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

Abstract

The present disclosure provides a signal processing method, device, storage medium and program product, relates to the technical field of signal processing, and can solve the problem that related technologies cannot accurately and efficiently collect and process signals. The method comprises the following steps: measuring a to-be-measured signal to obtain a first signal value and a second signal value, the first signal value and the second signal value being signal values of different sampling points of the to-be-measured signal in a time domain; obtaining a target signal value according to a time interval of corresponding sampling points of the first signal value and the second signal value, the first signal value and the second signal value; and obtaining a signal processing result based on the target signal value. The present disclosure can accurately and efficiently collect and process signals.

Description

Technical Field

[0001] This disclosure relates to the field of signal processing technology, and in particular to a signal processing method, apparatus, storage medium, and program product. Background Technology

[0002] In fields such as optics, acoustics, communications, and wave dynamics, the acquisition and processing of real signals are crucial. Real signals exhibit spectral symmetry, meaning they consist of two parts: a positive frequency and a negative frequency, and these two parts are conjugate symmetrical. Therefore, in practical applications, only a portion of the real signal can be utilized. Furthermore, the acquisition of real signals typically only allows for the acquisition of amplitude information, not phase information, leading to the loss of some information during the acquisition process.

[0003] How to accurately and efficiently acquire and process signals is a problem that urgently needs to be solved. Summary of the Invention

[0004] This disclosure provides a signal processing method, apparatus, storage medium, and program product that can solve the problem that related technologies struggle to accurately and efficiently acquire and process signals.

[0005] On the one hand, a signal processing method is provided, comprising: measuring a signal to be measured to obtain a first signal value and a second signal value, wherein the first signal value and the second signal value are signal values ​​of the signal to be measured at different sampling points in the time domain; obtaining a target signal value based on the time interval between the sampling points corresponding to the first signal value and the second signal value, the first signal value, and the second signal value; and obtaining a signal processing result based on the target signal value.

[0006] In another aspect, a signal processing device is provided, comprising: a signal acquisition module and a signal processing module; the signal acquisition module is used to acquire a signal to be measured; the signal acquisition module includes at least one sensor; the signal acquisition module is used to measure the signal to be measured to obtain a first signal value and a second signal value, the first signal value and the second signal value being signal values ​​of the signal to be measured at different sampling points in the time domain; the signal processing module is used to obtain a target signal value based on the time interval between the sampling points corresponding to the first signal value and the second signal value, the first signal value, and the second signal value; the signal processing module is used to obtain a signal processing result based on the target signal value.

[0007] In another aspect, a signal processing apparatus is provided, comprising: a memory and a processor; the memory and the processor being coupled; the memory being used to store a computer program; and the processor executing the computer program to implement the method of any of the above embodiments.

[0008] In another aspect, a computer-readable storage medium is provided, on which computer program instructions are stored, which, when executed by a processor, implement the method described in any of the above embodiments.

[0009] In another aspect, a computer program product is provided, the computer program product including computer program instructions that, when executed by a processor, implement the method described in any of the above embodiments.

[0010] In this embodiment, the signal processing device can measure the signal to be measured to obtain a first signal value and a second signal value, and obtain a target signal value based on the time interval between the sampling points corresponding to the first and second signal values, the first signal value, and the second signal value. Compared with the problem in related technologies that can only collect real signals and thus result in the loss of signal phase information, in this embodiment, since the first and second signal values ​​are signal values ​​of the signal to be measured at different sampling points in the time domain, the signal processing device can directly estimate the phase information based on the signal values ​​at different sampling points, avoiding the loss of signal phase information. In addition, the above-mentioned technical solution of this disclosure does not require complex hardware and algorithm design when collecting the signal to be measured. In summary, this disclosure can accurately and efficiently collect and process signals. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this disclosure, the accompanying drawings used in some embodiments of this disclosure will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings.

[0012] Figure 1 This is a structural diagram of a signal processing apparatus provided in some embodiments of the present disclosure;

[0013] Figure 2 This is a structural diagram of yet another signal processing apparatus provided in some embodiments of the present disclosure;

[0014] Figure 3 This is a structural diagram of yet another signal processing apparatus provided in some embodiments of the present disclosure;

[0015] Figure 4 A flowchart illustrating a signal processing method provided in some embodiments of this disclosure;

[0016] Figure 5 A scenario diagram illustrating a signal measurement method provided in some embodiments of this disclosure;

[0017] Figure 6 A flowchart illustrating yet another signal processing method provided in some embodiments of this disclosure;

[0018] Figure 7 A flowchart illustrating yet another signal processing method provided in some embodiments of this disclosure;

[0019] Figure 8 A scenario diagram illustrating yet another signal measurement method provided in some embodiments of this disclosure;

[0020] Figure 9 A flowchart illustrating yet another signal processing method provided in some embodiments of this disclosure;

[0021] Figure 10 This is a structural diagram of a signal acquisition module provided in some embodiments of the present disclosure;

[0022] Figure 11 A structural diagram of a CCD array provided in some embodiments of this disclosure;

[0023] Figure 12 A matrix modulus distribution diagram provided for some embodiments of this disclosure;

[0024] Figure 13 Another matrix modulus distribution diagram provided for some embodiments of this disclosure;

[0025] Figure 14 Another matrix modulus distribution diagram provided for some embodiments of this disclosure;

[0026] Figure 15 Another matrix modulus distribution diagram provided for some embodiments of this disclosure;

[0027] Figure 16 Another matrix modulus distribution diagram provided for some embodiments of this disclosure;

[0028] Figure 17 Another matrix modulus distribution diagram provided for some embodiments of this disclosure;

[0029] Figure 18 This is a structural diagram of yet another signal processing apparatus provided in some embodiments of the present disclosure;

[0030] Figure 19 This is a structural diagram of another signal processing apparatus provided in some embodiments of the present disclosure. Detailed Implementation

[0031] The technical solutions of this disclosure will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0032] It should be noted that, in this disclosure, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this disclosure should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0033] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0034] In the description of this disclosure, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in this document is merely a description of 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, and B alone. Furthermore, "at least one" means one or more, and "more than one" means two or more.

[0035] In fields such as optics, acoustics, communications, and wave dynamics, the acquisition and processing of real signals are crucial. Real signals are signals directly measured in actual physical systems and are typically represented by real-number functions in the time or spatial domains. Despite their widespread use in engineering applications, the measurement and processing of real signals still face numerous challenges.

[0036] Taking a periodic real signal as an example, a periodic real signal can be represented by a cosine function, as shown in the following formula 1:

[0037] s(t)=a*cos(2πft) Formula 1

[0038] Where s(t) is a periodic real signal, a is the amplitude, and f is the frequency, Equation 1 can be transformed into Equation 2 using Euler's formula:

[0039]

[0040] Where s(t) is a periodic real signal, a is the amplitude, and f is the frequency. As shown in Equation 2 above, the spectrum of a real signal exhibits symmetry; that is, its Fourier transform result contains both positive and negative frequencies, and these two parts are conjugate symmetric. This means that only half of the frequency components in the spectrum of a real signal are independent; the other half can be derived through conjugate symmetry. Therefore, only half of the information in the spectrum of a real signal is actually useful; the other half is redundant. This characteristic imposes significant limitations on signal processing.

[0041] Taking direction of arrival (DOA) estimation based on real signals as an example, since the spectrum of a real signal contains positive and negative frequency components, the algorithm will estimate two directions of arrival at the same time, and cannot distinguish which direction is the real direction of arrival, thus causing ambiguity in direction estimation and increasing the complexity of signal processing.

[0042] Taking optical imaging as an example, the media used for recording the light field (such as holograms, charge-coupled devices (CCDs), or complementary metal-oxide-semiconductor (CMOS) media) generally record the amplitude information of the light field, but cannot directly record the phase information. However, the complete information of the light field includes both amplitude and phase, and the phase information is crucial for image quality, resolution, and 3D reconstruction. Due to the lack of phase information, it is difficult to fully recover the characteristics of the light field based solely on amplitude information.

[0043] For example, in optical holographic imaging, the holographic plate records the interference pattern of the light field, i.e., the distribution of light intensity. When this light intensity information is used for imaging, both real and virtual images are generated. The presence of virtual images can interfere with observation, so a special reference light design is needed to move the virtual image outside the observation area. This process increases the complexity of the optical system and still cannot completely solve the problem caused by the lack of phase information.

