor; and at least one memory storing executable instructions that, when executed by the processor, cause operations to be performed, the operations including: determining a second signal between the receiver and the target object based on a first signal between the receiver and the target object, wherein the first signal includes a direct signal, and wherein the second signal includes an indirect signal transmitted via an intelligent reflecting surface; determining the Doppler frequency of the second signal; and determining an estimated speed of the target object based on the Doppler frequency.

[0027] In one or more example embodiments, the second signal may be determined in response to determining that the first signal is blocked by an intermediate object located between the receiver and the target object.

[0028] In one or more example embodiments, the second signal may be determined to not be blocked by an intermediate object located between the receiver and the target object.

[0029] In one or more example embodiments, the speed estimate of the target object may be directly determined via: a linear search of the amplitude of the first signal or the second signal and the angle of the speed of the first signal or the second signal by a defined matched filter.

[0030] In one or more example embodiments, the speed estimate of the target object may be indirectly determined via: a matched filter for estimating the Doppler frequency of the first signal or the second signal with respect to the first signal or the second signal; and a linear search of the amplitude of the first signal or the second signal and the angle of the speed of the first signal or the second signal by a defined matched filter.

[0031] In one or more example embodiments, the Doppler frequency of the second signal may be a first Doppler frequency, where the intelligent reflecting surface is a first intelligent reflecting surface, and where the operation further includes: determining a third signal between the receiver and the target object, where the third signal includes an indirect signal transmitted via a second intelligent reflecting surface; and determining a second Doppler frequency of the third signal, where the speed estimate is further determined based on the second Doppler frequency of the third signal.

[0032] In one or more example embodiments, the above operation may further include: determining the amount of the intelligent reflecting surface assisted signal associated with determining the speed estimate of the target object with a threshold accuracy.

[0033] In one or more example embodiments, the target object may be determined to be in motion.

[0034] In one or more example embodiments, the above operation may further include: determining the geometric relationship between the target object, the intelligent reflecting surface, and the receiver, where the speed estimate of the target object is further determined based on the geometric relationship.

[0035] In another example embodiment, a non - transitory machine - readable medium may include executable instructions that, when executed by a processor, cause an operation to be performed, the operation including: determining a second link between a base station and a target object based on a first link between the base station and the target object, where the first link includes a direct link, and where the second link includes an indirect link transmitted via an intelligent reflecting surface; determining the Doppler frequency of the second link; and determining a speed estimate of the target object based on the Doppler frequency.

[0036] In one or more example embodiments, the first link may include an uplink signal.

[0037] In one or more example embodiments, the base station may include a radar receiver.

[0038] In one or more example embodiments, the intelligent reflecting surface may be selected from a group of intelligent reflecting surfaces that relay a link between the base station and the target object.

[0039] In one or more example embodiments, the first link or the second link may include a cellular signal transmitted via a cellular network. In this regard, the cellular network may include a fifth-generation cellular network or a sixth-generation cellular network.

[0040] In one or more example embodiments, the first link is determined to be blocked by a stationary object.

[0041] In one or more example embodiments, the first link may be determined to be blocked by a moving object.

[0042] In yet another example embodiment, a method may include: determining, by a system including at least one processor, a second communication between a receiver device and a target object device based on a first communication between the receiver device and the target object device, wherein the first communication includes direct communication, and wherein the second communication includes indirect communication transmitted via an intelligent reflecting surface; determining, by the system, a Doppler frequency of the second communication; and calculating, by the system, a speed estimate of the target object device based on the Doppler frequency.

[0043] In one or more example embodiments, calculating the speed estimate of the target object device may include: directly determining the speed estimate based on a result of a linear search of an angle of a speed based on the first communication or the second communication by a defined matched filter.

[0044] In one or more example embodiments, the Doppler frequency of the second communication may be a first Doppler frequency, and calculating the speed estimate of the target object device may include indirectly determining the speed estimate: estimating a second Doppler frequency of the first communication or the first Doppler frequency of the second communication using a matched filter with respect to the first communication or the second communication; and using a result of a linear search based on an amplitude of the first communication or the second communication.

[0045] For example, velocity estimation can be performed by analyzing the Doppler shift. The Doppler effect refers to the change in the frequency of an electromagnetic wave caused by the relative motion between an observer and a wave source, and is widely used for velocity estimation. Traditional velocity estimation mainly uses a matched filter to estimate the motion parameters. Micro-Doppler frequency estimation can be used in an orthogonal frequency division multiplexing (OFDM) radar system. Oversampling can be utilized to mitigate the inter-carrier interference caused by the Doppler effect to improve the performance of Doppler estimation. Generally, the performance of the method based on the matched filter (MF) is limited by the inherent grating problem. To solve this problem, joint range-velocity estimation based on the multiple signal classification process can be used in the OFDM radar system, which achieves a higher resolution than the MF-based method. However, due to the nature of the Doppler effect, traditional single-static ISAC BSs can only measure the radial projection of the velocity, resulting in a large estimation error. One solution is to obtain another perspective towards a maneuvering target so that the velocity can be accurately determined.

[0046] Intelligent reflecting surface (IRS) can enhance the performance of both communication and sensing systems. A continuous model for IRS-aided satellite communication can be utilized. For example, based on this model, the IRS phase shifters are optimized to simultaneously maximize the received power and minimize the delay and Doppler spread. For example, a two-stage protocol for channel estimation can be used in an IRS-aided high-mobility communication system, where the IRS is deployed at a high-speed vehicle, for example, to mitigate the Doppler effect. For example, by considering the user equipment (UE) mobility and the spatial broadband effect, the positioning problem in an IRS-aided single-input single-output (SISO) system can be considered. However, the potential of IRS for velocity estimation has not been understood previously. There are two main benefits of using IRS in velocity estimation. First, IRS can be used to create additional links to estimate the velocity of a moving target. Second, even when the direct target BS link is blocked, multiple IRSs can facilitate the estimation of the actual velocity.

