Signal processing method and apparatus, storage medium, chip system, and program product

Abstract

The signal processing method, device, storage medium, chip system and program product provided by the embodiments of the present application relate to the field of communication technology. The method comprises: receiving first information sent by a sensing target, wherein the first information is used to indicate a decision threshold, and the decision threshold is related to the building density and / or height of the position of the sensing target. In the case that the log-likelihood ratio (LLR) of a first path and a second path is greater than or equal to the decision threshold, a first signal is sent by using the first path; in the case that the LLR is less than the decision threshold, the first signal is sent by using the second path, and the positioning error of the second path is greater than that of the first path. The method provided by the present application improves the accuracy of identifying the LOS path and the NLOS path.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, and in particular to a signal processing method and device, a storage medium, a chip system and a program product. BACKGROUND

[0002] With the rapid development of communication technology, Integrated Sensing and Communication (ISAC) emerges as a new technology concept. Integrated Sensing and Communication deeply integrates communication and sensing functions, aiming to use the same set of hardware devices and spectrum resources to achieve efficient information transmission while accurately sensing the surrounding environment, thereby bringing new application modes and development to intelligent transportation, smart cities, industrial Internet of Things and many other fields. In Integrated Sensing and Communication, the propagation path of a signal exists a Line of Sight (LOS) path and a Non-Line of Sight (NLOS) path, and accurately identifying the LOS path and the NLOS path is crucial to Integrated Sensing and Communication. However, the current identification of the LOS path and the NLOS path has the problem of low accuracy.

[0003] Therefore, how to improve the accuracy of identifying the LOS path and the NLOS path is a problem to be solved. SUMMARY

[0004] Embodiments of the present application provide a signal processing method and device, a storage medium, a chip system and a program product, which are applied to the technical field of communication to improve the accuracy of identifying the LOS path and the NLOS path.

[0005] In a first aspect, an embodiment of the present application provides a signal processing method. The method comprises:

[0006] receiving first information sent by a sensing target, the first information being used to indicate a decision threshold, the decision threshold being related to a building density and / or a height of a location of the sensing target;

[0007] in a case where a Log Likelihood Ratio (LLR) of a first path and a second path is greater than or equal to the decision threshold, using the first path to send a first signal;

[0008] in a case where the LLR is less than the decision threshold, using the second path to send the first signal, the second path having a positioning error greater than the first path.

[0009] Optionally, the first information comprises a first parameter and / or a second parameter, the first parameter being used to determine the building density of the location of the sensing target, and the second parameter being used to determine the height of the location of the sensing target.

[0010] Optionally, the first parameter comprises a positioning location of the sensing target.

[0011] Optionally, the decision threshold is related to a first path prior probability and a second path prior probability, the first path prior probability is related to the first parameter and / or the second parameter.

[0012] Optionally, the second path prior probability is negatively related to the first path prior probability.

[0013] Optionally, the first parameter is related to a plurality of building densities within a target time window, and the second parameter is related to a plurality of heights within the target time window.

[0014] Optionally, the first parameter is related to a mean value of the plurality of building densities within the target time window, and the second parameter is related to a mean value of the plurality of heights within the target time window.

[0015] Optionally, a length of the target time window belongs to a target length interval.

[0016] Optionally, a lower limit of the target length interval is greater than or equal to a first value, and the first value is greater than or equal to a signal transmission delay of the sensing target.

[0017] Optionally, an upper limit of the target length interval is less than or equal to a second value, and the second value is related to a motion speed of the sensing target and an effective radius of a cell where the sensing target is located.

[0018] Optionally, the second value is equal to a quotient of the effective radius and the motion speed.

[0019] Optionally, the LLR is related to an observation parameter of a second signal, and the second signal is a signal before the first signal.

[0020] Optionally, the observation parameter of the second signal comprises at least one of skewness, kurtosis, peak-to-average ratio, angle-of-arrival skewness, angle-of-arrival variance, and time delay.

[0021] Optionally, in a case where an angle-of-arrival spread of the second signal is greater than or equal to a first preset threshold, the observation parameter comprises the angle-of-arrival variance.

[0022] In a case where the angle-of-arrival spread of the second signal is less than a second preset threshold, the observation parameter comprises the angle-of-arrival skewness, and the second preset threshold is less than or equal to the first preset threshold.

[0023] Optionally, the first path prior probability is related to the first parameter, and the method further comprises:

[0024] generate the first path prior probability according to a first prior probability function corresponding to the first path, the first prior probability function being related to a building density reference value and a building density attenuation factor.

[0025] Optionally, the first path prior probability is related to the second parameter, and the method further comprises:

[0026] generate the first path prior probability according to a second prior probability function corresponding to the first path, the second prior probability function being related to a height reference value and a height attenuation factor.

[0027] Optionally, the first path prior probability is related to the first parameter and the second parameter, and the method further comprises:

[0028] generate the first path prior probability according to a first prior probability function corresponding to the first path, the first parameter, a second prior probability function, the second parameter, a first weight of the first prior probability function, and a second weight of the second prior probability function.

[0029] In a second aspect, an embodiment of the present application provides a signal processing apparatus, and the apparatus comprises:

[0030] a receiving module, configured to receive first information sent by a sensing target, the first information being used to indicate a decision threshold, the decision threshold being related to a building density and / or a height of a location where the sensing target is located;

[0031] a control module, configured to use a first path to send a first signal when a log likelihood ratio (LLR) of the first path and a second path is greater than or equal to the decision threshold, and use the second path to send the first signal when the LLR is less than the decision threshold, the second path having a positioning error greater than the first path.

[0032] In a third aspect, an embodiment of the present application provides a signal processing apparatus, comprising a processor and a memory, the memory being used to store computer execution instructions, and the processor being used to run the computer execution instructions stored in the memory to execute the method described in the first aspect or any possible implementation manner of the first aspect.

[0033] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program or instructions, when the computer program or instructions run on a computer, the computer program or instructions make the computer execute the method described in the first aspect or any possible implementation manner of the first aspect.

[0034] In a fifth aspect, an embodiment of the present application provides a computer program product including a computer program, which, when executed by a computer, causes the computer to perform the method described in the first aspect or any possible implementation manner of the first aspect.

[0035] In a sixth aspect, the present application provides a chip or a chip system, which includes at least one processor and a communication interface, the communication interface and the at least one processor are interconnected through a line, the at least one processor is configured to execute a computer program or instructions to perform the method described in the first aspect or any possible implementation manner of the first aspect. The communication interface in the chip can be an input / output interface, a pin or a circuit, etc.

[0036] In a possible implementation, the chip or the chip system described in the present application further includes at least one memory, and the at least one memory stores instructions. The memory can be a storage unit inside the chip, for example, a register, a cache, etc., or a storage unit of the chip (for example, a read-only memory, a random access memory, etc.).

[0037] The signal processing method, device, storage medium, chip system and program product provided by the embodiments of the present application can receive first information sent by a sensing target, the first information being used to indicate a decision threshold related to a building density and / or height of a location where the sensing target is located, in a case where LLRs of a first path and a second path are greater than or equal to the decision threshold, a first signal is transmitted by using the first path, and in a case where the LLRs of the first path and the second path are less than the decision threshold, the first signal is transmitted by using the second path. The method of the present application further introduces parameters related to the building density, the height and the like corresponding to the location where the terminal is located and having a relatively high correlation with path identification accuracy when identifying the path, thereby improving the accuracy of identifying the path in real time, making the accuracy of the path used for transmitting the first signal higher, and thus improving the detection accuracy of the sensing target and the accuracy of positioning the sensing target. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 A schematic diagram of an architecture of a communication system is provided for an embodiment of the present application;

[0039] Figure 2 A flowchart of a signal processing method is provided for an embodiment of the present application;

[0040] Figure 3 A flowchart of another signal processing method is provided for an embodiment of the present application;

[0041] Figure 4 A flowchart of still another signal processing method is provided for an embodiment of the present application;

[0042] Figure 5 A schematic flowchart illustrating another signal processing method provided in an embodiment of this application;

[0043] Figure 6 A schematic flowchart illustrating another signal processing method provided in an embodiment of this application;

[0044] Figure 7 This is a schematic diagram of the structure of a signal processing device provided in an embodiment of this application;

[0045] Figure 8 This is a schematic diagram of another signal processing device provided in an embodiment of this application. Detailed Implementation

[0046] In the embodiments of this application, terms such as "first" and "second" are used to distinguish identical or similar items with substantially the same function and purpose. For example, "first chip" and "second chip" are used only to distinguish different chips and do not limit their order of execution. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or execution order, and that "first" and "second" do not necessarily imply that they are different.

