A multipath component processing method, terminal and computer storage medium

Abstract

The embodiment of the present invention discloses a method for processing multipath components, the method comprising: obtaining a binary image corresponding to a power spectrum of a propagation channel to be processed, and obtaining a first image; wherein the power spectrum represents the distribution of the power of the propagation channel based on a first preset parameter; removing stray points in the first image, and obtaining a second image; determining a first trajectory corresponding to a second preset parameter of the propagation channel from the second image, and determining a second trajectory based on the first trajectory; wherein the second trajectory includes the first trajectory; the first trajectory and the second trajectory are used to represent the change of the second preset parameter over time. The embodiment of the present invention also discloses a terminal and a computer storage medium.

Description

Technical Field

[0001] The present invention relates to but is not limited to the field of communications, and in particular to a multipath component processing method, a terminal and a computer storage medium. Background Art

[0002] In recent years, vehicle-to-vehicle communication has attracted extensive attention from researchers because it can significantly improve the efficiency and safety of intelligent transportation systems. The study of vehicle-to-vehicle communication wave propagation is the basis for the study of the entire vehicle-to-vehicle wireless communication system. However, unlike traditional cellular communication systems, vehicle-to-vehicle communication is more complicated due to its significant channel time-varying characteristics.

[0003] The trajectory of a time-varying path cluster will experience fast fading and blocking, and the trajectories of different path clusters may intersect each other. These characteristics make it difficult for algorithms such as Kalman filtering or hierarchical clustering to extract the trajectory of path clusters. Summary of the invention

[0004] In order to solve the above technical problems, the embodiments of the present invention hope to provide a multipath component processing method, terminal and computer storage medium, which solves the problem that the trajectory of the path cluster cannot be extracted in the related technology, and can effectively identify, track and extract the trajectory of the path cluster with low computational complexity.

[0005] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0006] An embodiment of the present invention provides a method for processing multipath components, the method comprising:

[0007] Acquire a binary image corresponding to a power spectrum of a propagation channel to be processed to obtain a first image; wherein the power spectrum represents a distribution of power of the propagation channel based on a first preset parameter;

[0008] removing stray points in the first image to obtain a second image;

[0009] A first trajectory corresponding to a second preset parameter of the propagation channel is determined from the second image, and a second trajectory is determined based on the first trajectory; wherein the second trajectory includes the first trajectory; and the first trajectory and the second trajectory are used to characterize changes in the second preset parameter over time.

[0010] An embodiment of the present invention provides a terminal, the terminal comprising: a processor, a memory and a communication bus;

[0011] The communication bus is used to realize the communication connection between the processor and the memory;

[0012] The processor is used to execute the multipath component processing program in the memory to implement the following steps:

[0013] Acquire a binary image corresponding to a power spectrum of a propagation channel to be processed to obtain a first image; wherein the power spectrum represents a distribution of power of the propagation channel based on a first preset parameter;

[0014] removing stray points in the first image to obtain a second image;

[0015] A first trajectory corresponding to a second preset parameter of the propagation channel is determined from the second image, and a second trajectory is determined based on the first trajectory; wherein the second trajectory includes the first trajectory; and the first trajectory and the second trajectory are used to characterize changes in the second preset parameter over time.

[0016] An embodiment of the present invention provides a computer storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the steps of the multipath component processing method as described above.

[0017] An embodiment of the present invention provides a method, terminal and computer storage medium for processing multipath components, the method comprising: obtaining a binary image corresponding to a power spectrum of a propagation channel to be processed to obtain a first image; wherein the power spectrum characterizes the distribution of the power of the propagation channel based on a first preset parameter such as delay; removing stray points in the first image to obtain a second image; determining a first trajectory corresponding to a second preset parameter of the propagation channel from the second image, and determining a second trajectory based on the first trajectory; wherein the second trajectory includes the first trajectory; the first trajectory and the second trajectory are used to characterize the change of the second preset parameter over time; that is, in an embodiment of the present invention, by converting path information into black and white image information, and identifying and tracking the trajectory corresponding to the preset parameter of the propagation channel in the black and white image, the problem of being unable to extract trajectories in the related art is solved, and effective clustering and tracking of multipath components in the time-delay parameter domain is achieved with low computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a flow chart of a method for processing multipath components provided by an embodiment of the present invention;

[0019] Figure 2 A first image corresponding to a delayed power spectrum of a time-varying channel provided by an embodiment of the present invention;

[0020] Figure 3 A schematic flow chart of another method for processing multipath components provided by an embodiment of the present invention;

[0021] Figure 4A schematic flow chart of another method for processing multipath components provided by an embodiment of the present invention;

[0022] Figure 5 An image corresponding to a fixed time delay in a first image provided by an embodiment of the present invention;

[0023] Figure 6 A transformation of an image corresponding to a fixed time delay in a first image provided by an embodiment of the present invention;

[0024] Figure 7 For Figure 6 An enlarged schematic diagram of any path in ;

[0025] Figure 8 An image obtained by removing stray points from an image corresponding to a fixed time delay in a first image provided by an embodiment of the present invention;

[0026] Fig. 9 An image obtained by removing stray points from a first image provided by an embodiment of the present invention;

[0027] Fig.10 A schematic diagram of an improved Hough transform space of a first image provided by an embodiment of the present invention;