[0044] For example, in 3D imaging and reconstruction, acquiring phase information is particularly important. Due to the phase ambiguity of real signals, monocular cameras cannot directly acquire depth information, thus requiring a binocular camera system. Binocular cameras simulate the parallax principle of the human eye, using two cameras to capture the same scene from different angles, and calculating depth information by matching corresponding points in the two images. While effective, this method significantly increases hardware costs and system complexity. For instance, in autonomous driving and robot navigation, binocular camera systems require precise calibration and synchronization to ensure the accuracy of depth information. Furthermore, the high computational complexity of binocular cameras necessitates powerful real-time processing capabilities, further increasing system cost and power consumption.

[0045] In wireless communication, in-phase and quadrature (IQ) modulation is typically used to transmit complex signals (containing both amplitude and phase information). IQ modulation modulates the real and imaginary parts of the complex signal onto two orthogonal carriers, generating a single real signal for transmission. IQ modulation requires precise synchronization and complex hardware design to ensure the receiver can correctly demodulate the real and imaginary parts of the complex signal. However, this approach increases signal processing complexity and sacrifices some channel capacity.

[0046] In underwater acoustic communication and positioning, sensors typically can only record the amplitude information of sound waves, but cannot directly record phase information. This is similar to the problem in optical imaging; the limitation of amplitude information makes signal processing and precise positioning of underwater targets difficult.

[0047] In seismic source location, focal mechanism estimation, deep structure analysis, and oil exploration based on seismic wave observation data, it is also necessary to monitor seismic wave signals through seismic stations or detector arrays. However, this approach can only observe actual amplitude information and lacks phase information. Therefore, it is necessary to design complex inversion algorithms and multi-station data fusion technology to estimate the source location or underground structure information. This approach also suffers from high system cost and low efficiency.

[0048] In summary, the acquisition and processing of real signals has significant application value in many fields, but its spectral symmetry and lack of phase information present considerable challenges. In areas such as direction of arrival estimation, optical imaging, wireless communication, and underwater acoustic communication, these challenges directly impact system performance and complexity. How to accurately and efficiently acquire and process signals is a problem that urgently needs to be solved.

[0049] Therefore, in this embodiment of the present disclosure, the signal processing device can measure the signal to be measured to obtain a first signal value and a second signal value, and obtain a target signal value based on the time interval between the sampling points corresponding to the first signal value and the second signal value, the first signal value, and the second signal value. Compared with the problem in related technologies that can only collect real signals and thus result in the loss of signal phase information, in this embodiment of the present disclosure, since the first signal value and the second signal value are signal values ​​of the signal to be measured at different sampling points in the time domain, the signal processing device can directly estimate the phase information based on the signal values ​​at different sampling points, avoiding the loss of signal phase information. In addition, the above-mentioned technical solution of the present disclosure does not require complex hardware and algorithm design when collecting the signal to be measured. In summary, the present disclosure can accurately and efficiently collect and process signals.

[0050] The technical solutions of the embodiments disclosed herein can be used in various systems involving signal acquisition and processing, such as optical systems, acoustic systems, communication systems, wave dynamics systems, etc. Taking communication systems as an example, these systems can be 3GPP (Third Generation Partnership Project) communication systems, such as 4G (4th generation) systems like Long Term Evolution (LTE), 5G systems like New Radio (NR), LTE and 5G hybrid networking systems, integrated communication and sensing systems, non-terrestrial networks (NTN), device-to-device (D2D) communication systems, vehicle-to-everything (V2X) communication systems, machine-type communication (MTC) systems, Internet of Things (IoT) systems, satellite communication systems, short-range systems, Global System for Mobile Communications (GSM), Enhanced Data Rate for GSM Evolution (EDGE), Wideband Code Division Multiple Access (WCDMA), and Code Division Multiple Access 2000 systems. The system can be CDMA2000, Time Division-Synchronization Code Division Multiple Access (TD-SCDMA), or other future communication systems. This communication system can also be a non-3GPP communication system; there are no restrictions.

[0051] The systems described above that are applicable to this disclosure are only examples of communication systems. Systems applicable to this disclosure are not limited to communication systems. The systems provided in this disclosure do not impose any limitations on the solutions disclosed herein. This is hereby stated uniformly and will not be repeated below.

[0052] For example, such as Figure 1As shown, a signal processing device 10 provided in an embodiment of this disclosure includes a signal acquisition module 101 and a signal processing module 102. The signal acquisition module 101 and the signal processing module 102 are connected to each other, and there can be one or more signal acquisition modules 101 and signal processing modules 102, without limitation on the number.

[0053] The signal acquisition module 101 and the signal processing module 102 can be deployed on the same physical device, meaning the signal processing device 10 is an independent device entity. Alternatively, the signal acquisition module 101 and the signal processing module 102 can be deployed on different physical devices, meaning the signal processing device 10 is a distributed device system. This disclosure does not limit the scope of the application.

[0054] The signal acquisition module 101 is used to acquire the signal to be measured, and the signal processing device 10 can execute the signal processing method provided in the embodiments of this disclosure through the signal acquisition module 101 and the signal processing module 102.

[0055] In one example, such as Figure 2 As shown, Figure 1 An exemplary implementation of the device shown. The signal acquisition module 101 includes at least one sensor 1011 ( Figure 2 (Only one example is shown in the text).

[0056] Sensor 1011 is used to acquire the signal to be measured. For example, the signal to be measured may include electromagnetic wave signals (e.g., electromagnetic signals, optical signals) or mechanical wave signals (e.g., acoustic signals). However, in real-world scenarios, signals are usually categorized according to the acquisition method, rather than according to their physical properties. For instance, signals can be classified as electromagnetic signals, optical signals, acoustic signals, and mechanical wave signals based on different acquisition methods. For electromagnetic signals, sensor 1011 may include antennas, Hall effect sensors, magneto-resistive sensors, photoelectric detectors, Rydberg atom microwave sensors, nitrogen-cacancy centers (NV centers) microwave sensors, etc. For optical signals, sensor 1011 can be various types of photosensitive sensors, including photodiodes, photomultiplier tubes (PMTs), CCDs, CMOS image sensors, thermopile detectors, photoconductive detectors, avalanche photodiodes (APDs), phototransistors, pyroelectric detectors, and quantum dot detectors. For acoustic signals, sensor 1011 can include capacitive sensors, piezoelectric sensors, and fiber optic sensors. For mechanical wave signals, sensor 1011 can include stress gauges, accelerometers, and fiber optic sensors.

[0057] In some embodiments, the signal acquisition module 101 further includes a conversion unit 1012. The sensor 1011 and the conversion unit 1012 are connected together, and the conversion unit 1012 is connected to the signal processing module 102. The conversion unit 1012 is used to convert the signal under test from an analog signal to a digital signal. For example, the conversion unit 1012 can be an analog-to-digital converter (ADC).

[0058] In some embodiments, the signal acquisition module 101 further includes a switching circuit 1013. The switching circuit 1013 is used to connect or disconnect the measurement circuit at preset time intervals, and / or to switch the connection between different sensors 1011 and the signal processing module 102 in the signal acquisition module 101. The sensors 1011 and the conversion unit 1012 can be connected via the switching circuit 1013, which controls the circuit connection state between the sensors 1011 and the conversion unit 1012.

[0059] For example, at time t1, the switching circuit 1013 connects the sensor 1011 to the conversion unit 1012, obtains the first signal value a1, and then disconnects. At time t2, the switching circuit 1013 connects the sensor to the conversion unit 1012 again, obtaining the second signal value a2. The signal processing module 102 obtains the target signal value based on the first signal value a1, the second signal value a2, and the time interval. Then, the signal processing module 102 can obtain the signal processing result based on the target signal value.

[0060] In another example, such as Figure 3 As shown, Figure 1 An exemplary implementation of the device is shown. The signal acquisition module 101 includes a sensor 1011 and two conversion units 1012 (for ease of description, one conversion unit 1012 is referred to as ADC1, and the other as ADC2). The sensor 1011 is connected to both conversion units 1012, and the two conversion units 1012 are connected to the signal processing module 102.

[0061] In some embodiments, the signal acquisition module 101 further includes a switching circuit 1013, a power divider 1014, and a delay circuit 1015. The delay circuit 1015 is used to adjust the transmission path length between the sensor 1011 and the signal processing module 102.