[0047] Traditional single-static ISAC systems can only estimate the relative velocity between the sensing receiver and the target, i.e., the projection of the velocity on the line connecting the sensing receiver and the target. In addition, due to the complex environment, the direct link between the radar receiver (e.g., BS) and the target may be blocked. In such a case, traditional estimation methods will not be able to produce accurate results.

[0048] Various embodiments of the present disclosure contemplate IRS-assisted velocity estimation by leveraging the uplink signal from a target to a BS. Embodiments of the present disclosure contemplate a scenario where a mobile target moves in a complex environment in which the direct link between the target and the BS may be blocked. To address this issue, the system of the present disclosure can first determine whether the direct link is available and then determine how many IRS-assisted links should be utilized for velocity estimation. By leveraging the different perspectives provided by the IRS, the velocity can be recovered based on the Doppler frequencies estimated according to two separate links.

[0049] For example, embodiments of the present disclosure can utilize IRS-assisted links to obtain multiple perspectives for observing the Doppler frequency. Specifically, embodiments of the present disclosure implement two processes (e.g., an indirect process and a direct process). For the indirect process, the system of the present disclosure first estimates intermediate parameters and then utilizes the intermediate parameters to estimate the velocity. Specifically, the system of the present disclosure first estimates the Doppler frequencies of different links in a first stage. In a second stage, the system of the present disclosure determines the velocity vector based on the estimated Doppler frequencies and the geometric relationships among the target, the IRS, and the BS. For the direct process, the system of the present disclosure directly estimates the velocity without estimating intermediate parameters (e.g., the Doppler frequency). Such direct and indirect processes will be discussed in more detail later.

[0050] Now turning to Figure 1 , an example non-limiting system 102 in accordance with one or more example embodiments of the present disclosure is illustrated. System 102 may include a computerized tool that may be configured to perform various operations related to velocity estimation using an intelligent reflecting surface. System 102 may include one or more of a variety of components such as a memory 104, a processor 106, a bus 108, and / or computer-executable components 110. In various example embodiments, one or more of the memory 104, the processor 106, the bus 108, and / or the computer-executable components 110 may be communicatively or operably coupled to each other (e.g., via a bus or a wireless network) to perform one or more functions of system 102. In various example embodiments, system 102 may also include and / or be communicatively coupled to a receiver device 112 and / or a target object device 114.

[0051] Figure 2 A block diagram of an example non-limiting computer-executable component 110 that may facilitate velocity estimation using an intelligent reflecting surface in accordance with one or more embodiments described herein is illustrated. For the sake of brevity, repeated descriptions of similar elements employed in other embodiments described herein are omitted. As in Figure 2As shown in, computer-executable component 110 may include a signal component 202, a Doppler frequency component 204, a velocity estimation component 206, and / or a geometric relationship component 208. It should be noted that although the various components described herein may perform one or more corresponding functions, processes, or actions, computer-executable component 110 as a whole and / or processor 106 may be configured to perform one or more of the described functions, processes, or actions.

[0052] Figure 3 is a schematic diagram 300 of an example intelligent reflecting surface-assisted vehicle-to-infrastructure system (e.g., including system 102) according to one or more example implementation embodiments described herein. In Figure 3 , the velocity vector of a point-like maneuvering target (e.g., target 302) is estimated via a base station 304 assisted by two IRSs (IRS 308 and IRS 310), and the velocity vector is represented by υ. For example, the base station 304 and the IRS are respectively equipped with a uniform linear array (ULA) with N antennas and a uniform linear array (ULA) with M antennas. The velocity vector can be v = [|v|cosθ v , |v|sinθ v T is defined, where θ v represents the direction of the velocity, as illustrated in Figure 6a and Figure 6b and discussed in more detail later.

[0053] In various embodiments, based on a first signal (e.g., a first link) (e.g., signal 316) between a receiver (e.g., a BS such as a radar receiver) (e.g., base station 304) and a target object (e.g., target 302), signal component 202 may determine a second signal (e.g., a second link) (e.g., signal 312) between the receiver (e.g., base station 304) and the target object (e.g., target 302). In various embodiments, the first signal (e.g., signal 316) may include a direct signal, and the second signal (e.g., signal 312) may include an indirect signal transmitted via an intelligent reflecting surface (e.g., IRS 310). In this regard, the indirect signals herein may include signals reflected via the IRSs herein.

[0054] In one or more embodiments, signal component 202 may select an intelligent reflecting surface (e.g., IRS 310) from a group of intelligent reflecting surfaces (e.g., IRS 310, IRS 308) that relay a link between a receiver (e.g., base station 304) and a target object (e.g., target 302). For example, signal component 202 may select the IRS that includes the strongest signal, or select the IRS according to another suitable defined selection criterion.​

[0055] In various embodiments, the signal component 202 can determine a second signal (e.g., signal 312) in response to determining that a first signal (e.g., signal 316) is blocked by an intermediate object (e.g., object 306) located between a receiver (e.g., base station 304) and a target object (e.g., target 302) (e.g., via the signal component 202). The signal component 202 can use one or more of a variety of suitable processes to determine whether a signal herein is blocked, such processes as signal attenuation, return time (e.g., echo delay), Doppler shift change, multipath interference, phase shift, determination of the absence of an expected echo, or another suitable block determination process. Similarly, the signal component 202 can determine that a second signal (e.g., signal 312) is not blocked by an intermediate object (e.g., object 306) located between a receiver (e.g., base station 304) and a target object (e.g., target 302). In various embodiments, such an intermediate object (e.g., object 306) can be determined to include a stationary object or a moving object. To determine whether an object is in motion, the signal component 202 can use one or more of a variety of suitable processes, such processes as determination of Doppler shift, change in range (e.g., time delay), utilization of continuous wave radar, utilization of pulsed Doppler radar, tracking history (e.g., tracked position over time), speed magnitude calculation, or another suitable motion determination process. In this regard, object 306 is depicted as a stationary object (e.g., building), however in other embodiments, object 306 can be a moving object, such as a vehicle or another suitable moving object. For example, when a first signal (e.g., direct transmission) (e.g., signal 316) between a target object (e.g., transmitter) (e.g., target 302) and a receiver (e.g., base station 304) fails (e.g., loses connection or drops below a defined signal strength), the signal component 202 can determine a block.