[0028] Fig.11 A schematic diagram of a path area provided by an embodiment of the present invention;

[0029] Fig.12 A schematic diagram of a first trajectory segment in a path cluster provided by an embodiment of the present invention;

[0030] Fig.13 A schematic diagram of tracking a first trajectory segment in a path cluster provided by an embodiment of the present invention;

[0031] Fig.14 A schematic diagram of each identified trajectory provided by an embodiment of the present invention;

[0032] Fig.15 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0034] It should be understood that the "embodiments of the present invention" or "the aforementioned embodiments" mentioned throughout the specification mean that specific features, structures or characteristics related to the embodiments are included in at least one embodiment of the present invention. Therefore, "in the embodiments of the present invention" or "in the aforementioned embodiments" appearing in various places throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. In various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention. The serial numbers of the above-mentioned embodiments of the present invention are for description only and do not represent the advantages and disadvantages of the embodiments.

[0035] The method for processing multipath components proposed in the embodiment of the present invention can identify, track and extract multipath components of dynamic propagation channels in the time-delay domain with low computational complexity, and has broad application prospects in the field of wireless communications. In traditional communication systems, the processing of spatial and temporal domains is mainly concentrated in the digital domain, while the embodiment of the present invention accurately models and reproduces the spatial and temporal characteristics of wireless channels at the path cluster level. The method for processing multipath components proposed in the embodiment of the present invention is of great value for the design of future wireless communication systems and the verification of various communication technologies in actual propagation environments. In actual communication products, the identification and tracking results obtained by the method for processing multipath components proposed in the embodiment of the present invention can also be used to determine the presence of major scatterers in the communication environment, and these scatterers can be used for multi-link transmission or diversity transmission, thereby effectively improving the quality, stability and low latency of communication.

[0036] The multipath component processing method proposed in the embodiment of the present invention can be applied to the following scenarios, for example, beamforming and tracking based on a multi-antenna communication system to achieve accurate modeling of the spatial and temporal characteristics of a wireless channel; for example, object recognition and motion prediction in a vehicle network and radar system, hazard warning and avoidance, etc.; for example, the channel multipath components extracted by the multipath component processing method proposed in the embodiment of the invention can be widely used as channel fingerprint information in positioning systems and big data analysis. In other words, the multipath component processing method proposed in the embodiment of the present invention has an extremely wide application market in the field of communications, from basic research to various technical scenarios.

[0037] The trajectory of a time-varying path cluster will experience fast fading and blocking, and different cluster trajectories may also cross each other. These features make it difficult for algorithms such as Kalman filtering or hierarchical clustering to be applied to the extraction of path clusters. Fast fading and blocking can easily lead to "tracking loss", and the intersection of different path clusters can also cause the tracking to erroneously shift from one path cluster to another. However, the method for processing multipath components proposed in the embodiment of the present invention can solve the above problems well. The method for processing multipath components proposed in the embodiment of the present invention can effectively overcome the above difficulties by converting path information into image information and identifying and tracking path clusters in black and white images, thereby effectively clustering and tracking multipath components in the time-delay parameter domain, and has low computational complexity.

[0038] Here, the meanings of the symbols involved in the embodiments of the present invention are explained, t time, τ propagation delay, u Doppler frequency, x horizontal coordinate axis, y vertical coordinate axis, ρ the distance from the origin to the straight line, i.e., the polar diameter, θ the angle between the horizontal coordinate axis and the vertical line, i.e., the polar angle, x f A fixed horizontal coordinate value, l snapshot corresponding to the time window, ts adjacent channel impulse response time interval, τ r Delay resolution, τ e The delay range of the main peak selection, y b The maximum blocking time of the path cluster, c the speed of light, and v the speed of the path cluster.

[0039] The embodiment of the present invention provides a method for processing multipath components, which is applied to a terminal, referring to Figure 1 As shown, the method comprises the following steps:

[0040] Step 101: Obtain a binary image corresponding to the power spectrum of a propagation channel to be processed to obtain a first image.

[0041] The power spectrum characterizes the power of the propagation channel based on the distribution of the first preset parameter such as the delay. Here, the time-varying delay power spectrum can be measured for the vehicle-to-vehicle channel. After the terminal obtains the time-varying delay power spectrum, the time-varying delay power spectrum can be binarized to obtain a binary image, that is, a black and white image as the first image.

[0042] In an embodiment of the present invention, a car and a van are selected as the transmitting end and the receiving end respectively for measuring the vehicle-to-vehicle channel. A quasi-omnidirectional antenna is installed on the upper left of the exterior of the car, about 1.5 meters from the ground, as a transmitting antenna; the same antenna is placed on the upper left of the exterior of the van, about 1.9 meters from the ground, as a receiving end. The measurement was carried out on a road with a speed limit of 80 kilometers per hour near the Jiading campus of Tongji University. The environment of the road is mainly suburban scenes. There are metal lamp posts and large factories on both sides of the road. There are many vehicles on the road, including cars, vans and large trucks. The receiving end and the transmitting end travel in the same direction and at a normal driving speed. The transmission signal bandwidth of the instrument used for channel measurement is 100MHz, that is, the delay resolution is 10 nanoseconds, and the center frequency is 5.9GHz. The channel impulse response (CIR) is collected every 1.2 milliseconds.