[0047] It should be noted that, in the embodiments of this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0048] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, a--c, bc, or abc, where a, b, and c can be single or multiple.

[0049] Figure 1 This is a schematic diagram of the architecture of a communication system provided in an embodiment of this application. Figure 1 As shown, the communication system 100 may include at least one network device (such as...) Figure 1at least one terminal (e.g., 110a, 110b, 110c in FIG. 1) and at least one network device (e.g., 120a-120g in FIG. 1). Figure 1 at least one terminal (e.g., 110a, 110b, 110c in FIG. 1) and at least one network device (e.g., 120a-120g in FIG. 1).

[0050] The network device and the terminal device can communicate with each other via a wireless link. When the network device is a communication sender, the terminal device can be a communication receiver. When the network device is a communication receiver, the terminal device can be a communication sender. The number of network devices and terminal devices included in the communication system is not limited in the embodiments of the present application. In addition, it should be understood that, Figure 1 The communication system can include other network devices, such as wireless relay devices and wireless backhaul devices, which are not shown in FIG. 1. Figure 1 The communication system can include other network devices, such as wireless relay devices and wireless backhaul devices, which are not shown in FIG. 1.

[0051] The network device can be a device that communicates with the terminal device. The network device can also be referred to as an access network device or a wireless access network device. For example, the network device can be a base station, a Node B, an evolved Node B (eNodeB or eNB), a transmission reception point (TRP), a next generation Node B (gNB) in a 5th generation (5G) mobile communication system, an access network device in an open radio access network (O-RAN or open RAN), a satellite base station in a non-terrestrial network (NTN), or an access node in a future mobile communication system, or a wireless fidelity (WiFi) system. Alternatively, the network device can be a module or unit that performs part of the functions of a base station. For example, the network device can be a central unit (CU), a distributed unit (DU), a CU control plane (CU-CP) module, or a CU user plane (CU-UP) module. The access network device can be a satellite base station (e.g., 110a in FIG. 1), a macro base station (e.g., 110b in FIG. 1), or a micro base station or indoor station (e.g., 110c in FIG. 1). Figure 1 The network device can be a device that communicates with the terminal device. The network device can also be referred to as an access network device or a wireless access network device. For example, the network device can be a base station, a Node B, an evolved Node B (eNodeB or eNB), a transmission reception point (TRP), a next generation Node B (gNB) in a 5th generation (5G) mobile communication system, an access network device in an open radio access network (O-RAN or open RAN), a satellite base station in a non-terrestrial network (NTN), or an access node in a future mobile communication system, or a wireless fidelity (WiFi) system. Alternatively, the network device can be a module or unit that performs part of the functions of a base station. For example, the network device can be a central unit (CU), a distributed unit (DU), a CU control plane (CU-CP) module, or a CU user plane (CU-UP) module. The access network device can be a satellite base station (e.g., 110a in FIG. 1), a macro base station (e.g., 110b in FIG. 1), or a micro base station or indoor station (e.g., 110c in FIG. 1). Figure 1 The network device can be a device that communicates with the terminal device. The network device can also be referred to as an access network device or a wireless access network device. For example, the network device can be a base station, a Node B, an evolved Node B (eNodeB or eNB), a transmission reception point (TRP), a next generation Node B (gNB) in a 5th generation (5G) mobile communication system, an access network device in an open radio access network (O-RAN or open RAN), a satellite base station in a non-terrestrial network (NTN), or an access node in a future mobile communication system, or a wireless fidelity (WiFi) system. Alternatively, the network device can be a module or unit that performs part of the functions of a base station. For example, the network device can be a central unit (CU), a distributed unit (DU), a CU control plane (CU-CP) module, or a CU user plane (CU-UP) module. The access network device can be a satellite base station (e.g., 110a in FIG. 1), a macro base station (e.g., 110b in FIG. 1), or a micro base station or indoor station (e.g., 110c in FIG. 1). Figure 1The network device can be a terminal device, or a network device such as a base station, a relay node, a donor node, etc. The specific technology and specific device form adopted by the network device in the present application are not limited. The 5G system can also be referred to as a new radio (NR) system.

[0052] The network in which the network device is located has strong computing capability, which can be provided by a computing node included in the network, or can be possessed by the network device itself. When the computing capability can be provided by the computing node included in the network, the network device can be connected with one or more computing nodes in the network, and the computing nodes are used to process the task data by distributing the task data received from the terminal device to the computing nodes. The computing node can be, for example, a multi-access edge computing (MEC), a distributed cloud node, a quantum computing node, a computing host, etc. In the computing node, one or more computing units can be included to realize concurrent processing of the task data, and the computing unit can be, for example, a central processing unit (CPU), a graphics processing unit (GPU), etc.

[0053] In one network structure, the network device can include a centralized unit (CU) node, or a distributed unit (DU) node, or a RAN device including the CU node and the DU node, or a RAN device including a control plane CU node (CU-CP node) and a user plane CU node (CU-UP node) and the DU node.

[0054] The network device provides services for a cell, and a terminal device communicates with the cell through transmission resources (for example, frequency domain resources, or spectrum resources) allocated by the network device. The cell can belong to a macro base station (for example, a macro eNB or a macro gNB, etc.), or a base station corresponding to a small cell. The small cell can include a metro cell, a micro cell, a pico cell, a femto cell, etc., which have the characteristics of small coverage and low transmit power, and are suitable for providing high-speed data transmission services.

[0055] Alternatively, the foregoing device in communication with the terminal device and the computing node can be regarded as a whole as the network device involved in the present application.

[0056] The terminal device in the embodiments of the present application can also be referred to as a user equipment (user equipment, UE), a mobile station (mobile station, MS), a mobile terminal (mobile terminal, MT), an access terminal, a user unit, a user station, a mobile station, a mobile station, a remote station, a remote terminal, a mobile device, a user terminal, a terminal, a wireless communication device, a user agent or a user device, etc. The terminal can be widely applied to various scenarios for communication. The scenario includes, for example, but is not limited to, at least one of the following scenarios: enhanced mobile broadband (enhanced mobile broadband, eMBB), ultra-reliable low-latency communication (ultra-reliable low-latency communication, URLLC), massive machine type communication (massive machine type communications, mMTC), device-to-device (device-to-device, D2D), vehicle-to-everything (vehicle to everything, V2X), machine type communication (machine type communication, MTC), Internet of Things (internet of things, IOT), virtual reality, augmented reality, industrial control, automatic driving, remote medical treatment, smart power grid, smart furniture, smart office, smart wear, smart transportation, or smart city, etc. The terminal can be a mobile phone (such as a mobile phone 120a, 120d, 120f in Figure 1 ), a tablet computer, a computer with wireless transceiver function (such as a computer 120g in Figure 1 ), a wearable device, a vehicle (such as 120b shown in Figure 1 ), an unmanned aerial vehicle (Unmanned Aerial Vehicle, UAV), a helicopter, an airplane (such as 120c in Figure 1 ), a ship, a robot, a mechanical arm, or a smart home device (such as a printer 120e in Figure 1 ), etc. The present application does not limit the specific technology and specific device form of the terminal.

[0057] By way of example and not limitation, in this application, the terminal device can be a terminal device in an XR system. As a key field of future human-computer interaction and digital content presentation, XR technology integrates VR, AR, and MR technologies, and its main technical feature is to seamlessly connect the digital world and the physical world through highly immersive experiences, and to realize deep interaction between users and virtual environments and real scenes. By way of example, the terminal device in the embodiments of the present application can be an XR device, which is a type of intelligent terminal specially designed for immersive experiences. By integrating display, sensing, computing, and communication technologies, virtual content or augmented information is superimposed on the user's field of view, or a completely virtual interactive space is constructed. XR devices include, but are not limited to, head-mounted displays, smart glasses, handheld interactive devices, and holographic projection devices. XR devices widely support cloud interaction, and through the network, real-time access to high-precision models, dynamic scene data, or AI inference services is realized, thereby breaking through the limitations of local computing power and promoting the landing of complex applications such as the metaverse and remote collaboration.

[0058] Currently, according to the signal propagation characteristics, the propagation path can be divided into a LOS path and a NLOS path. In the LOS path, the signal propagation path is direct, there is no obvious obstacle blocking, and the loss of the signal in the propagation process is relatively low. This low loss characteristic enables the LOS environment to support higher data rate transmission, meeting the needs of application scenarios such as high-definition video streaming, real-time data interaction, and other high-bandwidth and high-transmission rate requirements. In contrast, in the NLOS path, there are various obstacles in the signal propagation path, and the signal needs to go through complex processes such as reflection, refraction, or scattering to reach the receiving end. This complex propagation process can cause significant signal attenuation, and also introduces obvious delay and multipath effects. Multipath effects can cause the receiving end to receive multiple signal copies with different time delays and amplitudes, causing signal distortion and interference, and seriously affecting communication quality.