[0062] The sensor 1011 and the two conversion units 1012 are connected via a switching circuit 1013 and a power divider 1014. The switching circuit 1013 controls the circuit connection between the sensor 1011 and the conversion units 1012, and the power divider 1014 divides the signal acquired by the sensor 1011 into two identical signals, which are then transmitted to the two conversion units 1012 respectively. A delay circuit 1015 is deployed between one of the conversion units 1012 (e.g., ADC2) and the power divider 1014.

[0063] For example, the length of the delay circuit 1015 is L. ADC1 and ADC2 respectively measure the signal transmitted from sensor 1011 to obtain a first signal value a1 and a second signal value a2. The two signals are respectively transmitted to signal processing module 102, which combines the two signal values ​​into a target signal. Because the delay circuit 1015 increases the transmission path of the signal transmitted to ADC2, there is a certain time difference between the signals on ADC1 and ADC2.

[0064] In some examples, the time difference can be defined as the time interval between the sampling points corresponding to the first signal value and the second signal value. Thus, in this embodiment of the present disclosure, the signal processing device 10 can measure the first signal value a1 and the second signal value a2 at the same physical moment, and the first signal value a1 and the second signal value a2 can still be the signal values ​​of the signal under test at different sampling points in the time domain. This can further simplify the measurement logic of the signal processing device 10 and improve the signal processing efficiency.

[0065] In some examples, this time difference corresponds to the length of the delay circuit, i.e., time difference Δt = L / v, where v is the speed at which the signal travels in the delay circuit. For example, signal processing module 102 can obtain the target signal value based on the first signal value a1, the second signal value a2, and the time difference Δt. Then, signal processing module 102 can obtain the signal processing result based on the target signal value.

[0066] In some examples, Figure 3 The switching circuit 1013 shown can be integrated into the conversion unit 1012. For example, the conversion unit includes an ADC circuit, which can realize the time-domain switching function by adjusting the sampling rate of the ADC circuit, and determine the sampling time difference between the first signal value and the second signal value according to the sampling rate.

[0067] It should be understood that the signal processing device 10 in this embodiment can also add, reduce, or adjust its internal module units according to actual application scenarios. For example, the signal acquisition module 101 may also include at least one of the following: a mixer, a filter, an amplifier, and a memory, thereby realizing functions such as signal filtering, amplification, and frequency conversion. In addition, the signal processing device 10 in this embodiment can also be arranged in an array, and each array may include at least two signal processing devices 10 for acquiring and processing spatial signals to realize functions such as channel estimation, imaging, and positioning.

[0068] In some embodiments, the signal acquisition module 101 can perform frequency mixing and filtering on the signal to be measured by a mixer and a filter before measurement, thereby achieving down-conversion, and then measure the down-converted signal.

[0069] It should be noted that the various embodiments of this disclosure can be referenced or learned from each other. For example, the same or similar steps, method embodiments, system embodiments and device embodiments can be referenced from each other without limitation.

[0070] The following is combined with Figure 1 The signal processing apparatus 10 shown describes the signal processing method provided in the embodiments of this disclosure.

[0071] Figure 4 This is a flowchart illustrating a signal processing method provided in an embodiment of this disclosure. Figure 4 As shown, the method includes the following steps:

[0072] Step 401: Measure the signal to be tested to obtain the first signal value and the second signal value.

[0073] The first signal value and the second signal value are the signal values ​​of the signal under test at different sampling points in the time domain.

[0074] In some embodiments, the signal to be measured may include electromagnetic wave signals (e.g., electromagnetic signals, optical signals) or mechanical wave signals (e.g., acoustic signals). However, in practical scenarios, signals are usually classified according to the acquisition method rather than the physical properties of the signal. For example, signals can be classified into electromagnetic signals, optical signals, acoustic signals, and mechanical wave signals according to different acquisition methods. Signal processing devices can acquire signals using corresponding types of sensors. Related information can be found in the above description and will not be repeated here.

[0075] It should be noted that, in the embodiments of this disclosure, the first signal value and the second signal value correspond to different sampling points in the time-domain representation of the signal under test, and this disclosure does not limit the actual measurement time of the first signal value and the second signal value. That is to say, in the embodiments of this disclosure, the signal processing device can obtain the above-mentioned first signal value and the second signal value by measuring at different measurement times, and the signal processing device can also obtain the above-mentioned first signal value and the second signal value by measuring at the same measurement time.

[0076] In one example, such as Figure 5 As shown, the signal processing device can acquire the first signal value s1 at the first time t1 and the second signal value s2 at the second time t2. The first time and the second time are separated by Δt = t2 - t1.

[0077] In another example, embodiments of this disclosure can be achieved through, as shown in... Figure 3The device structure shown obtains the first and second signal values ​​at the same measurement time. Since a delay circuit is provided on one of the circuits, the first and second signal values ​​obtained by the signal processing device at the same measurement time still correspond to different sampling points in the time-domain representation of the signal under test. The two implementation methods described above are essentially equivalent; that is, the time interval mentioned in the embodiments of this disclosure is the time difference between the time-domain sampling points corresponding to the two signal values. For ease of description, the following scheme will be introduced in the manner shown in the first example.

[0078] In some embodiments, the signal processing apparatus includes at least one sensor for acquiring the signal value of the signal to be measured.

[0079] In some embodiments, the signal processing apparatus further includes a switching unit, which is used to acquire signal values ​​collected by different sensors at different times.

[0080] Step 402: Obtain the target signal value based on the time interval between the sampling points corresponding to the first signal value and the second signal value, the first signal value, and the second signal value.

[0081] In one possible implementation, the signal processing device determines a merging coefficient based on a time interval, and performs linear merging of the first signal value and the second signal value based on the merging coefficient to obtain the target signal value.

[0082] In some embodiments, the merging coefficient satisfies the following formula 3:

[0083] c = e j(βΔt+γ) Formula 3

[0084] Where c is the merging coefficient, Δt is the time interval, β is the first preset value, and γ is the second preset value. j is the imaginary unit. Therefore, the merging coefficient c used to merge the first signal value and the second signal value can be determined by the time interval Δt between the corresponding sampling points of the first signal value and the second signal value. Different time intervals Δt determine different merging coefficients c.

[0085] In some embodiments, the target signal value is obtained according to the following formula 4:

[0086] s t =s1+c*s2 Formula 4

[0087] Among them, s t s1 is the target signal value, s2 is the first signal value, s2 is the second signal value, and c is the merging coefficient.

[0088] It should be noted that in the embodiments of this disclosure, the terms "first" and "second" in "first signal value" and "second signal value" are used to distinguish the two measured signal values ​​and do not restrict the logical or temporal order. For example, the first signal value and the second signal value mentioned in the above example can be measured at the same time. Alternatively, in the time-domain representation of the signal under test, the sampling point corresponding to the first signal value can be located before the sampling point corresponding to the second signal value. Alternatively, in the time-domain representation of the signal under test, the sampling point corresponding to the first signal value can be located after the sampling point corresponding to the second signal value.

[0089] Step 403: Based on the target signal value, obtain the signal processing result.

[0090] In some embodiments, the signal processing result includes at least one of the following:

[0091] Target signal value;

[0092] Argument information of the target signal value;

[0093] The conjugate value of the target signal;

[0094] Argument information of the conjugate value of the target signal value;

[0095] The magnitude of the target signal value.

[0096] The signal processing device can output corresponding signal processing results according to actual needs. It can directly output the target signal value as the signal processing result, or it can process the target signal value and output the processed signal processing result. For example, in a multi-sensor array application, the target signal values ​​obtained by each sensor can be combined into a matrix for further processing. In this case, for a single sensor, the signal processing device only needs to output the target signal value itself to the next processing node.

[0097] Taking the signal processing result as the target signal value as an example, the signal processing device can process the target signal value s t As a result of signal processing; for example, when the first signal value s1 = 0.5, the second signal value s2 = 0.8, and the combining coefficient c = -j, s can be determined. t =0.5-0.8j.

[0098] Taking the argument information of the target signal value as an example, the signal processing device can calculate the target signal value s. t The argument θ is determined and used as the signal processing result; for example, when the first signal value s1 = 0.5, the second signal value s2 = 0.8, and the combining coefficient c = -j, s can be determined. t =0.5-0.8j, then θ=arg(s) t )≈58°.