[0043] In the embodiment of the present invention, the horizontal axis of the time-varying delay power spectrum measured for the vehicle-to-vehicle channel represents the propagation delay, the vertical axis represents the time, and the color represents the received power. Further, the time-varying delay power spectrum is binarized to obtain Figure 2 The first image shown, from Figure 2 As can be seen in Figure 1, the measured channel contains many trajectories in the time-delay domain. In addition, the Doppler characteristics of different trajectories are also different. Intuitively, these different trajectories can be considered as different path clusters. In addition to these path cluster trajectories, some path components that do not show obvious trajectories but are scattered in the delay domain are also observed, which we call clutter paths.

[0044] It should be noted that the trajectory of a time-varying path cluster will experience fast fading and blocking, and different cluster trajectories may also cross each other. These features make it difficult for algorithms such as Kalman filtering or hierarchical clustering to be applied to the extraction of path clusters. Fast fading and blocking can easily lead to tracking loss, and the intersection of different path clusters can also cause the tracking to erroneously shift from one path cluster to another. Based on the fact that these path cluster trajectories are curves with slowly changing slopes, the inventors believe that as long as these curves are detected, the path clusters can be determined. Since some unexpected peaks exist in the time-delay power spectrum, there are a lot of stray points in the black and white image.

[0045] Step 102: remove the stray points in the first image to obtain a second image.

[0046] Here, for a fixed time delay, the difference between a path cluster and stray points is that the points representing the path cluster appear continuously with high density within a certain period of time; this difference can be used to distinguish path clusters from stray points at a fixed time delay.

[0047] In the embodiment of the present invention, after the terminal acquires the first image, the point density may be used as a main reference factor for removing stray points in the first image, and the stray points are removed from the first image to obtain the second image.

[0048] For example, Figure 2 The point G corresponding to the fixed delay of 200 refers to the stray point, and E refers to the path.

[0049] Step 103: Determine a first trajectory corresponding to a second preset parameter of the propagation channel from the second image, and determine a second trajectory according to the first trajectory.

[0050] Among them, the second preset parameter can be a parameter related to the propagation direction of the signal, or a parameter related to the propagation power of the signal. The second trajectory includes the first trajectory; the first trajectory and the second trajectory are used to characterize the change of the preset parameter over time. The first trajectory can be called a first trajectory segment, and the first trajectory is a part of the second trajectory. Here, the terminal determines the first trajectory segment from the second image with stray points removed, and performs forward and backward tracking based on the first trajectory segment to determine the second trajectory. In this way, the identification and tracking of multipath components are realized, and each trajectory is determined from the multipath components.

[0051] The multipath component method provided by an embodiment of the present invention obtains a binary image corresponding to the power spectrum of the propagation channel to be processed to obtain a first image; wherein the power spectrum characterizes the distribution of a first preset parameter of the propagation channel such as power over delay; removes stray points in the first image to obtain a second image; determines a first trajectory corresponding to a second preset parameter of the propagation channel from the second image, and determines a second trajectory based on the first trajectory; wherein the second trajectory includes the first trajectory; the first trajectory and the second trajectory are used to characterize the change of the second preset parameter over time; that is, in an embodiment of the present invention, by converting path information into black and white image information, and identifying and tracking the trajectory corresponding to the preset parameter of the propagation channel in the black and white image, the problem of being unable to extract trajectories in the related art is solved, and effective clustering and tracking of multipath components in the time-delay parameter domain is achieved with low computational complexity.

[0052] Based on the above embodiments, an embodiment of the present invention provides a method for processing multipath components, which is applied to a terminal, referring to Figure 3 As shown, the method comprises the following steps:

[0053] Step 201: Obtain a binary image corresponding to the power spectrum of a propagation channel to be processed to obtain a first image.

[0054] The power spectrum characterizes the power of the propagation channel based on the distribution of the first preset parameter such as delay.

[0055] Step 202: remove the stray points in the first image to obtain a second image.

[0056] Step 203: Determine a first trajectory corresponding to a second preset parameter of the propagation channel from the second image, and determine a second trajectory according to the first trajectory.

[0057] The second trajectory includes the first trajectory; the first trajectory and the second trajectory are used to characterize changes of the second preset parameter over time.

[0058] Step 204: Save the track information of the second track, and remove the second track from the second image to obtain a fourth image.

[0059] The trajectory information is used to characterize the second trajectory.

[0060] Here, after determining the second track from the second image, the terminal first saves the path information of the second track, and then removes the second track from the second image to obtain the fourth image.

[0061] Step 205: determine a fourth trajectory corresponding to the second preset parameter from the fourth image, and determine a fifth trajectory according to the fourth trajectory.

[0062] The fifth track includes the fourth track; the fourth track and the fifth track are used to characterize the change of the second preset parameter over time. The fifth track and the second track are two different tracks. The fourth track can be called a fourth track segment, and the fourth track is a part of the fifth track.

[0063] Here, step 205 determines the fourth trajectory corresponding to the second preset parameter from the fourth image and determines the fifth trajectory according to the fourth trajectory in a similar manner to step 103 determines the first trajectory corresponding to the second preset parameter from the second image and determines the second trajectory according to the first trajectory.

[0064] In the embodiment of the present invention, when the terminal determines a track, the track is removed from the black-and-white image from which stray points are removed, so as to reduce interference in extracting the next track.