[0059] In the integration of sensing and communication scenarios, accurately identifying the current path (LOS or NLOS) is of great significance. Network devices can dynamically adjust signal transmission parameters such as power control, modulation mode, and coding strategy according to the identification result of the path. By reasonably adjusting these parameters, communication performance can be optimized, signal transmission reliability and effectiveness can be improved, and the error rate can be reduced.

[0060] In practical applications of positioning and navigation, the accuracy of path identification has a significant impact on positioning accuracy. Among them, the LOS path can usually provide more accurate distance and angle information, which is the basis for accurate calculation of target position. However, under the NLOS path, due to the complexity of signal propagation, it may introduce larger errors, resulting in inaccurate positioning results. Therefore, by effectively identifying the LOS / NLOS path, the network device can select the positioning algorithm corresponding to the actual path, eliminate the signal path with large positioning error, thereby significantly improving the positioning accuracy, meeting the requirements of application scenarios such as intelligent transportation, unmanned aerial vehicle navigation, etc. which have very high requirements for positioning accuracy.

[0061] The inventors found that some terminals (such as UAVs) currently have high mobility, and their positions change frequently, resulting in frequent switching between LOS and NLOS signal propagation paths. The current accuracy of identifying LOS and NLOS is low, which on the one hand, in the perception layer, the detection accuracy of the terminal is also low, and in a complex environment, the terminal cannot be perceived in time, and the accuracy of positioning the terminal is also low. On the other hand, in the communication layer, the low accuracy of LOS / NLOS identification also has a certain impact on beamforming and path optimization, for example, the low accuracy of LOS / NLOS identification leads to poor accuracy of beam direction adjustment and propagation path selection, which in turn leads to low link quality, more signal interruption and fading, and poor stability and reliability of communication.

[0062] Therefore, the present application provides a signal processing method, by receiving the first information related to the building density and / or height of the position of the sensing target sent by the sensing target for determining the path identification decision threshold, based on the first information, and the comparison result of the log-likelihood ratio of the first path and the second path, determine whether the first path or the second path is used for the first signal for communication sensing function. By the method of the present application, when identifying the path, further introduce the building density, height and other parameters related to the path identification accuracy corresponding to the position of the terminal, improve the accuracy of real-time path identification, make the accuracy of the path used for sending the first signal higher, thereby improve the detection accuracy of the sensing target, the accuracy of positioning the sensing target, also improve the accuracy of beam direction adjustment and propagation path selection in the communication process, and further improve the stability of the communication.

[0063] The signal processing method of the present application will be described in detail below with reference to the accompanying drawings. The execution subject of the embodiments shown in the present application is a network device. The network device in the embodiments of the present application can be the network device itself, or a chip, chip system or processor supporting the network device to implement the task processing method, or a logic module or software that can implement all or part of the network device functions. The present application does not make specific limitations on this.

[0064] Figure 2 A flowchart of a signal processing method provided by an embodiment of the present application is shown in FIG. 2. As shown in FIG. 2, the method can include the following steps. Figure 2

[0065] S201: receiving first information sent by a sensing target.

[0066] The first information is used to indicate a decision threshold, and the decision threshold is related to the building density and / or height of the location of the sensing target. The sensing target referred to in the present application is a sensing target in an outdoor scene, i.e., the terminal mentioned above, which can be a UAV or other terminal with high mobility, such as a vehicle, etc., and the present application does not limit this.

[0067] When the sensing target is a flying terminal such as a UAV, the decision threshold can be related to the building density of the location of the flying terminal, or related to the height of the location of the flying terminal, or related to the building density and height of the location of the flying terminal, etc. When the sensing target is a non-flying terminal such as a vehicle, the decision threshold can be related to the building density of the location of the terminal. The higher the building density, the greater the probability of being in NLOS, and the higher the flying height, the greater the probability of being in LOS.

[0068] The decision threshold is used to determine whether the path used to send the first signal is the first path or the second path. The first path can be LOS or NLOS, and when the first path is LOS, the second path is NLOS, and when the first path is NLOS, the second path is LOS. Optionally, in addition to LOS and NLOS, the path used by the first signal can also be a low-attenuation path, a high-attenuation path, or other paths that affect the transmission parameters of the first signal, etc., and the present application does not limit this. In subsequent embodiments, the first path is LOS and the second path is NLOS, i.e., the positioning error of the second path (NLOS) is greater than that of the first path (LOS).

[0069] Specifically, the log-likelihood ratio (LLR) of the first path and the second path can be calculated, and the log-likelihood ratio is compared with the decision threshold. If the LLR is greater than or equal to the decision threshold, it indicates that the sensing target is more likely to be in the first path, and step S202 is performed; if the LLR is less than the decision threshold, it indicates that the sensing target is more likely to be in the second path, and step S203 is performed.

[0070] S202: sending the first signal using the first path.

[0071] ​The first signal is a signal sent by the network device for the dual purposes of communication and sensing. For example, the first signal can be used for communication with a sensing target (for example, the first signal can carry user data, enabling information exchange between the network device and a sensing target such as a UAV or a vehicle, or the first signal can include a control instruction for guiding the movement, operation, or other behavior of the sensing target). The first signal can also be used to sense the location, shape, or the like of a sensing target that the network device needs to sense (for example, the shape of a sensing target such as a UAV or a vehicle can be sensed by analyzing the propagation characteristics such as the time of arrival, angle of arrival, or signal strength of the first signal). For example, the first signal can be a baseband signal, a radio frequency signal, a millimeter wave signal, a radar signal, or the like.

[0072] Specifically, the first signal is transmitted using the first path, that is, the first signal is transmitted using transmission parameters corresponding to the first path. For example, if the first signal is transmitted using the transmission parameters corresponding to the LOS path, the transmission power can be appropriately reduced to save energy consumption because the signal attenuation of the LOS path is small, a high-frequency signal frequency can be selected to provide high bandwidth and resolution, a high-order modulation can be selected to improve the data transmission rate because the signal-to-noise ratio of the LOS path is high, a high-gain directional antenna can be used to enhance the directional propagation capability of the signal, precise beamforming can be used to concentrate the signal energy in the target direction, high-efficiency error correction coding can be used to improve the data transmission efficiency because the signal-to-noise ratio of the LOS path is high, and a wideband signal can be used to provide a high data transmission rate because the LOS path is suitable for a wideband signal.

[0073] S203, transmitting the first signal using the second path.

[0074] Specifically, the first signal is transmitted using the second path, that is, the first signal is transmitted using transmission parameters corresponding to the second path. For example, if the first signal is transmitted using the transmission parameters corresponding to the NLOS path, the transmission power can be increased to compensate for the signal attenuation because the signal attenuation of the NLOS path is large, a low-frequency signal frequency can be selected to improve the signal penetration, a low-order modulation can be used to ensure the signal reliability because the signal-to-noise ratio of the NLOS path is low, an omnidirectional or low-gain antenna can be used because the NLOS path needs to consider a wider coverage range, wide beam or adaptive beamforming can be used to cope with the multipath effect because the NLOS path, the NLOS path needs stronger error correction capability and can use basic error correction coding, and a narrowband signal can be used because the signal attenuation of the NLOS path is large and the narrowband signal is easier to penetrate obstacles.

[0075] The method provided in the embodiments of the present application receives first information sent by the sensing target, the first information being used to indicate a decision threshold related to the building density and / or height of the location where the sensing target is located, in the case that the LLRs of the first path and the second path are greater than or equal to the decision threshold, the first signal is sent by using the first path, and in the case that the LLRs of the first path and the second path are less than the decision threshold, the first signal is sent by using the second path. The method of the present application further introduces the parameters related to the building density, height and the like corresponding to the location where the terminal is located and having a higher correlation with the path identification accuracy in the identification of the path, thereby improving the accuracy of the real-time identification of the path, making the accuracy of the path used for sending the first signal higher, and thereby improving the detection accuracy of the sensing target and the accuracy of the positioning of the sensing target.

[0076] Optionally, if the decision threshold is only related to the building density, the first information includes a first parameter used to determine the building density of the location where the sensing target is located, for example, the first parameter can be the building density, or can be a parameter used to determine the building density (for example, the positioning location of the sensing target). Taking the example that the first parameter includes the positioning location of the sensing target, the network device can receive the positioning location of the sensing target sent by the sensing target, and analyze the number of buildings in the area where the sensing target is located according to the positioning location, to determine the shielding condition (for example, in the city center where there are many high-rise buildings, the probability of being in NLOS is relatively high, while in the low-density or open area, the probability of being in LOS is improved). Specifically, the number of buildings in the area around the positioning location can be obtained through an open source map website, so as to calculate the building density. How to calculate the building density can refer to the prior art, and will not be described here.