[0099] Taking the signal processing result as the conjugate value of the target signal value as an example, the signal processing device can calculate the target signal value s. t The conjugate value s t *and s t *As a result of signal processing; for example, when the first signal value s1 = 0.5, the second signal value s2 = 0.8, and the combining coefficient c = -j, s can be determined. t =0.5-0.8j, then s t * = 0.5 + 0.8j.

[0100] Taking the argument information of the conjugate value of the target signal value as an example, the signal processing device can calculate the target signal value s. t The conjugate value s t *and s t The corresponding argument is used as the signal processing result; for example, when the first signal value s1 = 0.5, the second signal value s2 = 0.8, and the combining coefficient c = -j, s can be determined. t =0.5-0.8j, then arg(s) t *)≈-58°.

[0101] Taking the magnitude of the target signal value as an example, the signal processing device can calculate the target signal value s. t modulus |s t |and|s t | As a result of signal processing; for example, when the first signal value s1 = 0.5, the second signal value s2 = 0.8, and the combining coefficient c = -j, s can be determined. t =0.5-0.8j, then |s t |=0.5+0.8j≈0.94.

[0102] Based on the above technical solution, in this embodiment of the present disclosure, the signal processing device can measure the signal to be measured to obtain a first signal value and a second signal value, and obtain a target signal value based on the time interval between the sampling points corresponding to the first signal value and the second signal value, the first signal value, and the second signal value. Compared with the problem in related technologies that can only collect real signals and thus result in the loss of signal phase information, in this embodiment of the present disclosure, since the first signal value and the second signal value are signal values ​​of the signal to be measured at different sampling points in the time domain, the signal processing device can directly estimate the phase information based on the signal values ​​at different sampling points, avoiding the loss of signal phase information. In addition, the above technical solution of the present disclosure does not require complex hardware and algorithm design when collecting the signal to be measured. In summary, the present disclosure can accurately and efficiently collect and process signals.

[0103] Furthermore, in this embodiment of the disclosure, the merging coefficient can also be determined by the time interval and the period of the signal under test.

[0104] In some embodiments, the merging coefficient is determined by a time interval, a first preset value, and a second preset value, wherein the first preset value is determined by the period of the signal under test.

[0105] In some examples, the first preset value The second preset value γ = π, where T can be the period of the signal under test. Combining this with formula 3 above, we can obtain the merging coefficient c = e j(2πΔt / T+π) Correspondingly, the target signal value s t It can be obtained from the following formula 5:

[0106] s t =s1+e j(2πΔt / T+π) s2 Formula 5 In other examples, the first preset value The second preset value γ = -π, where T can be the period of the signal under test. Combining this with formula 3 above, we can obtain the combining coefficient c = e j(2πΔt / T+π) Correspondingly, the target signal value s t It can be obtained from the following formula 6:

[0107] s t =s1+e j(2πΔt / T-π) s2 Formula 6 In some embodiments, the range of the time interval is determined by the period of the signal under test. For example, the time interval can be a preset value or a preset value plus any integer multiple of the period of the signal under test. The preset value can be set according to actual conditions, and this disclosure does not limit it. Since signals are generally periodic, measurement results at intervals of integer multiples of the period are the same. Therefore, by superimposing an integer multiple of the period of the signal under test onto the preset value, the data acquisition time interval can be increased, the sampling rate requirement can be reduced, and thus the hardware cost can be reduced.

[0108] In some examples, the range of values ​​for this time interval can satisfy the following formula 7:

[0109] Δt∈(nT,(n+1)T) Formula 7 Where Δt is the time interval, T is the period of the signal under test, and n is an integer greater than or equal to 0.

[0110] In other examples, the range of values ​​for the time interval satisfies the following formula 8:

[0111]

[0112] Where Δt is the time interval, T is the period of the signal under test, and n is an integer greater than or equal to 0. Since the signal... Measurements at the periodic points are usually quite specific, therefore, they can be... Periodicity exclusion is used to avoid affecting measurement results.

[0113] Furthermore, the signal processing scheme provided in this disclosure can also be applied to scenarios involving multiple measurements. For example, it can perform multiple measurements on the same signal under test, or it can measure multiple signal components of the signal under test (in this case, the signal under test can be understood as a signal composed of multiple superimposed signals) separately. The signal processing device can perform multiple measurements using the same sensor, or it can perform single or multiple measurements using different sensors.

[0114] As one embodiment provided in this disclosure, combined with Figure 4 The illustrated embodiments, such as Figure 6 As shown, step 401 above can be achieved through the following step 601.

[0115] Step 601: Measure the signal to be tested to obtain multiple first signal values ​​and multiple second signal values.

[0116] Among them, multiple first signal values ​​correspond one-to-one with multiple second signal values.

[0117] Taking the same signal to be measured multiple times as an example, the first signal value and the second signal value can be collected multiple times.

[0118] In some embodiments, different first signal values ​​correspond to sampling points that differ from the period of the signal under test in the time domain by an integer multiple; different second signal values ​​correspond to sampling points that differ from the period of the signal under test in the time domain by an integer multiple.

[0119] In other words, the signal processing device can acquire a first signal value through multiple first moments and a second signal value through multiple second moments. The multiple first moments are separated by an integer number of periods of the signal to be measured, and the multiple second moments are also separated by an integer number of periods of the signal to be measured.

[0120] It should be noted that the first and second moments mentioned above refer to the time points corresponding to the sampling points in the time-domain representation of the signal under test measured by the signal processing device, rather than the actual measurement time of the signal processing device. The actual measurement time of the signal processing device can be changed by adjusting the internal circuit structure design of the signal processing device. The sampling points in the time-domain representation of the signal under test are determined by the physical properties of the signal under test (such as frequency, amplitude, phase, and other physical attributes).

[0121] For example, the signal processing device can measure N first signal values, denoted as s. 1,1 s 1,2 , ..., s 1,NThe signal processing device can measure N second signal values, denoted as s. 2,1 s 2,2 , ..., s 2,N Wherein, for any k = {1, 2, ..., N}, s 1,k and s 2,k The time interval between the corresponding sampling points is Δt.

[0122] For any l = {1, 2, ..., N-1}, s 1,l and s 1,l+1 The time interval is any integer multiple of the period T of the signal under test (the time interval between two adjacent first signal values ​​can be the same or different). 2,l and s 2,l+1 The time interval is any integer multiple of the period T of the signal under test (the time interval between two adjacent second signal values ​​can be the same or different).

[0123] In one possible implementation, in a scenario where multiple measurements are performed on the same signal to be measured, step 402 can be implemented in the following way: the signal processing device obtains a third signal value based on multiple first signal values ​​and a fourth signal value based on multiple second signal values, and the signal processing device obtains the target signal value based on the time interval between the sampling points corresponding to the first and second signal values, the third signal value, and the fourth signal value.

[0124] In some embodiments, the third signal value is obtained by summing multiple first signal values, and the fourth signal value is obtained by summing multiple second signal values. The target signal value is obtained by linearly combining the third and fourth signal values, and the combining coefficient of the linear combination is determined by the time interval.

[0125] Based on the above example, the signal processing device can sum N first signal values ​​to obtain s3, sum N second signal values ​​to obtain s4, and then, according to s... t =s3+c*s4 to obtain the target signal value, where the value of the combining coefficient c is determined by the time interval Δt.

[0126] Based on the above technical solution, embodiments of this disclosure can obtain multiple first signal values ​​and multiple second signal values ​​through multiple measurements, thereby determining the target signal value. Since signal noise interference usually follows a normal distribution, noise interference can be suppressed by superimposing multiple measurements, thereby improving the signal-to-noise ratio of the acquired signal.

[0127] As one embodiment provided in this disclosure, combined with Figure 6 The illustrated embodiments, such as Figure 7As shown, the signal processing device can perform multiple measurements at multiple time intervals. Step 601 can be implemented by step 701, and step 402 can be implemented by step 702.

[0128] Step 701: Determine multiple time intervals, and measure the signal to be tested according to the multiple time intervals to obtain multiple first signal values ​​and multiple second signal values.

[0129] In some embodiments, at least two of the plurality of first signal values ​​are acquired at the same first time, and / or at least two of the plurality of second signal values ​​are acquired at the same second time.

[0130] Among the multiple first signal values, at least two first signal values ​​are acquired at the same first time moment, which can also be expressed as at least two first signal values ​​having the same time-domain sampling point. Similarly, among the multiple second signal values, at least two second signal values ​​are acquired at the same second time moment, which can also be expressed as at least two second signal values ​​having the same time-domain sampling point.