[0056] In various embodiments, the first signal (e.g., signal 316) or the second signal (e.g., signal 312) can include an uplink signal. In various embodiments, the first link (e.g., signal 316) or the second link (e.g., signal 312) can include a cellular signal transmitted via a cellular network (e.g., of system 102 or communicatively coupled to system 102). In this regard, such a cellular network can include a fifth generation (5G) cellular network or a sixth generation (6G) cellular network, or another suitable cellular network.

[0057] In various embodiments, the Doppler frequency component 204 may determine the Doppler frequency of a second signal (e.g., signal 312). The Doppler frequency component 204 may use one or more of a variety of Doppler frequency determination processes to determine the Doppler frequency of the signals herein. For example, the Doppler frequency component 204 may utilize the Doppler shift formula where Δf is the Doppler shift (e.g., the difference between the transmit frequency and the receive frequency), v is the relative velocity of the target towards or away from the base station (e.g., the radial velocity), f 0 is the frequency of the transmitted base station signal (e.g., the carrier frequency), and c is the speed of light in a vacuum.

[0058] In various embodiments, the velocity estimation component 206 may determine a velocity estimate (e.g., a true velocity estimate) of a target object (e.g., target 302) based on the Doppler frequency. In this regard, the velocity estimation component 206 may determine the estimated velocity based on the Doppler frequency of the first signal (e.g., signal 316) and / or the second signal (e.g., signal 312). In various embodiments, the velocity estimation component 206 may determine that the target object (e.g., target 302) is in motion. To determine the velocity estimate based on the Doppler frequency, the velocity estimation component may utilize a direct process or an indirect process. Such processes are discussed immediately below and are also discussed in more detail later with respect to Figure 6a and Figure 6b for more detailed discussion.

[0059] In some embodiments, the velocity estimate of the target object (e.g., target 302) may be directly determined (e.g., by the velocity estimation component 206) (e.g., direct process) via a linear search of the amplitude of the first signal (e.g., signal 316) or the second signal (e.g., signal 312) and / or the angle of the velocity of the first signal (e.g., signal 316) or the second signal (e.g., information 312) through a defined matched filter. In various embodiments, the linear search may be associated with a matched filter (e.g., a matched filter). In various embodiments, the input to the matched filter may include the received signal and a given velocity. Thus, various embodiments herein may linearly search for velocities within a given range (e.g., via the velocity estimation component 206) and calculate the output of the matched filter. The velocity estimation component 206 may determine the velocity corresponding to the maximum output of the matched filter as the estimated velocity by the linear search. Generally, the matched filter may include a signal processing filter that maximizes the detection of a particular signal in the presence of noise. By convolving the received signal with a signal manifold corresponding to a given velocity (e.g., via the velocity estimation component 206), the output will be maximized if the given velocity equals the true velocity of the target 302.

[0060] In some other embodiments, via a matched filter that estimates the Doppler frequency of the first signal (e.g., signal 316) or the second signal (e.g., signal 312) with respect to the first signal (e.g., signal 316) or the second signal (e.g., signal 312), and a linear search on the amplitude of the first signal (e.g., signal 316) or the second signal (e.g., signal 312) and / or the angle of the velocity of the first signal (e.g., signal 316) or the second signal (e.g., signal 312) by the defined matched filter, the velocity estimate of the target object (e.g., target 302) can be indirectly determined (e.g., by the velocity estimation component 206) (e.g., indirect process).

[0061] In various embodiments, the indirect process herein refers to a two-step process that first estimates intermediate parameters such as angle, Doppler frequency, distance, etc., and then estimates the position of the target (e.g., target 302) based on the intermediate parameters (e.g., via the velocity estimation component 206 and / or the geometric relationship component 208). On the other hand, the direct determination process refers to a process in which the position is directly estimated based on the received signal without estimating intermediate parameters. Generally, although not necessarily, the indirect process is easier and computationally efficient, while the direct process is more accurate, so either process can be utilized depending on the estimation scenario.

[0062] In various embodiments, the Doppler frequency of the second signal (e.g., signal 312) is the first Doppler frequency. In this regard, the signal component 202 can determine a third signal (e.g., signal 314) between the receiver (e.g., base station 304) and the target object (e.g., target 302), where the third signal (e.g., signal 314) includes an indirect signal transmitted via the IRS 308 (e.g., the second intelligent reflecting surface). The Doppler frequency component 204 can also determine the second Doppler frequency of the third signal (e.g., signal 314), where the velocity estimate is also determined based on the second Doppler frequency of the third signal (e.g., signal 314) (e.g., via the velocity estimation component 206). In various embodiments, the velocity estimation component can utilize the direct process or the indirect process described above when determining the velocity estimate of the third signal (e.g., signal 314).

[0063] In various embodiments, the velocity estimation component 206 may determine the amount of intelligent reflecting surface assisted signals associated with determining a velocity estimate of a target object with a threshold accuracy. In other words, the velocity estimation component 206 may determine how much IRS (e.g., and thus the corresponding indirect signals) should be utilized to meet a defined velocity accuracy threshold. In this regard, such determination by the velocity estimation component 206 may be based on the signal strength of the corresponding indirect signals herein. Thus, the velocity estimation component 206 may determine the amount of IRS and indirect signals based on a defined relationship between the signal strength and the amount of IRS indirect signals.