[0077] If the decision threshold is only related to the height, the first information includes a second parameter used to determine the height of the location where the sensing target is located, for example, the second parameter can be the height, or can be a parameter used to determine the height (for example, the positioning location of the sensing target, barometer sensor data, inertial measurement unit data, etc.). When the height of the sensing target is low, the influence of the ground obstacle is large, and the probability of being in NLOS is high, while when the height of the sensing target is high, the influence of the ground obstacle is small, and the probability of being in LOS is high.

[0078] If the decision threshold is related to the building density and the height, the first information includes the first parameter and the second parameter.

[0079] Next, based on the above embodiments, the specific implementation modes of the following several different determination of the decision threshold are introduced in detail.

[0080] Implementation mode 1: the decision threshold is related to the building density.

[0081] Figure 3 Another flowchart of a signal processing method is provided for the embodiments of the present application. As shown in the flowchart, the method can include: Figure 3

[0082] S301, obtaining a first prior probability function corresponding to a first path.

[0083] The first prior probability function is related to a building density reference value and a building density attenuation factor. Taking the first path as an LOS for example, the first prior probability function corresponding to the LOS can be obtained according to an analysis of building densities in different density cities or regions. For example, the first prior probability function can be determined according to actual situation analysis as shown in the following formula (1) for example:

[0084] (1)

[0085] wherein, is a first path prior probability, is a first parameter (for example, which can be a building density), is a building density attenuation factor, is a building density reference value.

[0086] For example, according to actual analysis, the building density in a high-density city is about 800 buildings per square kilometer, and the building density in a low-density city is about 200 buildings per square kilometer. Based on this analysis, the building density reference value and the building density attenuation factor can be determined according to actual situation. For example, according to actual analysis, the building density reference value is determined to be 300 buildings per square kilometer (i.e. ), and the building density attenuation factor is determined to be 0.005 (i.e. ). In this example, the values of the first parameter and the first path prior probability corresponding to the first prior probability function can be referred to Table 1 shown below:

[0087] Table 1

[0088]

[0089] The first prior probability function can be pre-constructed and stored in a network device, or can be delivered to the network device by another device (for example, a core network device), or can be pre-defined by a protocol, and the present application does not limit this.

[0090] It should be understood that the above is only an example of a first prior probability function provided for convenience of understanding, and the corresponding first prior probability function can be determined according to actual needs in actual scenarios, and is not limited to the above first prior probability function.

[0091] ​S302, generating the first path prior probability according to the first prior probability function and the first parameter.

[0092] In this step, based on the first prior probability function provided in the above step S301, the first parameter is substituted into the first prior probability function, and the first path prior probability is calculated. Alternatively, the first path prior probability corresponding to the first parameter can be obtained based on the mapping relationship between the first parameter and the first path prior probability, and the mapping relationship is the mapping relationship represented by the first prior probability function in the above step S301.

[0093] S303, obtaining the second path prior probability according to the first path prior probability.

[0094] In this step, since one of the first path and the second path is the target hypothesis and the other is the alternative hypothesis, that is, when the first path is the target hypothesis, the second path is the alternative hypothesis; when the second path is the target hypothesis, the first path is the alternative hypothesis. Therefore, in order to facilitate calculation, the first path prior probability and the second path prior probability can be set to be negatively correlated. For example, the sum of the first path prior probability and the second path prior probability can be set to 1.

[0095] After obtaining the first path prior probability through the above step S302, the second path prior probability can be calculated through the correlation between the first path prior probability and the second path prior probability. Taking the sum of the first path prior probability and the second path prior probability as 1 as an example, if the first path prior probability is 0.5, then the second path prior probability is 1-0.5=0.5.

[0096] Alternatively, the second path prior probability corresponding to the first prior probability function can be determined according to the actual scene analysis, and the second path prior probability can be calculated by substituting the first parameter into the prior probability function. Here, it is not repeated.

[0097] S304, determining the decision threshold according to the first path prior probability and the second path prior probability.

[0098] In this step, according to the Bayes theory, in order to make the misjudgment rate of path recognition meet the actual demand, the optimal decision threshold satisfies the following formula (2):

[0099] (2)

[0100] wherein, is the decision threshold, is the first path prior probability (LOS prior probability), is the second path prior probability (NLOS prior probability). That is Probability of occurrence of the LOS path in a real scene, Probability of occurrence of the NLOS path in a real scene.

[0101] Implementation 2: The decision threshold is related to the height.

[0102] Figure 4 A flowchart of another signal processing method provided by an embodiment of the present application is shown in FIG. 6. As shown in the figure, the method can include the following steps. Figure 4

[0103] S401, obtaining a second prior probability function corresponding to a first path.

[0104] The second prior probability function is related to a height reference value and a height attenuation factor. Taking the LOS as an example, the second prior probability function corresponding to the LOS can be obtained according to analysis of the flight terminal at different heights. For example, the second prior probability function can be determined according to actual situation analysis as shown in the following formula (3) for example:

[0105] (3)

[0106] wherein, is the first path prior probability, is a second parameter (for example, the height of the location where the target is perceived), is a height attenuation factor, is a height reference value.

[0107] For example, it is assumed that according to actual analysis, the height of a general urban building will not exceed 200 meters, and when the height of the perceived target is 80 meters, the first path prior probability and the second path prior probability are both close to 0.5, that is, the probabilities of being in the LOS and the NLOS are close. Therefore, based on this analysis, the height reference value and the height attenuation factor can be determined according to the actual situation. For example, according to actual analysis, the height reference value is determined to be 80 meters (i.e. ), and the height attenuation factor is determined to be 0.01 (i.e. ). In this example, the values of the second parameter and the first path prior probability corresponding to the second prior probability function can be referred to Table 2 shown below:

[0108] Table 2

[0109]

[0110] The second prior probability function can be pre-constructed and stored in the network device, can be issued to the network device by another device (for example, a core network device), or can be pre-defined by a protocol, and the present application does not limit this. ​

[0111] It should be appreciated that the above is only an exemplary second prior probability function provided for the convenience of understanding, and the corresponding second prior probability function can be determined according to actual needs in actual scenarios, and is not limited to the above second prior probability function.

[0112] S402, generating a first path prior probability according to the second prior probability function and the second parameter.

[0113] In this step, based on the second prior probability function provided in the above step S401, the second parameter is substituted into the second prior probability function, and the first path prior probability is calculated and obtained. Alternatively, the first path prior probability corresponding to the second parameter can be obtained based on the mapping relationship between the second parameter and the first path prior probability, and the mapping relationship is the mapping relationship represented by the second prior probability function in the above step S401.

[0114] S403, obtaining a second path prior probability according to the first path prior probability.

[0115] Among them, since one of the first path and the second path is the target hypothesis, and the other is the alternative hypothesis, that is, when the first path is the target hypothesis, the second path is the alternative hypothesis; when the second path is the target hypothesis, the first path is the alternative hypothesis. Therefore, in order to facilitate calculation, the first path prior probability and the second path prior probability can be set to be negatively correlated. For example, the sum of the first path prior probability and the second path prior probability can be set to 1.

[0116] After obtaining the first path prior probability through the above step S402, the second path prior probability can be calculated through the correlation between the first path prior probability and the second path prior probability. Taking the sum of the first path prior probability and the second path prior probability as 1 as an example, if the first path prior probability is 0.5, then the second path prior probability is 1-0.5=0.5.

[0117] Alternatively, the prior probability function corresponding to the second path prior probability can be determined according to the construction method of the second prior probability function, and the second path prior probability can be calculated by substituting the second parameter into the prior probability function. Here, it is not repeated.

[0118] S404, determining a decision threshold according to the first path prior probability and the second path prior probability.

[0119] The implementation manner of this step is the same as the above step S304, and will not be repeated here.

[0120] Implementation manner 3: the decision threshold is related to the building density and height.

[0121] Figure 5Another flowchart of a signal processing method is provided for the embodiments of the present application. As shown in Figure 5 the method can include:

[0122] S501, acquiring a first prior probability function corresponding to the first path, a second prior probability function, a first weight of the first prior probability function, and a second weight of the second prior probability function.

[0123] The first prior probability function is the first prior probability function mentioned in the foregoing Figure 3 The second prior probability function is the second prior probability function mentioned in the foregoing Figure 4 Since the influence of the building density and height of the location where the perception target is located on the decision threshold needs to be considered in the present implementation, the mapping relationship between the first parameter and the second parameter and the first path prior probability can be determined by fusing the first prior probability function and the second prior probability function corresponding to the first path in a weighted manner.