[0065] It should be noted that, for the description of the same steps and the same contents in this embodiment as those in other embodiments, reference can be made to the description in other embodiments and will not be repeated here.

[0066] Based on the above embodiments, the embodiments of the present invention provide a method for processing multipath components. Figure 4 As shown, the method comprises the following steps:

[0067] Step 301: Obtain a binary image corresponding to the power spectrum of a propagation channel to be processed to obtain a first image.

[0068] The power spectrum characterizes the power of the propagation channel based on the distribution of the first preset parameter such as delay.

[0069] Step 302: Obtain a first sub-image corresponding to each first preset parameter in the first image.

[0070] The first sub-image includes a plurality of third trajectories; the third trajectories are used to characterize the change of the preset parameters over time. Here, for a fixed time delay, the difference between the path cluster, i.e., the trajectory, and the stray points is that the points representing the path cluster appear continuously and at a high density within a certain period of time; therefore, in the embodiment of the present invention, the first sub-image corresponding to each fixed time delay in the first image is processed to remove the stray points.

[0071] In the embodiment of the present invention, illustratively, Figure 5 The first image shows that the delay is fixed at x f = 200, i.e., the first sub-image of the entire time range, i.e., the black-and-white image segment p(x f , y).

[0072] Step 303: Obtain the length, point density and time length of each third track in the first sub-image.

[0073] Here, after obtaining the first sub-image corresponding to each time delay in the first image, the terminal performs image transformation on the first sub-image to obtain the length, point density and time length of each third track in the first sub-image. Figure 5 The first sub-image in is transformed to obtain Figure 6 The image shown shows the length, point density and time length of each third track in the first sub-image. Figure 6 The horizontal axis represents the delay, the vertical axis represents the number of channel snapshots, and the color represents the point density. Figure 7 against Figure 5 A third track in the diagram is enlarged and displayed, where the vertices of the wedge-shaped area are as follows: Figure 7 The position indicated by the black circle in the figure shows the information of the longest segment that meets the requirements of a valid trajectory segment. In the embodiment of the present invention, the stray points can be removed by retaining only the segments corresponding to the wedge-shaped endpoints with a trajectory length greater than or equal to 20ts.

[0074] Step 304: Determine, among the plurality of third trajectories, a trajectory whose length meets a preset length, whose point density meets a preset density, and whose time length meets a preset time length as a first target trajectory.

[0075] Here, for example, the preset length may be 20, the preset density may be 0.8, and the preset time length may be 20. The terminal compares the length of each third track with the preset length, the point density of each third track with the preset density, and the time length of each third track with the preset time length, and determines the third track whose length meets the preset length, point density meets the preset density, and time length meets the preset time length among the plurality of third tracks as the first target track. Figure 6 The track in the middle rectangular box is determined as a first target track.

[0076] Step 305: remove the second track except the first target track in the first sub-image to obtain a second image.

[0077] Here, after determining the first target trajectory corresponding to each time delay, the terminal determines that the third trajectory other than the first target trajectory in the first sub-image corresponding to each time delay is a stray point, that is, a clutter path.

[0078] Here, after determining the clutter path corresponding to each fixed time delay, the terminal removes the clutter path corresponding to each fixed time delay from the first image to obtain the second image.

[0079] Exemplarily, the image after removing the clutter path from the first sub-image corresponding to the fixed delay 200 is as follows: Figure 8 Further, by repeating the same operation at each fixed time delay, the stray points in the first image can be removed. After the clutter paths corresponding to each fixed time delay in the first image are removed, we can get Fig. 9 The second image is shown after removing stray points from the first image.

[0080] Step 306: Determine a target sub-image corresponding to the second preset parameters from the second image.

[0081] Here, after obtaining the second image with stray points removed, the terminal determines a target sub-image corresponding to the preset parameters of the propagation channel from the second image, and the target sub-image can be called a trajectory range. The trajectory range represents the range of as many points as possible in the current second image.

[0082] In the embodiment of the present invention, step 306 of determining the target value image corresponding to the second preset parameter from the second image may include:

[0083] A1. Perform Hough transform on the second image to obtain a plurality of first point densities.

[0084]

[0085] Among them, T(y, l; x f ) represents a fixed delay xf τ r , the point density of a black and white image segment with a time center of yts and a time length of (1+l)ts.

[0086] A2. Obtain the first point density with the largest median value among multiple first point densities to obtain the maximum first point density.

[0087] Here, after the terminal calculates the point density of each point in the second image using the above formula (1), it can determine the first point density with the largest value from multiple point densities to obtain the maximum first point density. For example, Fig.10 The point density indicated by the middle circle is the largest first point density. Fig.10 The horizontal axis represents the polar angle, the vertical axis represents the polar diameter, and the color represents the point density, that is, the number of points at that point. The intersection of several curves at a point means that these fixed points are collinear, and then the polar angle and polar diameter corresponding to the largest first point density can be determined.

[0088] A3. Determine the first straight line in the second image according to the polar angle and polar diameter corresponding to the maximum first point density.

[0089] Here, assuming Fig.11 The straight line corresponding to a1 is the first straight line.

[0090] A4. Determine a second straight line in the second image based on the first straight line.