[0064] In various embodiments, the geometric relationship component 208 may determine the geometric relationship between a target object (e.g., target 302), an intelligent reflecting surface (e.g., IRS 310), and a receiver (e.g., base station 304). The geometric relationship component 208 may determine the geometric relationship based on the positions of the target object (e.g., target 302), the intelligent reflecting surface (e.g., IRS 310), and the receiver (e.g., base station 304). For example, the positions of the IRS 310 and the base station 304 may be known to the system 102. In this regard, the positions of the IRSs 308 and 310 and the base station 304 may be predefined as such positions generally do not change or at least do not change frequently. Then, for example, the position of the target 302 may be determined based on a defined positioning process. Such a defined positioning process may include, for example, using triangulation of the signals herein or another suitable positioning process. In this regard, the velocity estimation component 206 may also determine a velocity estimate of the target object (e.g., target 302) based on the geometric relationship described above (e.g., to improve accuracy).

[0065] Figure 4 FIG. 400 is a schematic diagram of an example intelligent reflecting surface assisted vehicle-to-vehicle system (e.g., including system 102) according to one or more example embodiments described herein. Figure 4 Depicts an ISAC system in which the velocity vector of a point-like maneuvering target (e.g., vehicle 402) is estimated by another vehicle (e.g., vehicle 404) (e.g., including system 102) with the assistance of an IRS 406. Without the IRS 406, the estimated velocity (e.g., via the velocity estimation component 206) would be the projection of the velocity on the direct link 408. If the system 102 only utilizes the direct link 408, based on the geometric relationship between vehicle 402 and vehicle 404, vehicle 402 would be perceived (e.g., from the perspective of vehicle 404) as moving significantly away from vehicle 404. As a result, vehicle 404 would not be able to anticipate Figure 4The potential collision of the vehicle depicted in. However, with the assistance of IRS 406 and thus the indirect link 410, vehicle 404 (e.g., including system 102) can estimate the speed of vehicle 402, enabling vehicle 404 to cause corrective actions (e.g., changing direction or speed).

[0066] Figure 5 FIG. 500 is a schematic diagram of an example intelligent reflecting surface assisted indoor integrated sensing and communication system (e.g., including system 102) according to one or more example embodiments described herein. For example, due to the complex nature of the indoor environment, indoor speed estimation (e.g., of target 502) can be challenging. For example, the indoor environment typically includes various potential obstructions to the signals herein. Embodiments herein may include an ISAC system where the speed of target 502 can be estimated by base station 504 (e.g., including system 102) with the assistance of multiple IRSs herein. For example, the direct signal 518 between target 502 and base station 504 may be blocked or potentially blocked by object 506 (e.g., a couch). Thus, indirect signals via the IRSs herein can be utilized, such as indirect signal 514 via IRS 510, indirect signal 516 via IRS 512, and / or indirect signal 520 via IRS 508. In various embodiments, system 102 can perform speed estimation on target 502, similar to that described with respect to Figure 3 and Figure 4 and discussed later with respect to Figure 6a and Figure 6b .

[0067] Figure 6a and Figure 6b illustrate the Doppler effect on different links according to one or more example embodiments described herein. Conventional monostatic radar can only access μ 0 —which corresponds to the radial component of velocity. As a result, the tangential projection will lead to ambiguity (e.g., different velocities may produce the same observation at the BS). An example is illustrated in Figure 6a where two velocity vectors, namely v amb (608) and v(610), give the same projection. Similarly, for the IRS link, velocity ambiguity occurs, as illustrated in Figure 6b . Thus, either of the two links, without the other, will result in velocity ambiguity. However, by jointly considering the two links, the velocity vector can be accurately determined. It is assumed herein that the distance between base station 602 and IRS 606 is similar to the distance between the target (e.g., target 604) and BS 602 and / or IRS 606.

[0068] For example, assume that the directions of the target 604 with respect to the base station 602 and the i-th IRS - denoted by θ Figure 6a and Figure 6b in tb and - are pre - estimated (e.g., via the system 102). The received signal with the k - th pilot symbol s k received by the base station 602 is given by:

[0069]

[0070] where:

[0071]

[0072] denote the response vectors of the receiver and the IRS, where d and λ denote the internal spacing and wavelength of the ULA, respectively. G represents the channel between the base station 602 and the IRS 606, which is modeled as a Rice channel including a line - of - sight (LoS) path and multiple non - LoS paths. Ψ represents the phase shifters designed to be aligned towards the target. α 0 and α i denote the complex channel gains, taking into account path loss and target reflectivity. The fluctuations of the target reflectivity are typically modeled using standard Swerling classes. The embodiments in this paper adopt the Swerling I model, where it is assumed that the reflectivity is constant within a symbol. T s denotes the symbol period, and denotes the noise vector, whose elements are drawn independently from a complex Gaussian distribution with zero mean and covariance matrix . μ0 and μi denote the Doppler frequencies corresponding to the direct link and the i - th IRS link, respectively. Since the Doppler effect depends on the mobility of the target, the embodiments in this paper first determine the relationship between the velocity v and the Doppler frequencies of the direct link and the IRS link.

[0073] The Doppler frequency of the relative motion between the BS and the mobile target 604 is given by:

[0074]

[0075] where v denotes the amplitude of the velocity, and θ v denotes the angle between the velocity direction and the x - axis, (610 in Figure 6a and Figure 6b ). It should be noted that vcos(θ v -θ tb ) is the component of the velocity along the line across the base station 602 and the target 604 (in Figure 6aDepicted as a projection onto 612). The deployment of IRS 606 creates another path that enables the BS to observe the velocity from an additional perspective. The Doppler frequency of the relative motion between IRS 606 and the maneuvering target is given by:

[0076]

[0077] S is represented as a set of indices of existing links. Specifically, 0 ∈ S and i ∈ S indicate the existence of the direct link and the existence of the i-th IRS link, respectively. In the following, the velocity estimation process for the IRS-aided system (e.g., via system 102) is explained. For all i ∈ S, w i is defined as the beamforming vector corresponding to the received beam pointing to the estimated target position through the corresponding link. Given the pilot symbol sk = 1, the output of the k-th symbol is given by:

[0078]

[0079] where:

[0080]

[0081] and represents the combined noise following a Gaussian distribution, i.e., Adding z i ,k to the vector gives:

[0082]

[0083] As discussed above, to estimate the velocity of the target in this paper, the embodiments of this paper (e.g., via system 102) implement two velocity estimation processes: an indirect process and a direct process.