[0131] For example, such as Figure 8 As shown, two first signal values ​​(s and s) are measured. 11 s 21 ), 2 second signal values ​​(s respectively) 12 s 22 For example, s is measured. 11 The first moment is t 11 , measuring s 21 The first moment is t 21 , measuring s 12 The second time point is t 12 , measuring s 22 The second time point is t 22 s 11 and s 12 The time interval is Δt1, s 21 and s 22 The time interval is Δt2 s. 11 The first moment t 11 and s 21 The first moment t 21 It can be at the same time, s 11 and s 21 It can be the signal value at the same moment, i.e., s 11 =s 21 Therefore, the signal processing device can obtain s through a single measurement. 11 and s 21 This reduces the number of measurements required.

[0132] In some embodiments, at least two of the multiple time intervals are identical. For example, when the bandwidth of the signal under test is small, the periods of the multiple signal components differ very little, so the same time interval can be used to reduce the number of measurements.

[0133] Step 702: Linearly combine multiple first signal values ​​and multiple second signal values ​​to obtain multiple target signal values.

[0134] The multiple merging coefficients of the linear merging are determined by multiple time intervals and multiple signal periods.

[0135] In some embodiments, the signal under test may include multiple signal components, and the above technical solution can be used to measure each signal component in the signal under test. The signal processing device can determine the time interval corresponding to each signal component, and then measure the signal under test according to the multiple time intervals, and determine the corresponding combining coefficient.

[0136] For example, an electromagnetic wave signal to be measured includes two frequency components, where the first frequency component corresponds to the signal period T1 and the second frequency component corresponds to the period T2. The signal processing device can determine two time intervals Δt1 and Δt2 respectively, and perform two measurements on the first frequency component based on the time interval Δt1 to obtain the corresponding first signal value s. 11 Second signal value s 12 Then, based on the time interval Δt2, two measurements of the second frequency component are performed to obtain the corresponding first signal value s. 21 Second signal value s 22 Then, the signal processing device can determine the combining coefficients c1 and c2 of the first frequency component based on Δt1 and Δt2, and finally obtain the target signal values ​​s corresponding to the two frequency components. 1t and s 2t .

[0137] For example, when Δt1 = T1 / 4 and Δt2 = 3T2 / 4, let the first preset value... If the second preset value γ = π, then the combining coefficient of the first frequency component is: The combining coefficient of the second frequency component is Therefore, the target signal value of the first frequency component can be obtained as s. 1t =s 11 -js 12 The target signal value of the second frequency component is s 2t =s 21 +js 22 .

[0138] For example, when Δt1 = T1 / 4 and Δt2 = 3T2 / 4, let the first preset value... If the second preset value γ = -π, then the combining coefficient of the first frequency component is: The combining coefficient of the second frequency component is Therefore, the target signal value of the first frequency component can be obtained as s. 1t =s 11 -js 12 The target signal value of the second frequency component is s 2t =s 21 +js 22 .

[0139] As can be seen from the above example, γ = π or γ = -π does not affect the final merging coefficient, and the value of the merging coefficient is determined by the time interval between the two measurements.

[0140] For example, when Δt1 = 4T1 / 9 and Δt2 = 6T2 / 7 + 2T2, let the first preset value... If the second preset value γ = -π, then the combining coefficient of the first frequency component is: The combining coefficient of the second frequency component is Therefore, the target signal value of the first frequency component can be obtained as s. 1t =s 11 -e -jπ / 9 s 12 The target signal value of the second frequency component is s 2t =s 21 +e j5π / 7 s 22 .

[0141] As can be seen from the above embodiments, different merging coefficients can be applied when two measurements are performed at different time intervals. It should be noted that when the signal under test contains multiple signal components, the two measurements of the multiple signal components can be performed at the same time interval, or the time interval between the two measurements can be determined separately based on the signal period of each signal component. Furthermore, the various technical solutions provided in this disclosure can be combined; for example, multiple measurements can be performed on each signal component to suppress noise interference.

[0142] In some embodiments, after obtaining multiple target signal values, the signal processing device can obtain a signal processing result based on the multiple target signal values.

[0143] For example, the signal processing result includes at least one of the following:

[0144] Multiple target signal values;

[0145] Argument information of multiple target signal values;

[0146] The conjugate value of multiple target signal values;

[0147] Argument information of the conjugate values ​​of multiple target signal values;

[0148] The magnitude of multiple target signal values.

[0149] For related explanations, please refer to the descriptions in the above embodiments, which will not be repeated here.

[0150] As one embodiment provided in this disclosure, combined with Figure 6 The illustrated embodiments, such as Figure 9 As shown, the signal processing device can perform multiple measurements at multiple acquisition locations. Step 601 can be implemented by step 901, and step 402 can be implemented by step 902.

[0151] Step 901: Determine multiple acquisition locations and the time interval corresponding to each acquisition location. At each acquisition location, measure the signal to be tested according to the corresponding time interval to obtain the first signal value and the second signal value corresponding to the multiple acquisition locations respectively.

[0152] In some scenarios, signal processing devices need to measure the signal under test at multiple acquisition locations. For example, in multiple-input multiple-output (MIMO) wireless communication, an array containing multiple antenna elements can be used for signal transmission and reception. As another example, in the measurement of optical signals, when a CCD is used as a sensor, the CCD is also composed of a large number of photosensitive units arranged in a regular array.

[0153] For example, the signal processing device can perform signal measurement using sensors at multiple locations. For instance, sensors at multiple locations can each acquire a first signal value s at a corresponding location at a first time t1. k,1 And at the second time t2, the second signal value s at the corresponding position is collected. k,2 , where the subscript k represents the k-th position.

[0154] In some embodiments, the time intervals corresponding to each acquisition location can be the same, in which case the sensors at multiple acquisition locations measure according to the same time interval. Alternatively, the time intervals corresponding to each acquisition location can be different, in which case the signal processing device needs to determine the time interval corresponding to each acquisition location, and the sensors at each acquisition location measure according to the corresponding time interval. Furthermore, among multiple acquisition locations, some acquisition locations may correspond to the same time interval, while others may correspond to different time intervals.

[0155] In some embodiments, multiple acquisition locations are divided into at least one location group, and one or more acquisition locations in the location group correspond to the same time interval and / or the same merging coefficient. For example, one or more acquisition locations in the location group can measure a corresponding first signal value at the same first time moment and a corresponding second signal value at the same second time moment.

[0156] For example, when using an antenna array for electromagnetic signal acquisition, the antennas on the antenna array can be divided into several antenna groups. At least two antenna groups can share the same acquisition circuit. In this case, the acquisition circuit can be connected to one of the antenna groups at different times and the signal can be measured twice by switching the switch, and then switched to connect to the next antenna group and the signal can be measured twice.

[0157] like Figure 10 As shown, the antenna array used for electromagnetic signal measurement includes four antennas. Antennas 1 and 2 are connected to analog-to-digital converter circuit 1 (ADC 1), and antennas 3 and 4 are connected to analog-to-digital converter circuit 2 (ADC 2). Antennas 1 and 3 are grouped together, and antennas 2 and 4 are grouped together. ADC 1 is first connected to antenna 1, and ADC 2 is first connected to antenna 3. [The last two sentences appear to be incomplete and possibly refer to a different antenna array.] 11 The first signal value s was measured at time 1. 11 and s 31 , in t 12 The second signal value s was measured at time t. 12 and s 32 Then ADC 1 is switched to be connected to antenna 2, and ADC 2 is switched to be connected to antenna 4. 21 Measure the first signal value s at time 1 21 and s 41 , and then t 22 Measure the second signal value s at time 1 22 and s 42 Where the time interval Δt is t 12 -t 11 =t 22 -t 21 , and t 21 >t 12 In this way, signal measurements at four locations can be completed using only two analog-to-digital converters, thus significantly reducing hardware costs.

[0158] Step 902: Linearly merge the first signal value and the second signal value corresponding to multiple acquisition locations to obtain multiple target signal values.

[0159] In some embodiments, the multiple merging coefficients for linear merging are determined by the time interval corresponding to each acquisition location.