[0091] Here, after determining the first straight line, the terminal translates the first straight line a1 to the right by a preset distance to obtain the second straight line a2. The first straight line a1 and the second straight line a2 correspond to the ideal path (a). The straight line corresponding to b can be called the ideal path (b).

[0092] A5. Determine the image corresponding to the area enclosed by the first straight line, the second straight line, and the boundary line of the second image as the target sub-image.

[0093] Here, the terminal may determine the target sub-image according to the area enclosed by the first straight line, the second straight line and the boundary line of the second image. The target sub-image may be called a path area or a path range; that is, the trajectory is searched within the path range in the embodiment of the present invention.

[0094] Attached Fig.11The point distribution of two ideal paths (a) and (b) with constant speed is shown. For path (a), its trajectory points do not fall on the same straight line due to the delay resolution limitation. In fact, they are within the range of two parallel lines, and the distance between the two lines is (1-|tanθ|)cosθ. Note that when the absolute value of θ is greater than or equal to 45 degrees, the two lines are unified into the same line, as shown in path (b). In the embodiment of the present invention, (θ, ρ, ρ′) can be used to represent a trajectory range,

[0095] ρ′=ρ+(1-α)cosθ (2)

[0096] in,

[0097] In addition, the relative speed v of the trajectory range can be calculated by the following formula:

[0098]

[0099] Where c represents the speed of light. In the embodiment of the present invention, in the speed-limited vehicle scene, the range of θ can be reduced from 180° to about 8°, for example, from -4° to 4°, which can greatly reduce the amount of calculation. For a point at (x, y) in a black and white image, there are countless paths passing through it. These ranges satisfy the following conditions

[0100] (x cosθ+y sinθ-ρ)(x cosθ+y sinθ-ρ′)≤0 (4)

[0101] That is, the polar angle and the polar diameter corresponding to the point between the first straight line a1 and the second straight line a2 both satisfy formula (4).

[0102] The improved Hough transform is thus expressed as

[0103]

[0104] in,

[0105] In the embodiment of the present invention, ρ′ can be omitted in formula (5) to make the expression more concise, because formula (5) is essentially determined by two variables, θ and ρ. In this way, by finding the local extreme value of the improved Hough transform space, it is possible to correspond to the trajectory range that passes through as many points as possible. In addition, considering that the influence of multipath superposition, noise, etc. may cause the path points to shift to adjacent positions, the path area width, that is, the distance ρ′-ρ between the first straight line a1 and the second straight line a2, can be expanded to, for example, (1-α)cosθ+2cosθ. That is, when θ is less than or equal to 45°, we believe that the path to be tracked is between these two parallel straight lines, and when θ is greater than 45°, these two straight lines become one straight line. The difference between the improved Hough transform and the traditional Hough transform is that the traditional Hough transform requires that the condition to be satisfied is that the path to be tracked should pass through the point (ρ, θ) in the parameter domain, while in the improved Hough transform, the path to be tracked should be within a limited area.

[0106] Step 307: Determine a first trajectory from the target sub-image.

[0107] The tracking process is initialized by finding a trajectory segment and then continues tracking in time to form a complete trajectory.

[0108] We use the parameter set Ω = {θ, ρ, x - ,y - , x + ,y +} represents a path cluster fragment, which belongs to the trajectory region (θ, ρ) and starts at (x - ,y - ), ends at (x + ,y + ).

[0109] B1. Perform Hough transform on the target sub-image to obtain multiple second point densities.

[0110] B2. Obtain the second point density with the largest median value among multiple second point densities to obtain the maximum second point density.

[0111] Here, the terminal may obtain the maximum second point density by detecting the maximum value of the improved Hough transform space of the target sub-image, and then obtain the polar angle and polar diameter corresponding to the maximum second point density.

[0112] B3. Determine, according to the polar angle and polar diameter corresponding to the maximum second point density, the third straight line in the target sub-image as the first trajectory.

[0113] For the black-and-white image I considered, for example, the current complete black-and-white image I 0 , the trajectory range Ω containing the largest number of points is obtained as follows.

[0114] See attached Fig. 9 and attached Fig.10 As shown, for the trajectory area (θ, ρ) limited to the target sub-image I (as shown in the attached Fig. 9 The bold black line in the figure shows the application of the stray point removal transformation, (x - ,y - , x + ,y + ) can be obtained by finding the endpoint of the wedge region with the maximum path length in the transform domain, such as Fig.12 The mapping from I to Ω is denoted by

[0115] Ω=M(I) (6)

[0116] Step 308: Determine a fourth straight line and a fifth straight line according to the first trajectory.

[0117] The fourth straight line is different from the fifth straight line, and the angle between the fourth straight line and the first track is equal to the angle between the fifth straight line and the first track.

[0118] Will be attached Fig.12 The black line in the middle marks the first trajectory segment obtained, which is used as the initial segment and continues to be tracked in the future (forward) and past (backward) time to form a complete trajectory.

[0119] Since the relative speed of the path cluster changes slowly over time, it is natural to continue searching for the next trajectory segment within the range close to the previously obtained trajectory segment, and the relative speed of the next trajectory segment should be close to the relative speed of the previous trajectory segment.

[0120] Step 309: determine the image corresponding to the area enclosed by the fourth straight line, the fifth straight line and the preset straight lines in the second image as the second target sub-image.