[0084] In the indirect process, the embodiments of this paper first estimate the Doppler frequencies of different links and obtain the velocity based on the Doppler frequencies.

[0085] Phase I (estimation of μi):

[0086] The steering vector in the Doppler domain is defined as:

[0087]

[0088] The estimation of μi is obtained as:

[0089]

[0090] Phase II (estimation of v and θ v ):

[0091] The amplitude and angle of the velocity can be obtained by the following formula:

[0092]

[0093] In the direct process, the embodiments of the present disclosure directly determine the velocity.

[0094] The steering vector in the Doppler domain is defined as:

[0095]

[0096] The amplitude and angle of the velocity can be obtained by the following formula:

[0097]

[0098] Figure 7 The normalized root mean square error (NMSE) as a function of the target velocity is shown for different signal-to-noise ratios (SNRs) according to one or more example embodiments described herein. Various embodiments herein assume the presence of a direct link and utilize one IRS-assisted link. In the Figure 7 experiment depicted, the base station and the IRS are located at positions (0, 0) and (20, 0), respectively, and all such coordinates are defined in meters. The directions of the target relative to the base station and the IRS are π / 6 and 2π / 3, respectively. It can be observed that the root mean square error (RMSE) performance is significantly improved in the case of deploying the IRS. This is because in the absence of the IRS, only the radial projection of the velocity can be estimated, such that the tangential projection of the velocity dominates the estimation error. It can also be observed that in the presence of the IRS, the RMSE performance improves as the target velocity increases. Furthermore, as the velocity increases, the gap between the curves with the IRS and without the IRS becomes larger, indicating that the benefits of deploying the IRS will be more pronounced in high-mobility implementations.

[0099] Figure 8 The RMSE performance of the direct and indirect processes is shown where the target moves at different velocities according to one or more example embodiments described herein. In this regard, Figure 8 the RMSE performance of the direct and indirect processes is shown where the target moves at different velocities v = 5, 10, and 15 m / s. Other simulation settings are comparable to those in Figure 7 . For the sake of brevity, the repeated descriptions are omitted. It can be observed that the velocity can be found with a fairly high accuracy. As the signal-to-noise ratio (SNR) increases, the NMSE of both processes decreases. The direct process can achieve better performance compared to the indirect process, for example, because the information loss used to estimate the Doppler frequency in the first step is avoided.

[0100] Figure 9 is a flowchart of process 900 associated with speed estimation using an intelligent reflecting surface according to one or more example embodiments described herein. At 902, process 900 may include determining (e.g., via signal component 202) a second signal (e.g., signal 312) between a receiver (e.g., base station 304) and a target object (e.g., target 302) based on a first signal (e.g., signal 316) between the receiver (e.g., base station 304) and the target object (e.g., target 302), where the first signal (e.g., signal 316) includes a direct signal and where the second signal (e.g., signal 312) includes an indirect signal transmitted via an intelligent reflecting surface (e.g., IRS 310). At 904, process 900 may include determining (e.g., via Doppler frequency component 204) the Doppler frequency of the second signal (e.g., signal 312). At 906, process 900 may include determining (e.g., via speed estimation component 206) a speed estimate of the target object (e.g., target 302) based on the Doppler frequency.

[0101] Figure 10 is a flowchart of process 1000 associated with speed estimation using an intelligent reflecting surface according to one or more example embodiments described herein. At 1002, process 1000 may include determining (e.g., via signal component 202) a second link (e.g., signal 312) between a base station (e.g., base station 304) and a target object (e.g., target 302) based on a first link (e.g., signal 316) between the base station (e.g., base station 304) and the target object (e.g., target 302), where the first link (e.g., signal 316) includes a direct link and where the second link (e.g., signal 312) includes an indirect link transmitted via an intelligent reflecting surface (e.g., IRS 310). At 1004, process 1000 may include determining (e.g., via Doppler frequency component 204) the Doppler frequency of the second link (e.g., signal 312). At 1006, process 1000 may include determining (e.g., via speed estimation component 206) a speed estimate of the target object (e.g., target 302) based on the Doppler frequency.

[0102] Figure 11FIG. 1100 is a flowchart of a process 1100 associated with speed estimation using an intelligent reflecting surface, according to one or more embodiments described herein. At 1102, the process 1100 may include determining (e.g., via signal component 202) a second communication (e.g., signal 312) between a receiver device (e.g., base station 304) and a target object device (e.g., target 302) by a system including at least one processor, based on a first communication (e.g., signal 316) between the receiver device (e.g., base station 304) and the target object device (e.g., target 302), where the first communication (e.g., signal 316) includes direct communication, and where the second communication (e.g., signal 312) includes indirect communication transmitted via an intelligent reflecting surface (e.g., IRS 310). At 1104, the process 1100 may include determining (e.g., via Doppler frequency component 204) a Doppler frequency of the second communication (e.g., signal 312) by the system. At 1106, the process 1100 may include calculating (e.g., via speed estimation component 206) a speed estimate of the target object device (e.g., target 302) by the system, based on the Doppler frequency.

[0103] To provide additional context for the various example embodiments described herein, Figure 12 and the following discussion is intended to provide a brief, general description of a suitable computing environment 1200 in which the various example embodiments described herein may be implemented. While embodiments have been described above in the general context of computer-executable instructions that may run on one or more computers, those skilled in the art will recognize that embodiments may also be implemented in combination with other program modules and / or as a combination of hardware and software.

[0104] Generally, program modules include routines, programs, components, modules, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will understand that various methods may be practiced using other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, and personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc., each of which may be operatively coupled to one or more associated devices.