[0160] For example, the first signal values ​​corresponding to multiple acquisition locations are denoted as s. 1,1 s 2,1 ... s K,1 The second signal values ​​corresponding to the multiple acquisition locations are denoted as s. 1,2 s 2,2 ... s K,2 The signal processing device can linearly combine multiple first signal values ​​and second signal values, i.e., s k,t =s k,1 +c*s k,2 , where k={1,2,...,K}, K is the number of real signals collected at the same time, and c is the merging coefficient, the value of which is determined by the time interval Δt.

[0161] In some communication systems, reconfigurable intelligent surfaces (RIS) can be used to assist signal transmission. RIS typically consists of reflective or transmissive arrays composed of electromagnetic units that can control electromagnetic signals. However, when an electromagnetic signal is reflected or transmitted, one or more physical properties of the signal are altered by the electromagnetic units it passes through, such as amplitude, phase, polarization direction, and frequency. In practical applications, the electromagnetic units of a RIS can include signal measurement circuitry. By measuring the signal amplitude twice at each electromagnetic unit (i.e., a first signal value and a second signal value), and linearly combining the two measurement results, the target signal value is obtained. The combining coefficient is determined by the time interval between the two measurements. Finally, the precoding required for the RIS can be determined based on the target signal value corresponding to each electromagnetic unit. For example, the RIS might take the conjugate of the target signal value obtained at each electromagnetic unit and use the phase of this conjugate signal as the precoding phase of that electromagnetic unit. In some cases, the electromagnetic units on the RIS can only be configured with discrete phase values. In such cases, the discrete phase closest to the phase of the conjugate signal can be selected from the available set of discrete phase values ​​as the precoding phase.

[0162] In some embodiments, the multiple merging coefficients for linear merging are determined by the time interval corresponding to each acquisition location and the acquisition location itself.

[0163] For example, the signal processing device can acquire first signal values ​​corresponding to multiple acquisition locations at a first time t1, denoted as s. 1,1 s 2,1 ... s K,1 At the second time t2, the second signal values ​​corresponding to multiple acquisition positions are collected, denoted as s. 1,2 s 2,2 ... s K,2The interval between the first and second time points is Δt. The signal processing device can linearly combine multiple first and second signal values, i.e., s. k,t =s k,1 +c k *s k,2 Where k = {1, 2, ..., K}, K is the number of real signals acquired at the same time, and c k is the merging coefficient of the first and second signal values ​​in the k-th group, and its value is determined by the time interval Δt and the acquisition position.

[0164] For example, in some cases, multiple s1 and s2 samples are collected from different locations, and these locations are not coplanar. In such cases, the determination of the merging coefficient needs to consider the influence of the collection locations. For instance, the coordinates of the collection location can be projected onto a preset reference direction, and the merging coefficient corresponding to that location can be determined based on the projection value and Δt. Alternatively, the distance from the coordinates of the collection location to a preset reference plane can be calculated, and the merging coefficient corresponding to that location can be determined based on this distance and Δt.

[0165] As one embodiment provided in this disclosure, for scenarios involving multiple measurements, the signal values ​​can be processed using a matrix approach.

[0166] In some embodiments, the target signal values ​​corresponding to multiple acquisition locations are represented by a first signal matrix; the signal processing result includes at least one of the following:

[0167] First signal matrix;

[0168] The magnitude of each element in the first signal matrix;

[0169] The conjugate matrix of the first signal matrix;

[0170] The second signal matrix is ​​obtained by transforming the first signal matrix;

[0171] The magnitudes of the R elements with the largest magnitudes in the second signal matrix and / or the positions of the R elements with the largest magnitudes in the second signal matrix; R is an integer greater than or equal to 1.

[0172] This transformation process can be understood as multiplying the first signal matrix by a transformation matrix. The transformation process includes at least one of the following: Fourier transform, fractional Fourier transform, Laplace transform, Wegener transform, wavelet transform, principal component analysis, rotational transform, and affine transform.

[0173] For example, the signal processing device can form a matrix M1 from multiple first signal values ​​acquired at a first moment and form a matrix M2 from multiple second signal values ​​acquired at a second moment. M1 and M2 can be one-dimensional vectors, two-dimensional matrices, three-dimensional matrices or high-dimensional matrices, and M1 and M2 have the same dimension.

[0174] In some embodiments, the dimensions of matrix M can be determined by multiple acquisition locations. Taking a CCD in optical signal measurement as an example, such as... Figure 11 As shown, the CCD array comprises 4 rows and 8 columns of photosensitive units. Based on the signal processing method provided in this embodiment, 32 first signal values ​​and 32 second signal values ​​can be measured at a first time and a second time, respectively. According to the arrangement of the photosensitive units on the CCD array, the 32 first signal values ​​can be combined into a 4x8 matrix M1, and the 32 second signal values ​​can be combined into a 4x8 matrix M2. The target signal value can also be represented by matrix M3, i.e., M3 = M1 + c * M2, where c is a linear combining coefficient determined by the time difference between the first and second time points.

[0175] For example, the signal processing device can calculate the conjugate of M3 to obtain M3* as the target result. Alternatively, the signal processing device can perform a transformation on M3 to obtain M... 3t As a desired outcome, taking the Fourier transform as an example, performing a Fourier transform on M3 is equivalent to multiplying M3 by a Fourier matrix. The signal processing device can also perform a transform on M3 to obtain M... 3t In M 3t The maximum modulus value and its position are determined as the target result.

[0176] The following description uses Fourier transform as an example to illustrate the matrix calculation method provided in the embodiments of this disclosure.

[0177] For example, the signal processing device uses an antenna array to receive electromagnetic signals. This antenna array comprises 16 rows and 16 columns of antenna elements. The incident electromagnetic signal is a plane wave with a frequency of f = 3.5 GHz, a wavelength of λ ≈ 8.6 cm, a period of T ≈ 2.86e-11 s, and an antenna spacing of half a wavelength. At t1 = 0, the antenna array collects 256 first signal values ​​from each antenna element, forming a 16*16 matrix M1. The values ​​of the elements in M1 are as follows... Figure 12 As shown. After a quarter-cycle interval (i.e., t2≈7.14e-11s), the antenna array collected 256 second signal values ​​from each antenna element, forming a 16*16 matrix M2. The values ​​of the elements in M2 are as follows: Figure 13 Afterwards, the signal processing device can obtain the matrix form of the target signal value, M3 = M1 + c * M2. Based on Δt = t2 - t2 = T / 4, the linear combining coefficient c = e^(-t / t) can be determined. j(2πT / 4 / R-π)=-j, that is, M3 = M1 - jM2, and the modulus distribution of M3 is as follows Figure 14 As shown.

[0178] For example, the signal processing device can perform a discrete Fourier transform on M3 using the following formula 9.

[0179]

[0180] The corresponding matrix M is obtained through this transformation. 3t M 3t It remains a 16x16 matrix, M 3t The distribution of the modulus is as follows Figure 15 As shown.

[0181] In some cases, M3, determined by M1 and M2, can be used as the result of signal processing; for example, in holographic imaging, M3 can be used as recorded wavefront information to calculate holographic images.

[0182] In some cases, the magnitude of M3, determined by M1 and M2, can be used as the signal processing result.

[0183] In some cases, the conjugate M3* of M3 determined by M1 and M2 can be used as the signal processing result; for example, in beamforming of a phased array, M3* can be used as the precoding for signal transmission.

[0184] In some cases, the Fourier transform M of M3, determined by M1 and M2, can be used to transform M... 3t The result is used as a signal processing outcome.

[0185] In some cases, the Fourier transform M of M3, determined by M1 and M2, can be used to transform M... 3t The maximum value of the modulus and the index of the maximum value are used as the signal processing result. For example, in Figure 15 In the middle, the maximum value is M. 3t (2,13)≈201.83, therefore the target result is {201.83,2,13}, where (2,13) ​​indicates that the maximum value is at M. 3t The 2nd row and 13th column.

[0186] In some cases, the Fourier transform M of M3, determined by M1 and M2, can be used to transform M... 3t The index of the maximum value of the modulus is used as the signal processing result, that is, the signal processing result is (2,13).

[0187] In some cases, the Fourier transform M of M3, determined by M1 and M2, can be used to transform M... 3t The indices of the top R elements with the largest modulo values ​​are used as the signal processing results, for example... Figure 15The indices of the first two maximum values ​​are (2,13) ​​and (3,13) respectively, so the signal processing result is {{2,13},{3,13}}.