[0121] The preset straight line is associated with the preset time. Here, the preset straight line in the second image may be a boundary line of the second image. Of course, the preset straight line in the second image may also be a straight line different from the boundary line of the second image.

[0122] In the embodiment of the present invention, given the kth segment Ω k ={θ k , ρ k , x k - ,y k - , x k + ,y k +} and the speed change tolerance Δy of the adjacent segments, the (k+1)th trajectory segment forward should be searched within the triangular area surrounded by three straight lines. Fig.13 As shown, the preset straight line in the second image may be the boundary line of the second image, that is, the straight line S3 (90°, y m ), the fourth straight line S2(θ k +Δθ,x k + cos(θ k +Δθ)+y k + sin(θ k +Δθ)) and the fifth straight line S1 (θ k -Δθ,x k + cos(θ k -Δθ)+y k + sin(θ k The triangular area surrounded by -Δθ) is the sub-image I k+1 ={x k + ,y k + ,θ k -Δθ,θ k +Δθ,y m}. Where Δθ is calculated from Av by formula (3), y m is the total number of snapshots in 30 seconds, which is 25,000.

[0123] Step 310: Search for a second target trajectory corresponding to the first trajectory in the second target sub-image, and combine the first trajectory and the second target trajectory into a second trajectory.

[0124] The second target trajectory is used to characterize the change of the second preset parameter over time.

[0125] In the embodiment of the present invention, searching the second target track corresponding to the first track in the second target sub-image in step 310 can be implemented by the following steps:

[0126] C1. Obtain a first coordinate corresponding to a maximum value among a plurality of channel snapshot numbers in a first trajectory.

[0127] C2. Obtain the difference between the number of snapshots of each channel in the second target sub-image and the maximum value, and obtain the second coordinate corresponding to the minimum value in the difference.

[0128] C3. Determine the second target trajectory according to the first coordinate and the second coordinate.

[0129] In order to smoothly and continuously track the trajectory segments, the (k+1)th segment is not directly generated by Ω k+1 =M(I k+1 ) is obtained. Its candidate fragment Ω k+1 , n is obtained by the following operation

[0130] Ω k+1，n =M(I k+1， n) (7)

[0131] Where n represents Ω k+1 The candidate index of

[0132]

[0133] Until I k+1 , n is empty (y + k =y - k+1，n-1 ) or M(I k+1 , n) failure is in I k+1 , nNo valid fragments are found. Among these candidates, the speed change is within the threshold range and Ω k The segment with the smallest time difference is finally selected as Ω k+1 ,Right now

[0134]

[0135] Among them, |θ k+1 m-θ k |≤Δθ(10) holds true. Empty or Time (y b represents the maximum blocking time that may occur), the forward search process is terminated. Considering the type of environment and the motion state of the vehicle, y b and Δy are empirically chosen to be 1 s and 3 m / s, respectively.

[0136] Attached Fig.12 To the attached Fig.13 An example forward tracking process is shown, where there is only one candidate segment. The purpose of such a recursive operation to obtain candidate segments is to avoid possible tracking loss, for example, when the path cluster speed changes rapidly. In addition, the backward tracking process is similar to the forward tracking process, except that the superscripts + and - in formulas (8) and (9) are swapped, and y m Replaced with 0.

[0137] The multiple path clusters observed in the measured channel are obtained through a “track-and-remove” process. The proposed algorithm transforms the measured channel from the raw channel impulse response into a visually and physically interpretable picture, which helps to deeply study the characteristics of the channel at the path cluster level. Fig.14 The final tracking results of the time-varying path clusters (or the peak points of the path clusters) are shown, where different path clusters are represented by different colors. With reference to the positions of these peak points, it is easy to determine the boundaries of the path clusters in the delay domain. In our example, when the power of the delay sampling point drops by more than 20 dB relative to the peak power or drops below the noise line, it is selected as the boundary. The remaining sampling points outside the time-varying path cluster are considered to be clutter paths.

[0138] It should be noted that, for the description of the same steps and the same contents in this embodiment as those in other embodiments, reference can be made to the description in other embodiments and will not be repeated here.

[0139] Based on the foregoing embodiments, an embodiment of the present invention provides a terminal, which can be applied to Figure 1 , 3 In a method for processing multipath components provided in the embodiment corresponding to 4, referring to Figure 5 As shown, the terminal 50 includes: a processor 51, a memory 52 and a communication bus 53, wherein:

[0140] The first communication bus 53 is used to realize the communication connection between the first processor 51 and the first memory 52;

[0141] The first processor 53 is used to execute the multipath component processing program stored in the first memory 52 to implement the following steps:

[0142] Acquire a binary image corresponding to a power spectrum of a propagation channel to be processed to obtain a first image; wherein the power spectrum represents a distribution of power of the propagation channel based on a first preset parameter;

[0143] Remove stray points in the first image to obtain a second image;

[0144] A first trajectory corresponding to a second preset parameter of the propagation channel is determined from the second image, and a second trajectory is determined based on the first trajectory; wherein the second trajectory includes the first trajectory; and the first trajectory and the second trajectory are used to characterize changes in the second preset parameter over time.