[0105] The embodiments illustrated herein may also be practiced in a distributed computing environment where certain tasks are performed by remote processing devices linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0106] Computing devices generally include a variety of media, which may include computer-readable storage media, machine-readable storage media, and / or communication media, which two terms are used differently from each other herein, as described below. Computer-readable storage media or machine-readable storage media can be any available storage media accessible by a computer, and include volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, computer-readable storage media or machine-readable storage media can be implemented in conjunction with any method or technology for storing information such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0107] Computer-readable storage media can include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), Blu-ray disc (BD) or other optical disc storage devices, magnetic tape cartridges, magnetic tape, magnetic disk storage devices or other magnetic storage devices, solid state drives or other solid state storage devices, or other tangible and / or non-transitory media that can be used to store the desired information. In this regard, the terms "tangible" or "non-transitory" as applied to storage devices, memories, or computer-readable media herein should be understood as modifiers that only exclude propagated transitory signals themselves, and do not relinquish rights to all standard storage devices, memories, or computer-readable media that are not only propagated transitory signals themselves.

[0108] Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via an access request, query, or other data retrieval protocol, for performing a variety of operations regarding the information stored by the media.

[0109] Communication media typically contain computer-readable instructions, data structures, program modules, or other structured or unstructured data in a data signal, such as a modulated data signal, e.g., a carrier wave or other transmission mechanism, and include any information delivery or transmission medium. The term "modulated data signal" or "plural modulated data signals" refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example and not limitation, communication media include wired media such as a wired network or a direct wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media.

[0110] Refer again to Figure 12, An example environment 1200 for various example implementation schemes for implementing the aspects described herein includes a computer 1202, which includes a processing unit 1204, a system memory 1206, and a system bus 1208. The system bus 1208 couples system components including, but not limited to, the system memory 1206 to the processing unit 1204. The processing unit 1204 can be any of a variety of commercially available processors. Dual microprocessors and other multiprocessor architectures can also be used as the processing unit 1204.

[0111] The system bus 1208 can be any of several types of bus structures, and can also interconnect with a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory 1206 includes a ROM 1210 and a RAM 1212. The basic input / output system (BIOS) can be stored in non-volatile memory (such as ROM, erasable programmable read-only memory (EPROM), EEPROM), and the BIOS contains basic routines that help transfer information between elements within the computer 1202, such as during startup. The RAM 1212 can also include high-speed RAM, such as static RAM for caching data.

[0112] The computer 1202 also includes an internal hard disk drive (HDD) 1214 (e.g., EIDE, SATA), one or more external storage devices 1216 (e.g., a magnetic floppy disk drive (FDD), a memory stick or flash drive reader, a memory card reader, etc.), and an optical disk drive 1220 (e.g., which can read from or write to a disk 1222 such as a CD-ROM disk, a DVD, a BD, etc.). Although the internal HDD 1214 is illustrated as being within the computer 1202, the internal HDD 1214 can also be configured for external use in a suitable chassis (not shown). Additionally, although not shown in the environment 1200, a solid-state drive (SSD) can be used in addition to or instead of the HDD 1214. The HDD 1214, the external storage device(s) 1216, and the optical disk drive 1220 can be connected to the system bus 1208 via an HDD interface 1224, an external storage device interface 1226, and an optical disk drive interface 1228, respectively. The interface 1224 for external drive implementations can include at least one or both of the universal serial bus (USB) and the Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technology. Other external drive connection technologies are within the scope of the embodiments described herein.

[0113] The drive and its associated computer-readable storage medium provide non-volatile storage of data, data structures, computer-executable instructions, and the like. For computer 1202, the drive and storage medium accommodate storage of any data in a suitable digital format. Although the above description of computer-readable storage media refers to corresponding types of storage devices, those skilled in the art should understand that other types of storage media that are computer-readable, whether currently existing or to be developed in the future, may also be used in the exemplary operating environment, and furthermore, any such storage media may contain computer-executable instructions for performing the methods described herein.

[0114] Multiple program modules may be stored in the drive and RAM 1212, and the modules include an operating system 1230, one or more application programs 1232, other program modules 1234, and program data 1236. All or part of the operating system, application programs, modules, and / or data may also be cached in RAM 1212. The systems and methods described herein may be implemented using various commercially available operating systems or combinations of operating systems.

[0115] Computer 1202 may optionally include emulation technology. For example, a hypervisor (not shown) or other intermediary may emulate the hardware environment for operating system 1230, and the emulated hardware may optionally be different from Figure 12 that illustrated in. In such an implementation, operating system 1230 may include one of a plurality of virtual machines (VMs) hosted at computer 1202. Additionally, operating system 1230 may provide a runtime environment (such as a Java runtime environment or a.NET framework) for application programs 1232. The runtime environment is a consistent execution environment that allows application programs 1232 to run on any operating system that includes the runtime environment. Similarly, operating system 1230 may support containers, and application programs 1232 may be in the form of containers, which are lightweight, independent, executable software packages that include, for example, code, runtime, system tools, system libraries, and settings for the application program.

[0116] Furthermore, computer 1202 may enable a security module, such as a Trusted Platform Module (TPM). For example, using the TPM, the boot component will hash the next boot component in time before loading it and wait for the result to match a security value. This process may occur at any layer in the code execution stack of computer 1202, for example, applied at the application execution level or at the operating system (OS) kernel level, thereby achieving security at any code execution level.

[0117] A user can input commands and information into the computer 1202 through one or more wired / wireless input devices (e.g., keyboard 1238, touch screen 1240, and a pointing device such as mouse 1242). Other input devices (not shown) may include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control or other remote controls, a joystick, a virtual reality controller and / or virtual reality headset, a game pad, a stylus, an image input device (e.g., one or more cameras), a gesture sensor input device, a vision motion sensor input device, an emotion or face detection device, a biometric identification input device (e.g., fingerprint or iris scanner), etc. These and other input devices are typically connected to the processing unit 1204 through an input device interface 1244 that can be coupled to the system bus 1208, but can be connected through other interfaces (such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, interfaces, etc.).