[0124] Optionally, the first weight and the second weight can be set to 0.5, or can be adaptively adjusted according to the location where the perception target is located. For example, when the perception target is located in a high-density city or a core commercial area of a city, or other areas with high building density, the building density is the main factor for identifying LOS / NLOS, the first weight can be set to a higher value, and the second weight can be set to a lower value, for example, the first weight can be set to 0.8, and the second weight can be set to 0.2; when the perception target is located in a low-density city or an open area, or other areas with low building density, the height is the main factor for identifying LOS / NLOS, the second weight can be set to a higher value, and the first weight can be set to a lower value, for example, the second weight can be set to 0.8, and the first weight can be set to 0.2.

[0125] Optionally, any one of the first prior probability function, the second prior probability function, the first weight, and the second weight can be pre-constructed and stored in the network device, can be issued to the network device by other devices (for example, a core network device), or can be pre-defined by a protocol, and the present application does not limit this. That is, each of the first prior probability function, the second prior probability function, the first weight, and the second weight can be acquired in the same way, or at least two of them can be acquired in different ways.

[0126] S502, generating a first path prior probability according to the first prior probability function, the second prior probability function, the first weight, the second weight, the first parameter, and the second parameter.

[0127] Wherein, taking the first prior probability function and the second prior probability function as an example, the mapping relationship between the first path prior probability and the first parameter and the second parameter can be shown in the following formula (4):

[0128] (4)

[0129] Wherein, is the first path prior probability, is the first weight, is the second weight, is the first parameter, is the second parameter.

[0130] In this step, the first weight, the second weight, the first parameter and the second parameter are substituted into the above formula (4), and the first path prior probability (i.e. the LOS prior probability) can be calculated and obtained.

[0131] S503, obtaining the second path prior probability according to the first path prior probability.

[0132] In this step, the way of obtaining the second path prior probability is the same as the way of obtaining the second path prior probability according to the first path prior probability in the above steps S303 and S403, which will not be repeated here.

[0133] S504, determining the decision threshold according to the first path prior probability and the second path prior probability.

[0134] The implementation of this step is the same as the above steps S304 and S404, which will not be repeated here.

[0135] The three methods for generating a decision threshold provided by the embodiments of the present application determine the mapping relationship between the first path prior probability and the first parameter and / or the second parameter according to the actual situation, combine the first parameter and / or the second parameter extracted from the first information to obtain the first path prior probability, and obtain the second path prior probability according to the first path prior probability, so as to determine the decision threshold according to the first path prior probability and the second path prior probability. The method further introduces parameters such as building density and height corresponding to the location of the terminal, which have a higher correlation with path recognition accuracy, when recognizing the path, and dynamically determines the corresponding decision threshold according to the location of the sensing target, so that the decision threshold is dynamically related to the current state of the sensing target, thereby improving the accuracy of real-time path recognition, making the accuracy of the path used for sending the first signal higher, and thus improving the detection accuracy of the sensing target and the accuracy of positioning the sensing target.

[0136] In a possible implementation, a time window related to the communication environment in which the sensing target is located can be introduced when the decision threshold is dynamically determined, so as to further improve the accuracy of the decision threshold. The time window related to the communication environment in which the sensing target is located can also be aligned with the retransmission rhythm of the physical layer in the communication system, so as to improve the adaptability of the dynamically determined decision threshold to the communication system, thereby optimizing the judgment process of the decision threshold, and further improving the accuracy and applicability of path identification.

[0137] In this implementation, the decision threshold can be related to the first parameter, or related to the second parameter, or related to both the first parameter and the second parameter. Taking the case that the decision threshold is related to both the first parameter and the second parameter as an example, the first parameter is related to the densities of multiple buildings in the target time window, and the second parameter is related to the heights of multiple buildings in the target time window.

[0138] The target time window can be determined according to the communication environment in which the sensing target is located, for example, according to the cell in which the sensing target is currently located, the motion speed of the sensing target, the signal transmission delay of the communication environment in which the sensing target is located, and the like. For example, the length of the target time window belongs to a target length interval, which can be determined according to the communication environment in which the sensing target is located and based on actual judgment requirements.

[0139] For example, the lower limit of the target length interval can be greater than or equal to a first value, and the first value is greater than or equal to the signal transmission delay of the sensing target. The signal transmission delay of the sensing target refers to the physical layer delay from the sending of a signal data packet to the receipt of an ACK / NACK feedback corresponding to the signal data packet when the network device and the sensing target perform data transmission. The signal transmission delay represents that one complete HARQ cycle (for example, including the processes of sending, receiving, and feedback of a data packet, and possible retransmission) must be covered in the target time window. If the target length interval is set too short, for example, short enough to fail to cover one complete HARQ cycle, it can cause the loss of signal data packets or the delay of retransmission, and reduce the reliability of communication. Therefore, by setting the lower limit of the target length interval to be greater than or equal to the first value, it can be ensured that the target length interval can cover one complete HARQ cycle, and the stability of communication is improved, thereby further improving the stability and accuracy of the determination of the decision threshold.

[0140] Optionally, the upper limit of the target length interval can be less than or equal to a second value, the second value being related to the motion speed of the sensing target and the effective radius of the cell where the sensing target is located. For example, the second value can be calculated according to the motion speed of the sensing target and the effective radius of the cell where the sensing target is located, such as the second value being equal to the effective radius of the cell where the sensing target is located divided by the motion speed of the sensing target (i.e. the second value being equal to the quotient of the effective radius related and the motion speed), or the second value being less than the effective radius of the cell where the sensing target is located divided by the motion speed of the sensing target. The second value represents that the sensing target should not fly out of the current cell to which the sensing target belongs within a target time window. If the sensing target flies out of the current cell to which the sensing target belongs within a target time window, it can cause communication interruption, or frequent cell switching, thereby increasing the complexity and instability of communication, and reducing the stability and accuracy of determining the decision threshold. By setting the upper limit of the length of the target time window as that the sensing target should not fly out of the current cell to which the sensing target belongs within the target time window, the complexity and instability caused by communication interruption or cell switching are reduced, thereby further improving the stability and accuracy of determining the decision threshold.

[0141] In the target time window, a plurality of building densities and a plurality of heights of the sensing target can be obtained from the sensing target, and then a first parameter is determined according to the plurality of building densities, and a second parameter is determined according to the plurality of heights. For example, the plurality of building densities within the target time window can be fused to obtain a fusion result as the first parameter, and the plurality of heights within the target time window can be fused to obtain a fusion result as the second parameter. The fusion calculation can be, for example, calculating the mean, median, etc. of the plurality of data, which is not limited in the present application. In addition, the fusion calculation of the first parameter and the second parameter can be the same or different.

[0142] After the first parameter and the second parameter corresponding to the target time window are determined, the first path prior probability related to the first parameter and the second parameter can be obtained based on the foregoing Figure 5 The first path prior probability and the second path prior probability can be obtained based on the first path prior probability, and then the decision threshold is determined based on the first path prior probability and the second path prior probability. The decision threshold can be used for path identification of the first signal sent in the subsequent time window, for example, for path identification of the first signal sent in the next time window.

[0143] Next, how to determine the LLR of the first path and the second path in the foregoing embodiments is described in detail.

[0144] Figure 6 Another signal processing method provided by the embodiments of the present application is shown in the flowchart. As Figure 6 shown, the method can further include:

[0145] S601, acquire an observation parameter of a sample signal.

[0146] The sample signal includes a sample signal under a first path and a sample signal under a second path. In the case of the first path being LOS and the second path being NLOS, the sample signal includes a sample signal under LOS and a sample signal under NLOS.

[0147] The observation parameter of the sample signal includes one or more of skewness, kurtosis, peak-to-average ratio, angle-of-arrival skewness, angle-of-arrival variance, and time delay. The sample signal and the observation parameter of the sample signal can be pre-acquired, and can be extracted from a database or a storage device that stores the observation parameter of the sample signal.

[0148] The skewness refers to the asymmetry of the amplitude distribution of the sample signal. In the LOS path, the signal transmission is relatively direct, and there is a clear main path, so the signal amplitude is relatively concentrated around the main path, and the asymmetry of the amplitude distribution is low, so the skewness value is small. In the NLOS path, the signal will pass through multiple paths such as reflection and scattering to reach the receiving end, and the signal amplitudes of different paths differ greatly, resulting in more dispersed sample signal amplitude distribution and enhanced asymmetry, so the skewness value is relatively high.