[0188] For example, according to Figure 15 The location of the maximum value in the equation can determine the direction of arrival of the electromagnetic wave. For example, the azimuth angle φ and tilt angle θ of the incoming wave can be calculated using the following formulas 10 and 11.

[0189]

[0190] Among them, l max 'and m max 'They satisfy the following formulas 12 and 13 respectively.

[0191]

[0192] Among them, l max The row containing the maximum value, m max The column containing the maximum value, where N1 and N2 are the dimensions of the antenna array. Referring to the example above, l max =2,m max If N1 = N2 = 16, then the estimated signal arrival directions are φ ≈ 146.3° and θ ≈ 26.8°. When there are R maximum values, it indicates that the incident signal arrives at the sensor array from multiple directions; each maximum value can determine one arrival direction.

[0193] In related technologies, Fourier transform is typically performed directly on the amplitude matrix M1 or M2 of the real signal, and the direction of arrival is estimated based on the transformed matrix. However, this often results in two identical maxima appearing simultaneously in the transform domain, making it impossible to accurately determine the direction of arrival. For example... Figure 16 As shown, this is matrix M1 after undergoing a Fourier transform. 1t The distribution of the modulus, such as Figure 17 As shown, this is matrix M2 after undergoing a Fourier transform. 2t The distribution of the modulus, where M 1t Or M 2t There are two maximum points, corresponding to the two incoming wave directions respectively: θ≈26.8° and θ≈26.8°. Without prior information, the true direction of arrival cannot be determined. However, the signal processing method provided in this embodiment can eliminate the maximum point corresponding to the false direction of arrival in the transform domain, thereby allowing direct estimation of the signal's direction of arrival without prior information.

[0194] For related explanations, please refer to the descriptions in the above embodiments, which will not be repeated here.

[0195] It is understood that, in order to achieve the above-mentioned functions, the signal processing apparatus includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the algorithm steps of the various examples described in conjunction with the embodiments of this disclosure, this disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0196] This disclosure embodiment can divide the signal processing device into functional modules according to the above method embodiment. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one functional module. The integrated module can be implemented in hardware or software. It should be noted that the module division in this disclosure embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods. The following description uses the example of dividing each functional module according to each function.

[0197] for example, Figure 18 This is a structural diagram of a signal processing apparatus 180 provided in an embodiment of this disclosure. The signal processing apparatus 180 can execute the signal processing method provided in the above-described method embodiment. Figure 18 As shown, the signal processing device 180 includes a signal acquisition module 1801 and a signal processing module 1802.

[0198] The signal acquisition module 1801 is used to measure the signal under test to obtain a first signal value and a second signal value. The first signal value and the second signal value are the signal values ​​of the signal under test at different sampling points in the time domain.

[0199] The signal processing module 1802 is used to obtain the target signal value based on the time interval between the sampling points corresponding to the first signal value and the second signal value, the first signal value, and the second signal value.

[0200] The signal processing module 1802 is used to obtain the signal processing result based on the target signal value.

[0201] In some embodiments, the signal processing module 1802 is used to determine a merging coefficient based on a time interval, and to linearly merge a first signal value and a second signal value based on the merging coefficient to obtain a target signal value.

[0202] In some embodiments, the merging coefficient satisfies the following formula:

[0203] c = e j(βΔt+γ)

[0204] Where c is the merging coefficient, Δt is the time interval, β is the first preset value, and γ is the second preset value.

[0205] In some embodiments, the first preset value is determined by the period of the signal to be measured.

[0206] In some embodiments, the target signal value is obtained according to the following formula:

[0207] s3 = s1 + c * s2

[0208] Where s3 is the target signal value, s1 is the first signal value, s2 is the second signal value, and c is the merging coefficient.

[0209] In some embodiments, the signal processing result includes at least one of the following:

[0210] Target signal value;

[0211] Argument information of the target signal value;

[0212] The conjugate value of the target signal;

[0213] Argument information of the conjugate value of the target signal value;

[0214] The magnitude of the target signal value.

[0215] In some embodiments, the signal acquisition module 1801 is used to measure the signal to be tested to obtain a plurality of first signal values ​​and a plurality of second signal values, wherein the plurality of first signal values ​​correspond one-to-one with the plurality of second signal values.

[0216] In some embodiments, different first signal values ​​correspond to sampling points that differ from the period of the signal under test in the time domain by an integer multiple; different second signal values ​​correspond to sampling points that differ from the period of the signal under test in the time domain by an integer multiple.

[0217] In some embodiments, the signal processing module 1802 is used to obtain a third signal value based on a plurality of first signal values ​​and a fourth signal value based on a plurality of second signal values, and to obtain a target signal value according to the time interval between the sampling points corresponding to the first signal value and the second signal value, the third signal value, and the fourth signal value.

[0218] In some embodiments, the third signal value is obtained by summing multiple first signal values, and the fourth signal value is obtained by summing multiple second signal values; the target signal value is obtained by linearly combining the third signal value and the fourth signal value, and the combining coefficient of the linear combining is determined by the time interval.

[0219] In some embodiments, the signal acquisition module 1801 is used to determine multiple time intervals and measure the signal to be tested according to the multiple time intervals to obtain multiple first signal values ​​and multiple second signal values.

[0220] In some embodiments, the signal processing module 1802 is used to linearly combine multiple first signal values ​​and multiple second signal values ​​to obtain multiple target signal values.

[0221] In some embodiments, the multiple merging coefficients of linear merging are determined by multiple time intervals and multiple signal periods, respectively.

[0222] In some embodiments, at least two of the plurality of first signal values ​​are acquired at the same first time, and / or at least two of the plurality of second signal values ​​are acquired at the same second time.

[0223] In some embodiments, the signal processing result includes at least one of the following:

[0224] Multiple target signal values;

[0225] Argument information of multiple target signal values;

[0226] The conjugate value of multiple target signal values;

[0227] Argument information of the conjugate values ​​of multiple target signal values;

[0228] The magnitude of multiple target signal values.

[0229] In some embodiments, the signal acquisition module 1801 is used to determine multiple acquisition locations and the time interval corresponding to each acquisition location, and to measure the signal to be tested at each acquisition location according to the corresponding time interval to obtain the first signal value and the second signal value corresponding to the multiple acquisition locations respectively.

[0230] In some embodiments, the signal processing module 1802 is used to linearly merge the first signal value and the second signal value corresponding to multiple acquisition locations to obtain multiple target signal values.

[0231] In some embodiments, the multiple merging coefficients for linear merging are determined by the time interval corresponding to each acquisition location.

[0232] In some embodiments, the target signal values ​​corresponding to multiple acquisition locations are represented by a first signal matrix; the signal processing result includes at least one of the following:

[0233] First signal matrix;

[0234] The magnitude of each element in the first signal matrix;

[0235] The conjugate matrix of the first signal matrix;

[0236] The second signal matrix is ​​obtained by transforming the first signal matrix;

[0237] The magnitudes of the R elements with the largest magnitudes in the second signal matrix and / or the positions of the R elements with the largest magnitudes in the second signal matrix; R is an integer greater than or equal to 1.

[0238] In some embodiments, the transformation process includes at least one of the following: Fourier transform, fractional Fourier transform, Laplace transform, Wegener transform, wavelet transform, principal component analysis, rotational transform, and affine transform.

[0239] In some embodiments, the signal to be measured includes an electromagnetic wave signal or a mechanical wave signal.

[0240] In some embodiments, the signal processing device 180 includes at least one sensor for acquiring the signal value of the signal to be measured.

[0241] In some embodiments, the signal processing device 180 further includes a switching unit, which is used to acquire signal values ​​collected by different sensors at different times.

[0242] In implementing the functions of the integrated modules described above in hardware, this disclosure provides another possible structure for the signal processing apparatus involved in the above embodiments. For example... Figure 19 As shown, the signal processing device 190 includes a processor 1902 and a bus 1904. Optionally, the signal processing device 190 may also include a memory 1901; alternatively, the signal processing device 190 may also include a communication interface 1903.

[0243] Processor 1902 may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with embodiments of this disclosure. Processor 1902 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with embodiments of this disclosure. Processor 1902 may also be a combination of functions implementing computation, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0244] The communication interface 1903 is used to connect to other devices via a communication network. This communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0245] The memory 1901 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0246] As one possible implementation, the memory 1901 can exist independently of the processor 1902. The memory 1901 can be connected to the processor 1902 via a bus 1904 and is used to store instructions or program code. When the processor 1902 calls and executes the instructions or program code stored in the memory 1901, it can implement the method described in any embodiment of this disclosure.