[0145] In other embodiments of the present invention, the first processor 53 is used to execute the process stored in the first memory 52 to remove the stray points in the first image to obtain the second image, so as to implement the following steps:

[0146] Acquire a first sub-image corresponding to each first preset parameter in the first image; wherein the first sub-image includes a plurality of third tracks; and the third tracks are used to characterize changes of the second preset parameters over time;

[0147] Obtaining the length, point density and time length of each third track in the first sub-image;

[0148] Determine a trajectory among the plurality of third trajectories whose length meets a preset length, whose point density meets a preset density, and whose time length meets a preset time length as a first target trajectory;

[0149] The second track except the first target track in the first sub-image is removed to obtain a second image.

[0150] In other embodiments of the present invention, the first processor 53 is used to execute the first trajectory corresponding to the second preset parameter determined from the second image stored in the first memory 52 to implement the following steps:

[0151] Determine a target sub-image corresponding to a second preset parameter from the second image;

[0152] A first track is determined from the target sub-image.

[0153] In other embodiments of the present invention, the first processor 53 is used to execute the process stored in the first memory 52 to determine the target sub-image corresponding to the second preset parameter from the second image, so as to implement the following steps:

[0154] Performing Hough transform on the second image to obtain a plurality of first point densities;

[0155] Obtain the first point density with the largest median value among multiple first point densities to obtain the maximum first point density;

[0156] Determine a first straight line in the second image according to the polar angle and the polar diameter corresponding to the maximum first point density;

[0157] determining a second straight line in the second image based on the first straight line;

[0158] An image corresponding to an area enclosed by the first straight line, the second straight line, and the boundary line of the second image is determined as a target sub-image.

[0159] In other embodiments of the present invention, the first processor 53 is used to execute the process of determining the first trajectory from the target sub-image stored in the first memory 52 to implement the following steps:

[0160] Performing Hough transform on the target sub-image to obtain multiple second point densities;

[0161] Obtain the second point density with the largest median value among multiple second point densities to obtain the maximum second point density;

[0162] According to the polar angle and the polar diameter corresponding to the maximum second point density, the third straight line in the target sub-image is determined to be the first trajectory.

[0163] In other embodiments of the present invention, the first processor 53 is used to execute the process of determining the second trajectory according to the first trajectory stored in the first memory 52 to implement the following steps:

[0164] Determine a fourth straight line and a fifth straight line according to the first trajectory; wherein the fourth straight line is different from the fifth straight line, and an angle between the fourth straight line and the first trajectory is equal to an angle between the fifth straight line and the first trajectory;

[0165] Determine an image corresponding to an area enclosed by the fourth straight line, the fifth straight line and a preset straight line in the second image as a second target sub-image; wherein the preset straight line is associated with the preset time;

[0166] Searching for a second target trajectory corresponding to the first trajectory in the second target sub-image; wherein the second target trajectory is used to characterize the change of the second preset parameter over time;

[0167] The first trajectory and the second target trajectory are combined into a second trajectory.

[0168] In other embodiments of the present invention, the first processor 53 is used to execute the search for the second target trajectory corresponding to the first trajectory in the second target sub-image stored in the first memory 52 to implement the following steps:

[0169] Obtaining a first coordinate corresponding to a maximum value among a plurality of channel snapshot numbers in a first trajectory;

[0170] Obtain the difference between the number of snapshots of each channel in the second target sub-image and the maximum value, and obtain the second coordinate corresponding to the minimum value in the difference;

[0171] A second trajectory is determined according to the first coordinates and the second coordinates.

[0172] In other embodiments of the present invention, the first processor 53 is further configured to perform the following steps:

[0173] Saving the trajectory information of the second trajectory, and removing the second trajectory from the second image to obtain a fourth image; wherein the trajectory information is used to characterize the second target trajectory;

[0174] A fourth trajectory corresponding to the preset parameters of the propagation channel is determined from the fourth image, and a fifth trajectory is determined according to the fourth trajectory; wherein the fifth trajectory includes the fourth trajectory; and the fourth trajectory and the fifth trajectory are used to characterize changes in the preset parameters over time.

[0175] It should be noted that the specific implementation process of the steps executed by the first processor in this embodiment can refer to Figure 1 , 3 The implementation process of the multipath component processing method provided in the embodiments corresponding to 4 is not repeated here.

[0176] The terminal provided in an embodiment of the present invention obtains a binary image corresponding to the power spectrum of the propagation channel to be processed to obtain a first image; wherein the power spectrum characterizes the distribution of the power of the propagation channel based on a first preset parameter such as delay; removes stray points in the first image to obtain a second image; determines a first trajectory corresponding to a second preset parameter of the propagation channel from the second image, and determines a second trajectory based on the first trajectory; wherein the second trajectory includes the first trajectory; the first trajectory and the second trajectory are used to characterize the change of the second preset parameter over time; that is, in an embodiment of the present invention, by converting path information into black and white image information, and identifying and tracking the trajectory corresponding to the preset parameters of the propagation channel in the black and white image, the problem of being unable to extract trajectories in the related art is solved, and effective clustering and tracking of multipath components in the time-delay parameter domain is achieved, and has low computational complexity.

[0177] Based on the foregoing embodiments, an embodiment of the present invention provides a computer-readable storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the following. Figure 1 , 3 4 to 4 correspond to the steps of the method for processing multipath components provided in the embodiments.