[0149] The kurtosis reflects the sharpness of the amplitude distribution of the sample signal. In the LOS path, the existence of the main path causes the signal amplitude to be concentrated around a certain value, and the distribution curve presents a sharp peak and a heavy tail, that is, a high kurtosis characteristic. This means that there are some strong signal components in the signal whose amplitudes are significantly higher than other parts. In the NLOS path, the multi-path effect causes the signal amplitude distribution to be more uniform, without obvious peaks, and the distribution is relatively flat, so the kurtosis value is low.

[0150] The peak-to-average ratio is the ratio of the peak amplitude of the sample signal to the average amplitude, and the peak-to-average ratio reflects the fluctuation of the instantaneous power of the signal. In the LOS path, there is a clear strong path in the signal, which causes the signal to have a large peak value at some moments, and the average amplitude is relatively small, so the peak-to-average ratio is high. In the NLOS path, due to the superposition effect of multiple paths, the signal power distribution is relatively average, the peak amplitude and the average amplitude are not much different, and the peak-to-average ratio is low.

[0151] The angle of arrival (AoA) is the angle between the direction of signal propagation and the reference axis of the receiving antenna array of the receiving end (e.g., the sensing target in the present application). The skewness of the angle of arrival measures the asymmetry of the distribution of the angles of arrival of the sample signals. In the LOS path, the signal mainly propagates along a relatively fixed direction, and when it reaches the receiving antenna array, the angles of arrival are concentrated around a certain angle, the distribution is relatively concentrated, the asymmetry is low, and therefore the skewness of the angle of arrival is small. In the NLOS path, due to the multipath effect, the signal reaches the receiving antenna array from multiple different directions, the distribution of the angles of arrival is relatively dispersed, the asymmetry is high, and the skewness of the angle of arrival is large.

[0152] The variance of the angle of arrival is used to measure the dispersion degree of the distribution of the angles of arrival of the sample signals. In the LOS path, the angles of arrival of the signals are concentrated in a small angle range, the difference between each angle of arrival and the average angle of arrival is small, and the variance of the angle of arrival is small. In the NLOS path, the multipath effect causes the angles of arrival of the signals to be dispersed in multiple different angles, the difference between each angle of arrival and the average angle of arrival is large, and the variance of the angle of arrival is large. The larger the variance of the angle of arrival, the higher the dispersion degree of the direction of arrival of the signal.

[0153] The time delay reflects the time experienced by the sample signal from the transmitting end (e.g., the network device in the present application) to the receiving end (e.g., the sensing target in the present application). In the LOS path, the signal directly propagates from the transmitting end to the receiving end, the propagation path is short, and therefore the time delay is small. In the NLOS path, the signal needs to go through reflection, scattering, etc., the propagation path becomes longer, resulting in a larger time delay.

[0154] For example, the calculation methods of the skewness, the kurtosis, and the peak-to-average ratio can refer to the following formulas (5) to (7):

[0155] (5)

[0156] (6)

[0157] (7)

[0158] wherein the formula (5) is the calculation formula of the skewness, the formula (6) is the calculation formula of the kurtosis, and the formula (7) is the calculation formula of the peak-to-average ratio. is the amplitude value of the sample signal, is the amplitude mean value of the sample signal, is the amplitude standard deviation of the sample signal, and N is the number of the sample signals.

[0159] For the variance of the angle of arrival, the angle of arrival can be extracted using the Fast Fourier Transform (FFT) peak finding method when the receiver (i.e., the sensing target) is a uniform linear array. Based on the extracted angle of arrival The variance of the angle of arrival is calculated, and this calculation method can refer to existing techniques, which will not be elaborated here. For the skewness of the angle of arrival, the angle of arrival can be extracted. The skewness is calculated from the distribution. Since the angle values ​​are generally discrete, it is assumed that the angle of arrival... The value is ,in Characterizing the first The angle of arrival of each sample signal is taken. Then, based on... The mean was calculated. Then, the arrival angle skewness is obtained based on the following formula (8):

[0160] (8)

[0161] The time delay can be obtained in the following two ways:

[0162] Method 1: Extract the time delay (also known as the time delay difference) by using correlation analysis of auxiliary signals.

[0163] In this approach, the type of sample signal is not limited; it can be either a cyclically stationary signal or a non-cyclically stationary signal. The auxiliary signal is a known signal (e.g., called the transmitted signal) sent by the transmitting end (which can be a network device or a sensing target). The transmitted signal can be, for example, a pseudo-random sequence signal or a pulse signal. The received auxiliary signal (e.g., referred to as the received signal) is recorded at the receiving end. ). By calculating the received signal With the transmitted signal The cross-correlation function is used to obtain the time delay of the sample signal.

[0164] The cross-correlation function is shown in the following formula (9):

[0165] (9)

[0166] By determining the cross-correlation function shown in the above formula (9) The maximum value corresponding to The time delay can then be determined as .

[0167] Method 2: Calculate the time delay using the cyclic autocorrelation function of the sample signal.

[0168] This method is used when the sample signal is a cyclically stationary signal (e.g., a linearly frequency modulated signal). For cyclically stationary signals... Its cyclic autocorrelation function is shown in the following formula (10):

[0169] (10)

[0170] in, For time delay, The cycle frequency of the sample signal. The length of the time interval. The imaginary unit. At the cycle frequency... Under these conditions, the cyclic autocorrelation function of the sample signal will have a significant peak, and the time delay corresponding to the peak position is the time delay of the sample signal.

[0171] The two methods for calculating the time delay of sample signals described above are only briefly introduced here. For details, please refer to existing technologies. Further details will not be elaborated here.

[0172] S602. Based on the observation parameters of the sample signal, fit and generate at least two candidate Gaussian mixture models.

[0173] In this step, it is assumed that the distribution of the observed parameters of the sample signal conforms to a Gaussian Mixture Model (GMM) consisting of a weighted linear combination of multiple Gaussian distributions. Based on the observed parameters of the sample signal, the GMM is fitted to the observed parameters of the sample signal by estimating the GMM consisting of different numbers of Gaussian distributions, as well as the parameters of each Gaussian distribution in each GMM (e.g., the mean, variance, and weights of each Gaussian distribution). This yields at least two candidate GMMs corresponding to the observed parameters of the sample signal, where the number of Gaussian distributions included in these at least two candidate GMMs is different.

[0174] The formula for fitting the Gaussian mixture model is shown in formula (11) below:

[0175] (11)

[0176] in, This represents the probability density of the sample points corresponding to the sample signal. These are the observation parameters of each sample signal (i.e., the sample signal's observed parameters). In the Gaussian mixture model, the first The weights of a Gaussian distribution In the Gaussian mixture model, the first The mean of a Gaussian distribution, In the Gaussian mixture model, the first The standard deviation of a Gaussian distribution.

[0177] S603, determining the target Gaussian mixture model from the candidate Gaussian mixture models based on Bayesian Information Criterion.

[0178] The Bayesian Information Criterion (BIC) is a model selection criterion used to balance the complexity of the model and the goodness of fit when fitting a statistical model. Based on the Bayesian Information Criterion, the optimal value of the number of Gaussian components included in the Gaussian mixture model can be determined.

[0179] In this step, the BIC value The calculation formula can refer to the following formula (12):

[0180] (12)

[0181] Wherein, is the maximum likelihood estimate of the candidate Gaussian mixture model, indicating the likelihood value of the observed parameters of the sample signal under the parameters of the candidate Gaussian mixture model, and the parameters indicate the weight , mean , and standard deviation of the Gaussian distribution included in the candidate Gaussian mixture model. The number of parameters in the candidate Gaussian mixture model, for example, the candidate Gaussian mixture model includes Gaussian distribution, the parameters of the candidate Gaussian mixture model include , , , , , , . The number of sample signals.

[0182] In formula (12), is used to measure the goodness of fit of the candidate Gaussian mixture model to the observed parameters of the sample signal, the larger the likelihood value of the candidate Gaussian mixture model, the smaller, indicating that the candidate Gaussian mixture model is better fitted to the data (i.e. the observed parameters of the sample signal). is the penalty term of the complexity of the candidate Gaussian mixture model, the larger the number of parameters of the candidate Gaussian mixture model, the larger the penalty value, to prevent overfitting of the candidate Gaussian mixture model.

[0183] Generally, it will first decrease to a minimum value with the increase of the number of Gaussian distributions included in the candidate Gaussian mixture model, and then Then the growth is performed. In this process, the smallest The corresponding number of Gaussian distributions corresponds to the candidate Gaussian mixture model as the target Gaussian mixture model, so as to reduce the overfitting condition while ensuring the fitting effect of the Gaussian mixture model on the observation parameters of the sample signal.

[0184] S604, based on the observation parameters of the second signal, the target Gaussian mixture model, generating the LLR.