[0118] A monitor 1246 or other type of display device can also be connected to the system bus 1208 via an interface (such as a video adapter 1248). In addition to the monitor 1246, a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc.

[0119] The computer 1202 can operate in a networking environment using a logical connection that communicates with one or more remote computers (such as one or more remote computers 1250) wired and / or wirelessly. The one or more remote computers 1250 can be a workstation, a server computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other common network nodes, and typically include many or all of the elements described with respect to the computer 1202, although for the sake of brevity, only the memory / storage device 1252 is illustrated. The depicted logical connections include a wired / wireless connection to a local area network (LAN) 1254 and / or a larger network (e.g., a wide area network (WAN) 1256). Such LAN and WAN networking environments are common in offices and companies and result in enterprise-wide computer networks (such as intranets), all of which can be connected to a global communication network, such as the Internet.

[0120] When used in a LAN networking environment, computer 1202 can be connected to a local network 1254 through a wired and / or wireless communication network interface or adapter 1258. Adapter 1258 can facilitate wired or wireless communication with the LAN 1254, and the LAN 1254 can also include a wireless access point (AP) disposed thereon for communicating with adapter 1258 in a wireless mode.

[0121] When used in a WAN networking environment, computer 1202 can include a modem 1260, or can be connected to a communication server on the WAN 1256 via other devices for establishing communication through the WAN 1256 (such as via the Internet). The modem 1260 (which can be an internal or external and wired or wireless device) can be connected to the system bus 1208 via the input device interface 1244. In a networked environment, program modules depicted relative to the computer 1202 or portions thereof can be stored in a remote memory / storage device 1252. It will be understood that the network connections shown are examples, and other devices for establishing communication links between computers can be used.

[0122] When used in a LAN or WAN networking environment, computer 1202 can access a cloud storage system or other network-based storage system in addition to or instead of the external storage device 1216 as described above. Generally, the connection between the computer 1202 and the cloud storage system can be established, for example, via the adapter 1258 or the modem 1260 on the LAN 1254 or the WAN 1256, respectively. After connecting the computer 1202 to the associated cloud storage system, the external storage interface 1226 can manage the storage provided by the cloud storage system with the assistance of the adapter 1258 and / or the modem 1260, just as it manages other types of external storage. For example, the external storage interface 1226 can be configured to provide access to cloud storage sources as if those sources were physically connected to the computer 1202.

[0123] Computer 1202 can operably communicate with any wireless device or entity operably disposed in wireless communication, such as a printer, scanner, desktop and / or portable computer, portable data assistant, communication satellite, any device or location associated with a wirelessly detectable tag (such as a kiosk, newsstand, store shelf, etc.), and a telephone. This can include Wi-Fi and wireless technologies. Thus, the communication can be a predefined structure like a traditional network, or simply an ad hoc communication between at least two devices.

[0124] Now refer toFigure 13 , an exemplary block diagram of a computing environment 1300 in accordance with the present specification is illustrated. The system 1300 includes one or more clients 1302 (e.g., computers, smart phones, tablets, cameras, PDAs). The (one or more) clients 1302 can be hardware and / or software (e.g., threads, processes, computing devices). For example, the (one or more) clients 1302 can accommodate (one or more) cache files (cookies) and / or associated context information by adopting the present specification.

[0125] The system 1300 also includes one or more servers 1304. The (one or more) servers 1304 can also be hardware or hardware combined with software (e.g., threads, processes, computing devices). For example, the server 1304 can accommodate threads to perform the conversion of media items by adopting aspects of the present disclosure. A possible communication between the client 1302 and the server 1304 can be in the form of data packets suitable for transmission between two or more computer processes, where the data packets can include encoded analysis headspace and / or input. For example, the data packets can include cache files and / or associated context information. The system 1300 includes a communication framework 1306 (e.g., a global communication network such as the Internet), which can be adopted to facilitate communication between the (one or more) clients 1302 and the (one or more) servers 1304.

[0126] The communication can be caused via wired (including fiber optic) and / or wireless technologies. The (one or more) clients 1302 are operatively connected to one or more client data repositories 1308, which can be adopted to store information local to the (one or more) clients 1302 (e.g., the (one or more) cache files and / or associated context information). Similarly, the (one or more) servers 1304 are operatively connected to one or more server data repositories 1310, which can be adopted to store information local to the server 1304.

[0127] In one exemplary embodiment, the client 1302 can pass an encoded file (e.g., an encoded media item) to the server 1304. The server 1304 can store the file, decode the file, or transfer the file to another client 1302. It should be noted that, in accordance with the present disclosure, the client 1302 can also pass an uncompressed file to the server 1304, and the server 1304 can compress the file and / or convert the file. Similarly, the server 1304 can encode the information and transmit the information to one or more clients 1302 via the communication framework 1306.

[0128] Aspects of the present disclosure as illustrated can also be practiced in a distributed computing environment where certain tasks are performed by remote processing devices linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

[0129] The above description includes non-limiting examples of various example embodiments. Of course, for purposes of describing the disclosed subject matter, it is not possible to describe every conceivable combination of components, modules, or methods, and one of ordinary skill in the art will recognize that other combinations and permutations of the various example embodiments are possible. The disclosed subject matter is intended to cover all such changes, modifications, and variations that fall within the spirit and scope of the appended claims.

[0130] Regarding the various functions performed by the components, modules, devices, circuits, systems, etc. described above, unless otherwise specified, the terms used to describe such components or modules (including references to “means”) are also intended to include any (one or more) structures that perform the specified function of the described component or module (e.g., functional equivalents), even if not equivalent in structure to the disclosed structure. Further, although a particular feature of the disclosed subject matter may have been disclosed with respect to only one of several embodiments, such feature may be combined with one or more other features of the other embodiments as may be desired and advantageous for any given or particular application.