[0178] It should be noted that the above-mentioned computer storage medium can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM) and other memories; it can also be various electronic devices including one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0179] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "includes a ..." do not exclude the existence of other identical elements in the process, method, article or device including the element.

[0180] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0181] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0182] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented from computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0183] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0184] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0185] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for processing multipath components, characterized in that: The method comprises: Acquire a binary image corresponding to a power spectrum of a propagation channel to be processed to obtain a first image; wherein the power spectrum represents a distribution of power of the propagation channel based on a first preset parameter; removing stray points in the first image to obtain a second image; Determine a target sub-image corresponding to a second preset parameter of the propagation channel from the second image, wherein the second preset parameter is a parameter related to a propagation direction or propagation power of a signal; Performing a Hough transform on the target sub-image to obtain a plurality of second point densities, obtaining a second point density with the largest median value among the plurality of second point densities to obtain a maximum second point density, determining a third straight line in the target sub-image as a first trajectory according to a polar angle and a polar diameter corresponding to the maximum second point density, and performing forward and backward tracking according to a segment of the first trajectory to determine a second trajectory; wherein the second trajectory includes the first trajectory; and the first trajectory and the second trajectory are used to characterize changes of the second preset parameter over time.

2. The method according to claim 1, characterized in that The removing of stray points in the first image to obtain the second image includes: Acquire a first sub-image corresponding to each first preset parameter in the first image; wherein the first sub-image includes a plurality of third tracks; and the third tracks are used to characterize changes of the second preset parameter over time; Obtaining the length, point density and time length of each third track in the first sub-image; Determine a trajectory among the plurality of third trajectories whose length meets a preset length, whose point density meets a preset density, and whose time length meets a preset time length as a first target trajectory; The second track except the first target track in the first sub-image is removed to obtain the second image.

3. The method according to claim 1, characterized in that: The step of determining a target sub-image corresponding to a second preset parameter of the propagation channel from the second image includes: Performing Hough transform on the second image to obtain a plurality of first point densities; Obtain the first point density with the largest median value among the multiple first point densities to obtain the maximum first point density; determining a first straight line in the second image according to the polar angle and the polar diameter corresponding to the maximum first point density; Determine a second straight line in the second image based on the first straight line, wherein the second straight line is determined by translating the first straight line to the right; An image corresponding to an area enclosed by the first straight line, the second straight line, and a boundary line of the second image is determined as the target sub-image.

4. The method according to claim 1, characterized in that The step of performing forward and backward tracking according to the segments of the first trajectory to determine the second trajectory comprises: Determine a fourth straight line and a fifth straight line according to the first trajectory; wherein the fourth straight line is different from the fifth straight line, and an angle between the fourth straight line and the first trajectory is equal to an angle between the fifth straight line and the first trajectory; Determine an image corresponding to an area enclosed by the fourth straight line, the fifth straight line and a preset straight line in the second image as a second target sub-image; wherein the preset straight line is associated with a preset time; Searching for a second target trajectory corresponding to the first trajectory in the second target sub-image; wherein the second target trajectory is used to characterize the change of the second preset parameter over time; The first trajectory and the second target trajectory are combined into the second trajectory.

5. The method according to claim 4, characterized in that The searching for a second target trajectory corresponding to the first trajectory in the second target sub-image includes: Obtaining a first coordinate corresponding to a maximum value among a plurality of channel snapshot numbers in the first trajectory; Obtaining a difference between the number of snapshots of each channel in the second target sub-image and the maximum value, and obtaining a second coordinate corresponding to a minimum value in the difference; The second target trajectory is determined according to the first coordinates and the second coordinates.

6. The method according to claim 1, characterized in that After determining the first trajectory corresponding to the second preset parameter of the propagation channel from the second image and determining the second trajectory according to the first trajectory, the method further includes: Saving the trajectory information of the second trajectory, and removing the second trajectory from the second image to obtain a fourth image; wherein the trajectory information is used to characterize the second trajectory; A fourth trajectory corresponding to the second preset parameter is determined from the fourth image, and a fifth trajectory is determined according to the fourth trajectory; wherein the fifth trajectory includes the fourth trajectory; and the fourth trajectory and the fifth trajectory are used to characterize changes of the second preset parameter over time.

7. A terminal, characterized in that: The terminal includes: a processor, a memory and a communication bus; The communication bus is used to realize the communication connection between the processor and the memory; The processor is used to execute the multipath component processing program in the memory to implement the following steps: Acquire a binary image corresponding to a power spectrum of a propagation channel to be processed to obtain a first image; wherein the power spectrum represents a distribution of power of the propagation channel based on a first preset parameter; removing stray points in the first image to obtain a second image; Determine a target sub-image corresponding to a second preset parameter of the propagation channel from the second image, wherein the second preset parameter is a parameter related to a propagation direction or propagation power of a signal; Performing a Hough transform on the target sub-image to obtain a plurality of second point densities, obtaining a second point density with the largest median value among the plurality of second point densities to obtain a maximum second point density, determining a third straight line in the target sub-image as a first trajectory according to a polar angle and a polar diameter corresponding to the maximum second point density, and performing forward and backward tracking according to a segment of the first trajectory to determine a second trajectory; wherein the second trajectory includes the first trajectory; and the first trajectory and the second trajectory are used to characterize changes of the second preset parameter over time.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the multipath component processing method according to any one of claims 1 to 6.

Images