[0185] The second signal is a signal before the first signal, for example, it can be the last signal used for communicating with the sensing target or sensing the sensing target before sending the first signal, or it can be the previous multiple signals used for communicating with the sensing target or sensing the sensing target before sending the first signal, etc.

[0186] The observation parameters of the second signal include at least one of skewness, kurtosis, peak-to-average ratio, angle of arrival skewness, angle of arrival variance, time delay. The observation parameters can refer to the aforementioned description of the observation parameters of the sample signal, which will not be repeated here.

[0187] In this step, taking the first path as LOS and the second path as NLOS, and taking the observation parameters of the second signal including skewness, kurtosis, peak-to-average ratio, angle of arrival skewness, and time delay as an example, the LLR can be generated based on the observation parameters of the second signal and the target Gaussian mixture model. The formula (13) is as follows:

[0188] (13)

[0189] The formula (13) can be converted into formula (14) or formula (15):

[0190] (14)

[0191] (15)

[0192] Wherein, is the LLR of the first path and the second path conforming to the target Gaussian mixture model; , that is, the prior LLR of the first path and the second path conforming to the target Gaussian mixture model; , that is, the posterior LLR of the first path and the second path conforming to the target Gaussian mixture model; Corresponding to the five observation parameters of the skewness, kurtosis, peak-to-average ratio, angle of arrival skewness, and time delay of the second signal, respectively.

[0193] The method provided in the embodiments of the present application comprises the following steps: obtaining an observation parameter of a sample signal, fitting at least two candidate Gaussian mixture models based on the observation parameter of the sample signal, determining a target Gaussian mixture model from the candidate Gaussian mixture models based on a Bayesian information criterion, and generating an LLR according to the observation parameter of a second signal, and the target Gaussian mixture model. The method selects an observation parameter having a significant difference in a LOS path and a NLOS path, fits a target Gaussian mixture model conforming to a signal distribution, and calculates an LLR for identifying a path in which a sensing target is located according to an actual observation parameter of the second signal previously transmitted, thereby improving the accuracy of path identification.

[0194] Optionally, when the observation parameter of the second signal comprises an angle of arrival skewness or an angle of arrival variance, whether the angle of arrival skewness or the angle of arrival variance is used as a parameter in the observation parameter of the second signal can be determined according to an angle of arrival spread of the second signal. That is, the observation parameter of the second signal comprises the angle of arrival skewness in some cases and comprises the angle of arrival variance in other cases. The angle of arrival spread may, for example, be an RMS angle of arrival spread.

[0195] In this implementation, when the angle of arrival spread is large, it is indicated that there are rich multipaths in the signal propagation process and there is no significant main path, at this time, the angle of arrival variance changes greatly and can more clearly assist in identifying the LOS and the NLOS. When the angle of arrival spread is small, it is indicated that the power and the angle are concentrated on a main path in the signal propagation process, and the angle of arrival presents a case of a cluster of main paths plus a few sparse reflections, at this time, the change of the angle of arrival skewness is more obvious and can more clearly assist in identifying the LOS and the NLOS.

[0196] Therefore, in this implementation, whether the angle of arrival skewness or the angle of arrival variance is used in the observation parameter of the second signal can be determined by comparing the angle of arrival spread of the second signal with a preset threshold. For example, in a case where the angle of arrival spread of the second signal is greater than or equal to a first preset threshold, the observation parameter comprises the angle of arrival variance; and in a case where the angle of arrival spread of the second signal is less than a second preset threshold, the observation parameter comprises the angle of arrival skewness. The second preset threshold is less than or equal to the first preset threshold, that is, the second preset threshold can be the same as the first preset threshold or can be any value less than the first preset threshold. The above-described preset threshold can be determined according to actual requirements, and the present application does not limit this.

[0197] For example, taking the RMS angle of arrival spread as an example, the calculation formula of the RMS angle of arrival spread can refer to the following formula (16):

[0198] (16)

[0199] wherein, , represents the power of the first path, represents the angle of arrival of the first path, represents the power-weighted average angle of arrival.

[0200] For example, assuming that the second preset threshold is equal to the first preset threshold, the first preset threshold can be 15° in a sub-6Ghz urban micro (UMi) scenario; and the first preset threshold can be 8° in a millimeter wave small cell scenario.

[0201] Figure 7 A structural diagram of a signal processing apparatus provided by an embodiment of the present application is shown. It can be understood that the signal processing apparatus can correspond to the operation or steps of the network device in the foregoing various method embodiments. The signal processing apparatus can be a network device or can be a component, such as a chip, a chip module, or the like, which can be configured to the network device. As shown in the figure, the signal processing apparatus can include a receiving module 11 and a control module 12. Figure 7

[0202] The receiving module 11 receives first information sent by a sensing target, and the first information is used to indicate a decision threshold, the decision threshold being related to a building density and / or a height of a location of the sensing target.

[0203] The control module 12 uses a first path to send a first signal in a case where a log likelihood ratio (LLR) of the first path and a second path is greater than or equal to the decision threshold. In a case where the LLR is less than the decision threshold, the control module 12 uses a second path to send the first signal, the second path having a positioning error greater than the first path.

[0204] Optionally, the first information includes a first parameter and / or a second parameter, the first parameter being used to determine the building density of the location of the sensing target, and the second parameter being used to determine the height of the location of the sensing target.

[0205] Optionally, the first parameter includes a positioning location of the sensing target.

[0206] Optionally, the decision threshold is related to a first path prior probability and a second path prior probability, the first path prior probability being related to the first parameter and / or the second parameter.

[0207] Optionally, the second path prior probability is negatively related to the first path prior probability.

[0208] Optionally, the first parameter is related to a plurality of building densities in a target time window, and the second parameter is related to a plurality of heights in the target time window.

[0209] ​​​Optionally, the first parameter is related to a mean of the plurality of building densities within the target time window, and the second parameter is related to a mean of the plurality of heights within the target time window.

[0210] Optionally, the target time window has a length belonging to a target length interval.

[0211] Optionally, a lower limit of the target length interval is greater than or equal to a first value, and the first value is greater than or equal to a signal transmission delay of the sensing target.

[0212] Optionally, an upper limit of the target length interval is less than or equal to a second value, and the second value is related to a motion speed of the sensing target and an effective radius of a cell where the sensing target is located.

[0213] Optionally, the second value is equal to a quotient of the effective radius related and the motion speed.

[0214] Optionally, the LLR is related to an observation parameter of a second signal, and the second signal is a signal before the first signal.

[0215] Optionally, the observation parameter of the second signal includes at least one of skewness, kurtosis, peak-to-average ratio, angle-of-arrival skewness, angle-of-arrival variance, and time delay.

[0216] Optionally, in a case where an angle-of-arrival spread of the second signal is greater than or equal to a first preset threshold, the observation parameter includes the angle-of-arrival variance, and in a case where the angle-of-arrival spread of the second signal is less than a second preset threshold, the observation parameter includes the angle-of-arrival skewness, and the second preset threshold is less than or equal to the first preset threshold.

[0217] Optionally, in a case where the first path prior probability is related to the first parameter, the control module 12 is further configured to generate the first path prior probability according to a first prior probability function corresponding to the first path, the first parameter, the first prior probability function being related to a building density reference value and a building density attenuation factor.

[0218] Optionally, in a case where the first path prior probability is related to the second parameter, the control module 12 is further configured to generate the first path prior probability according to a second prior probability function corresponding to the first path, the second parameter, the second prior probability function being related to a height reference value and a height attenuation factor.

[0219] Optionally, in a case where the first path prior probability is related to the first parameter and the second parameter, the control module 12 is further configured to generate the first path prior probability according to a first prior probability function corresponding to the first path, the first parameter, a second prior probability function corresponding to the first path, the second parameter, a first weight of the first prior probability function, and a second weight of the second prior probability function.

[0220] The signal processing apparatus provided in the embodiment can execute the action of the network device in the foregoing method embodiment, and has similar implementation principles and technical effects, which will not be described here again.

[0221] Optionally, the signal processing apparatus can further include at least one storage module, which can include data and / or instructions. Other modules (for example, the receiving module, the sending module, the processing module, etc.) in the signal processing apparatus can read the data and / or instructions in the storage module to implement the corresponding method.

[0222] It should be noted that it should be understood that the sending module in each of the above embodiments can be a transmitter when actually implemented, the receiving module can be a receiver when actually implemented, or the sending module and the receiving module can be implemented through a transceiver, or the sending module and the receiving module can be implemented through a communication port. The processing module can be implemented in the form of software through a processing element, or can be implemented in the form of hardware. For example, the processing module can be at least one separately established processing element, or can be integrated in a chip of the apparatus, in addition, the processing module can also be stored in the form of program code in a memory of the apparatus, and the function of the processing module can be called and executed by a processing element of the apparatus. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element mentioned here can be an integrated circuit with signal processing capability. In the implementation process, each step of the above method or each of the above modules can be completed by integrated logic circuits of hardware or instructions in the form of software in the processing element.