[0131] As used herein, the terms “exemplary” and / or “illustrative” are intended to mean serving as an example, instance, or illustration. To avoid doubt, the subject matter disclosed herein is not limited by such examples. Further, any aspect or design described herein as “exemplary” and / or “illustrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor does it imply exclusion of equivalent structures and techniques known to those of ordinary skill in the art. Additionally, in the sense in which the terms “include,” “has,” “contain,” and other similar words are used in the detailed description or claims, such terms are intended to be inclusive—in a manner similar to the open transitional term “comprising”—and do not exclude any additional or other elements.

[0132] As used herein, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” For example, the phrase “A or B” is intended to include instances of A, B, and both A and B. Further, as used in this application and the appended claims, the articles “a” and “an” are generally to be construed to mean “one or more” unless otherwise specified or clear from the context as referring to the singular form.

[0133] As used herein, the term "set" does not include the empty set, i.e., a set that has no elements. Thus, a "set" in the present disclosure includes one or more elements or entities. Similarly, as used herein, the term "group" refers to an aggregation of one or more entities.

[0134] As provided herein, the description of the illustrated embodiments of the present disclosure (including what is described in the abstract) is not intended to be exhaustive or to limit the disclosed embodiments to the precise forms disclosed. While specific embodiments and examples are described herein for illustrative purposes, various modifications are considered to be within the scope of such embodiments and examples, as would be recognized by those skilled in the art. In this regard, while the subject matter has been described herein in connection with various example embodiments and the corresponding drawings, it should be understood that, where applicable, other similar embodiments may be used, or modifications and additions may be made to the described embodiments to perform the same, similar, alternative, or substitute functions of the disclosed subject matter, without departing from the disclosed subject matter. Accordingly, the disclosed subject matter should not be limited to any single embodiment described herein, but rather should be construed in breadth and scope in accordance with the appended claims below.

Claims

1. A system comprising: at least one processor; as well as at least one memory storing executable instructions that, when executed by the at least one processor, cause performance of operations comprising: determining a second signal between the receiver and the target object based on a first signal between the receiver and the target object, wherein the first signal comprises a direct signal, and wherein the second signal comprises an indirect signal transmitted via a smart reflective surface; determining a Doppler frequency of the second signal; and Based on the Doppler frequency, a velocity estimate of the target object is determined. 2 . The system of claim 1 , wherein the second signal is determined in response to determining that the first signal is blocked by an intervening object located between the receiver and the target object. 3 . The system of claim 1 , wherein the second signal is determined to be unobstructed by an intervening object located between the receiver and the target object.

4. The system of claim 1, wherein the velocity estimate of the target object is determined directly via a linear search of the amplitude of the first signal or the second signal and the angle of the velocity of the first signal or the second signal through a defined matched filter.

5. The system of claim 1 , wherein the velocity estimate of the target object is determined indirectly via: a matched filter for the first signal or the second signal used to estimate the Doppler frequency of the first signal or the second signal; and A linear search is performed on the amplitude of the first signal or the second signal and the angle of the speed of the first signal or the second signal through a defined matched filter.

6. The system of claim 1, wherein the Doppler frequency of the second signal is a first Doppler frequency, wherein the smart reflecting surface is a first smart reflecting surface, and wherein the operations further comprise: determining a third signal between the receiver and the target object, wherein the third signal comprises an indirect signal transmitted via a second smart reflective surface; as well as determining a second Doppler frequency of the third signal, Wherein the velocity estimate is further determined based on the second Doppler frequency of the third signal.

7. The system of claim 1, wherein the operations further comprise: An amount of a smart reflective surface aiding signal associated with determining the velocity estimate of the target object with a threshold accuracy is determined. The system of claim 1 , wherein the target object is determined to be in motion.

9. The system of claim 1, wherein the operations further comprise: A geometric relationship between the target object, the smart reflective surface, and the receiver is determined, wherein the velocity estimate of the target object is also determined based on the geometric relationship.

10. A non-transitory machine-readable medium comprising executable instructions that, when executed by at least one processor, cause performance of operations comprising: determining a second link between a base station and a target object based on a first link between the base station and the target object, wherein the first link comprises a direct link, and wherein the second link comprises an indirect link transmitted via a smart reflective surface; determining a Doppler frequency of the second link; as well as Based on the Doppler frequency, a velocity estimate of the target object is determined. The non-transitory machine-readable medium of claim 10 , wherein the first link comprises an uplink signal.

12. The non-transitory machine-readable medium of claim 10, wherein the base station comprises a radar receiver.

13. The non-transitory machine-readable medium of claim 10, wherein the smart reflective surface is selected from a group of smart reflective surfaces that relay a link between the base station and the target object.

14. The non-transitory machine-readable medium of claim 10, wherein the first link or the second link comprises a cellular signal transmitted via a cellular network.

15. The non-transitory machine-readable medium of claim 14, wherein the cellular network comprises a fifth generation cellular network or a sixth generation cellular network.

16. The non-transitory machine-readable medium of claim 10, wherein the first link is determined to be blocked by a stationary object.

17. The non-transitory machine-readable medium of claim 10, wherein the first link is determined to be blocked by a moving object.

18. A method comprising: determining, by a system including at least one processor, a second communication between a receiver device and a target object device based on a first communication between the receiver device and the target object device, wherein the first communication includes a direct communication, and wherein the second communication includes an indirect communication transmitted via a smart reflective surface; determining, by the system, a Doppler frequency of the second communication; and Based on the Doppler frequency, a velocity estimate of the target object device is calculated by the system.

19. The method of claim 18, wherein calculating the velocity estimate of the target object device comprises: The speed estimate is directly determined based on the result of a linear search based on the angle of the speed of the first communication or the second communication through a defined matched filter.

20. The method of claim 18, wherein the Doppler frequency of the second communication is a first Doppler frequency, and wherein calculating the velocity estimate of the target object device comprises indirectly determining the velocity estimate: estimating a second Doppler frequency of the first communication or the first Doppler frequency of the second communication using a matched filter with respect to the first communication or the second communication; and A result of a linear search based on the amplitude of the first communication or the second communication is used.

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