[0247] In another possible implementation, the memory 1901 can also be integrated with the processor 1902.

[0248] The 1904 bus can be an extended industry standard architecture (EISA) bus, etc. The 1904 bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 19 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0249] Some embodiments of this disclosure provide a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium) storing computer program instructions that, when executed on a computer, cause the computer to perform the methods described in any of the above embodiments.

[0250] For example, the computer-readable storage media described above may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tapes), optical disks (e.g., compact disks (CDs), digital versatile disks (DVDs), etc.), smart cards, and flash memory devices (e.g., erasable programmable read-only memory (EPROMs), cards, sticks, or key drives, etc.). The various computer-readable storage media described in this disclosure may represent one or more devices for storing information and / or other machine-readable storage media. The term "machine-readable storage media" may include, but is not limited to, wireless channels and various other media capable of storing, containing, and / or carrying instructions and / or data.

[0251] This disclosure provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods described in any of the above embodiments.

[0252] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any changes or substitutions within the technical scope disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A signal processing method, characterized in that, Applied to a signal processing apparatus, the method includes: The first signal value and the second signal value are obtained by measuring the signal to be tested. The first signal value and the second signal value are the signal values ​​of the signal to be tested at different sampling points in the time domain. The target signal value is obtained based on the time interval between the sampling points corresponding to the first signal value and the second signal value, the first signal value, and the second signal value; Based on the target signal value, the signal processing result is obtained.

2. The method according to claim 1, characterized in that, The step of obtaining the target signal value based on the time interval between the sampling points corresponding to the first signal value and the second signal value, the first signal value, and the second signal value includes: The merging coefficient is determined based on the time interval; The first signal value and the second signal value are linearly combined based on the merging coefficient to obtain the target signal value.

3. The method according to claim 2, characterized in that, The merging coefficient satisfies the following formula: c=e j(βΔt+γ) Where c is the merging coefficient, Δt is the time interval, β is the first preset value, and γ is the second preset value.

4. The method according to claim 3, characterized in that, The first preset value is determined by the period of the signal to be measured.

5. The method according to claim 2, characterized in that, The target signal value is obtained according to the following formula: s3 = s1 + c * s2 Wherein, s3 is the target signal value, s1 is the first signal value, s2 is the second signal value, and c is the merging coefficient.

6. The method according to claim 1, characterized in that, The signal processing result includes at least one of the following: The target signal value; The argument information of the target signal value; The conjugate value of the target signal value; The argument information of the conjugate value of the target signal value; The magnitude of the target signal value.

7. The method according to claim 1, characterized in that, The process of measuring the signal to be tested to obtain a first signal value and a second signal value includes: The signal to be tested is measured to obtain a plurality of first signal values ​​and a plurality of second signal values, wherein the plurality of first signal values ​​and the plurality of second signal values ​​correspond one-to-one.

8. The method according to claim 7, characterized in that, Different first signal values ​​correspond to sampling points that differ from the period of the signal under test in the time domain by an integer multiple; different second signal values ​​correspond to sampling points that differ from the period of the signal under test in the time domain by an integer multiple.

9. The method according to claim 8, characterized in that, The step of obtaining the target signal value based on the time interval between the sampling points corresponding to the first signal value and the second signal value, the first signal value, and the second signal value includes: A third signal value is obtained based on the plurality of first signal values, and a fourth signal value is obtained based on the plurality of second signal values; The target signal value is obtained based on the time interval between the sampling points corresponding to the first signal value and the second signal value, the third signal value, and the fourth signal value.

10. The method according to claim 9, characterized in that, The third signal value is obtained by summing the plurality of first signal values, and the fourth signal value is obtained by summing the plurality of second signal values; the target signal value is obtained by linearly combining the third signal value and the fourth signal value, and the combining coefficient of the linear combination is determined by the time interval.

11. The method according to claim 7, characterized in that, The measurement of the signal to be tested to obtain multiple first signal values ​​and multiple second signal values ​​includes: Multiple time intervals are determined, and the signal to be tested is measured according to the multiple time intervals to obtain multiple first signal values ​​and multiple second signal values.

12. The method according to claim 11, characterized in that, The step of obtaining the target signal value based on the time interval between the sampling points corresponding to the first signal value and the second signal value, the first signal value, and the second signal value includes: The plurality of first signal values ​​and the plurality of second signal values ​​are linearly combined to obtain a plurality of target signal values.

13. The method according to claim 12, characterized in that, The multiple merging coefficients of the linear merging are determined by multiple time intervals and multiple signal periods.

14. The method according to claim 11, characterized in that, At least two of the plurality of first signal values ​​are collected at the same first time, and / or at least two of the plurality of second signal values ​​are collected at the same second time.

15. The method according to claim 11, characterized in that, The signal processing result includes at least one of the following: Multiple target signal values; Argument information of the multiple target signal values; The conjugate value of the plurality of target signal values; Argument information of the conjugate values ​​of the multiple target signal values; The magnitude of the multiple target signal values.

16. The method according to claim 7, characterized in that, The measurement of the signal to be tested to obtain multiple first signal values ​​and multiple second signal values ​​includes: Multiple acquisition locations and time intervals corresponding to each acquisition location are determined. At each acquisition location, the signal to be tested is measured according to the corresponding time interval to obtain the first signal value and the second signal value corresponding to the multiple acquisition locations respectively.

17. The method according to claim 16, characterized in that, The step of obtaining the target signal value based on the time interval between the sampling points corresponding to the first signal value and the second signal value, the first signal value, and the second signal value includes: The first signal value and the second signal value corresponding to the multiple acquisition locations are linearly combined to obtain multiple target signal values.

18. The method according to claim 17, characterized in that, The multiple merging coefficients of the linear merging are determined by the time interval corresponding to each acquisition location.

19. The method according to claim 16, characterized in that, The target signal values ​​corresponding to the multiple acquisition locations are represented by a first signal matrix; the signal processing result includes at least one of the following: The first signal matrix; The magnitude of each element in the first signal matrix; The conjugate matrix of the first signal matrix; The second signal matrix is ​​obtained by transforming the first signal matrix. The magnitudes of the R elements with the largest magnitudes in the second signal matrix and / or the positions of the R elements with the largest magnitudes in the second signal matrix; R is an integer greater than or equal to 1.

20. The method according to claim 19, characterized in that, The transformation process includes at least one of the following: Fourier transform, fractional Fourier transform, Laplace transform, Wegener transform, wavelet transform, principal component analysis, rotational transformation, and affine transformation.

21. The method according to claim 1, characterized in that, The signal to be tested includes electromagnetic wave signals or mechanical wave signals.

22. The method according to claim 1, characterized in that, The signal processing device includes at least one sensor for acquiring the signal value of the signal to be measured.

23. The method according to claim 22, characterized in that, The signal processing device further includes a switching unit, which is used to acquire signal values ​​collected by different sensors at different times.

24. A signal processing apparatus, characterized in that, include: Signal acquisition module and signal processing module; The signal acquisition module is used to acquire the signal to be measured; the signal acquisition module includes at least one sensor; the signal processing device is used to perform the method as described in any one of claims 1 to 23 through the signal acquisition module and the signal processing module.

25. The signal processing apparatus according to claim 24, characterized in that, The signal acquisition module also includes a switching circuit; the switching circuit is used to connect or disconnect the measurement circuit according to a preset time interval, and / or to switch the connection between different sensors and the signal processing module in the signal acquisition module.

26. The signal processing apparatus according to claim 24, characterized in that, The signal acquisition module also includes a delay circuit; the delay circuit is used to adjust the transmission path length between the sensor and the signal processing module.

27. The signal processing apparatus according to claim 24, characterized in that, The signal acquisition module also includes an analog-to-digital converter; the analog-to-digital converter is used to convert the signal under test from an analog signal to a digital signal.

28. The signal processing apparatus according to claim 24, characterized in that, The signal acquisition module also includes at least one of the following: a mixer, a filter, an amplifier, and a memory.

29. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 23.

30. A computer program product, characterized in that, The computer program product includes computer program instructions that, when executed by a processor, implement the method as described in any one of claims 1 to 23.

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