[0223] For example, the above modules can be one or more integrated circuits configured to implement the above method, for example, one or more application specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs), etc. For another example, when a certain module is implemented in the form of calling program code by a processing element, the processing element can be a general-purpose processor, for example, a central processing unit (CPU) or other processor that can call program code. For another example, the modules can be integrated together to implement in the form of a system on a chip (SOC).

[0224] Figure 8 Another signal processing apparatus provided in the embodiment is shown in a structural schematic diagram. As shown in FIG. 6, the signal processing apparatus includes a receiving module 601, a sending module 602, and a processing module 603. Figure 8As shown, the signal processing apparatus 800 can include at least one processor 801, a memory 802, and a transceiver 803. The processor 801, the transceiver 803, and the memory 802 communicate with each other by an internal connection path. The memory 802 is configured to store instructions. The processor 801 is configured to execute the instructions stored in the memory 802 to control the transceiver 803 to transmit and / or receive information.

[0225] It should be understood that the signal processing apparatus can correspond to the network device in the above method embodiments. The signal processing apparatus can be configured to perform the steps and / or procedures of the network device in the above method embodiments. Optionally, the memory 802 can include a read-only memory and a random access memory, and provide instructions and data to the processor 801. A part of the memory 802 can also include a non-volatile random access memory. The memory 802 can be a separate device or integrated in the processor 801. The processor 801 can be configured to execute the instructions stored in the memory 802, and when the processor 801 executes the instructions stored in the memory, the processor 801 is configured to perform the steps and / or procedures of the above method embodiments.

[0226] The transceiver 803 can include a transmitter and a receiver. The transceiver 803 can further include an antenna, and the number of antennas can be one or more. The processor 801 and the memory 802 and the transceiver 803 can be devices integrated on different chips. For example, the processor 801 and the memory 802 can be integrated in a baseband chip, and the transceiver 803 can be integrated in a radio frequency chip. The processor 801 and the memory 802 and the transceiver 803 can also be devices integrated on the same chip. The present application does not make any limitation in this regard.

[0227] Optionally, the signal processing apparatus is a component, such as a chip, a chip system, etc., configured in a network device.

[0228] The transceiver 803 can also be a communication interface, such as an input interface and / or an output interface, a circuit, etc. The transceiver 803, the processor 801, and the memory 802 can be integrated in the same chip, such as a baseband chip.

[0229] In the implementation process, the steps of the above method can be completed by the integrated logic circuit of hardware in the processor or the instruction in the form of software. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as hardware processor execution completion, or executed by the combination of hardware and software modules in the processor. The software module can be located in the mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, register, etc. The storage medium is located in the memory, and the processor reads the information in the memory, and combines the hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0230] It should be noted that the processor in the embodiments of the present application can be an integrated circuit chip with signal processing capability. In the implementation process, the steps of the above method embodiments can be completed by the integrated logic circuit of hardware in the processor or the instruction in the form of software. The above processor can be a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The disclosed methods, steps and logic block diagrams in the embodiments of the present application can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as hardware decoding processor execution completion, or executed by the combination of hardware and software modules in the decoding processor. The software module can be located in the mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, register, etc. The storage medium is located in the memory, and the processor reads the information in the memory, and combines the hardware to complete the steps of the above method.

[0231] It is to be appreciated that the memory in the embodiments of the application can be a volatile memory or a nonvolatile memory, or can include both volatile and nonvolatile memory. Where the nonvolatile memory is a read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically EPROM (EEPROM), or flash memory. The volatile memory can be a random access memory (RAM), which is used as the external cache. By way of example, and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous dynamic RAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DR RAM). It is to be appreciated that the memory described herein is intended to include, among other things, these and any other suitable types of memory.

[0232] The application also provides a chip system, comprising at least one processor and a communication interface, the communication interface and the at least one processor are interconnected through a line, the at least one processor is used to run a computer program or instruction, so as to realize the method in the above-mentioned embodiments.

[0233] The application also provides a computer readable storage medium, which can include: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes, specifically, the computer readable storage medium stores program instructions, and the method in the above-mentioned embodiments is realized when the program instructions are executed.

[0234] The application also provides a computer program product, which includes execution instructions stored in a readable storage medium. At least one processor of a terminal or network equipment can read the execution instructions from the readable storage medium, and the at least one processor executes the execution instructions to make the terminal or network equipment implement the signal processing method provided by the various embodiments.

[0235] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions recorded in the above embodiments can still be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A signal processing method, characterized in that, The method includes: Receive first information sent by the sensing target, the first information being used to indicate a decision threshold, the decision threshold being related to the building density and / or height of the location of the sensing target; If the log-likelihood ratio (LLR) of the first path and the second path is greater than or equal to the decision threshold, the first path is used to send the first signal. If the LLR is less than the decision threshold, the first signal is transmitted using the second path, where the positioning error of the second path is greater than that of the first path.

2. The method according to claim 1, characterized in that, The first information includes a first parameter and / or a second parameter, wherein the first parameter is used to determine the building density at the location of the sensing target, and the second parameter is used to determine the height at the location of the sensing target.

3. The method according to claim 2, characterized in that, The first parameter includes the location of the perceived target.

4. The method according to claim 2, characterized in that, The decision threshold is related to the prior probability of the first path and the prior probability of the second path. The prior probability of the first path is related to the first parameter and / or the second parameter.

5. The method according to claim 4, characterized in that, The prior probability of the second path is negatively correlated with the prior probability of the first path.

6. The method according to claim 5, characterized in that, The first parameter is related to the density of multiple buildings within the target time window, and the second parameter is related to the height of multiple buildings within the target time window.

7. The method according to claim 6, characterized in that, The first parameter is related to the average density of multiple buildings within the target time window, and the second parameter is related to the average height of multiple buildings within the target time window.

8. The method according to claim 7, characterized in that, The length of the target time window belongs to the target length range.

9. The method according to claim 8, characterized in that, The lower limit of the target length interval is greater than or equal to a first value, and the first value is greater than or equal to the signal transmission delay of the perceived target.

10. The method according to claim 9, characterized in that, The upper limit of the target length range is less than or equal to the second value, which is related to the movement speed of the sensing target and the effective radius of the cell where the sensing target is located.

11. The method according to claim 10, characterized in that, The second value is equal to the quotient of the effective radius and the speed of motion.

12. The method according to any one of claims 1-11, characterized in that, The LLR is related to the observation parameters of the second signal, which is the signal preceding the first signal.

13. The method according to claim 12, characterized in that, The observation parameters of the second signal include at least one of the following: skewness, kurtosis, peak-to-average power ratio, angle of arrival skewness, angle of arrival variance, and time delay.

14. The method according to claim 13, characterized in that, When the angle of arrival spread of the second signal is greater than or equal to a first preset threshold, the observation parameter includes the angle of arrival variance; When the angle of arrival spread of the second signal is less than a second preset threshold, the observation parameters include the angle of arrival skewness, and the second preset threshold is less than or equal to the first preset threshold.

15. The method according to claim 5, characterized in that, The prior probability of the first path is related to the first parameter, and the method further includes: Based on the first prior probability function corresponding to the first path and the first parameter, the prior probability of the first path is generated. The first prior probability function is related to the building density reference value and the building density attenuation factor.

16. The method according to claim 5, characterized in that, The first path prior probability is related to the second parameter, and the method further includes: Based on the second prior probability function corresponding to the first path and the second parameter, the prior probability of the first path is generated. The second prior probability function is related to the altitude reference value and the altitude attenuation factor.

17. The method according to claim 5, characterized in that, The prior probability of the first path is related to the first parameter and the second parameter, and the method further includes: The prior probability of the first path is generated based on the first prior probability function corresponding to the first path, the first parameter, the second prior probability function, the second parameter, the first weight of the first prior probability function, and the second weight of the second prior probability function.

18. A signal processing apparatus, characterized in that, include: Processor and memory; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to perform the method as described in any one of claims 1-17.

19. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-17.

20. A chip system, characterized in that, It includes at least one processor and a communication interface, the communication interface and the at least one processor being interconnected via a line, the at least one processor being configured to run a computer program or instructions to perform the method as described in any one of claims 1-17.

21. A computer program product, characterized in that, Includes a computer program that, when run, causes a computer to perform the method as described in any one of claims 1-17.

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