A method and system for long-range acoustic positioning based on moving objects

Abstract

The application provides a long-distance acoustic positioning method and system based on a moving object, comprising: generating an acoustic pulse signal by modulating a preset pseudo-random noise model and sending it outward; receiving the acoustic pulse signal by a matching filter in a preset microphone, performing Doppler distortion compensation, and detecting an obvious correlation peak; taking the position of the correlation peak as the time when the acoustic pulse signal reaches the microphone, and calculating the relative time delay of the pulse arrival of the diagonal microphone; based on the relative time delay of the pulse arrival of the diagonal microphone, establishing a three-dimensional coordinate system through the moving object, determining the coordinate positions of the microphone and the moving object in the three-dimensional coordinate system; and positioning the moving object based on the coordinate positions of the microphone and the moving object in the three-dimensional coordinate system. The application solves the problem that the existing moving object is difficult to accurately position, and realizes accurate positioning through an acoustic pulse signal.

Description

Technical Field

[0001] This invention relates to the field of positioning technology, and in particular to a long-range acoustic positioning method and system based on moving objects. Background Technology

[0002] Drones have profoundly transformed the logistics industry. Compared to ground-based delivery personnel, drones can bypass complex urban traffic and quickly deliver packages to their destinations. Therefore, drones are ideal for delivering time-sensitive parcels, such as food or medical supplies. The current drone delivery process involves a delivery person loading the package onto the drone. The drone then takes off and climbs to its cruising altitude (typically 120m) before flying to its destination, usually a nearby parcel locker. As the drone approaches the locker, it enters a precision landing phase. In this phase, the drone first approaches the locker horizontally to align itself vertically as closely as possible. Then, it begins its vertical descent. Once the drone safely lands on the landing platform, the package is dropped into the locker, which the user then retrieves. During the horizontal approach, the drone's flight control system (flight controller) relies primarily on GPS or RTK technologies for positioning. During the vertical descent, the flight controller must ensure the drone is vertically aligned with the landing platform. However, as the drone descends, the performance of GPS or RTK gradually becomes unreliable, or even fails. This type of problem is particularly prominent in scenarios such as urban canyons. This is because buildings near the drone can reflect or even block GPS satellite signals, leading to severe multipath effects or non-line-of-sight signal propagation.

[0003] To address this issue, the current mainstream approach is to deploy an additional visual marker at the ground airport to assist drones in precise landing. The drone detects the marker's position using a ground-facing camera, thus determining its own position relative to the landing platform. However, in practice, the visual marker-based solution has several drawbacks. Marker recognition is highly sensitive to lighting conditions. When ambient light is poor, the visual marker is difficult for the camera to detect. Therefore, this positioning system cannot operate at night or in rainy or foggy weather. The positioning range is limited. Depending on the camera's field of view and the degree of vertical alignment between the drone and the marker, the camera may not be able to completely capture the marker, leading to positioning failure. Furthermore, it limits the overall system throughput. Because this solution requires a line-of-sight path between the camera and the marker, the marker only supports the descent of a single drone. Otherwise, drones close to the marker will visually obstruct it, preventing other drones from positioning. Summary of the Invention

[0004] This invention provides a long-range acoustic positioning method and system based on moving objects, which solves the problem of the difficulty in accurately positioning moving objects and achieves precise positioning through acoustic pulse signals.

[0005] This invention provides a long-range acoustic localization method based on moving objects, comprising:

[0006] Acoustic pulse signals are generated by modulation using a preset pseudo-random noise model and then transmitted outwards.

[0007] The acoustic pulse signal is received by a matched filter in a preset microphone, and Doppler distortion compensation is performed to detect obvious correlation peaks.

[0008] The position of the relevant peak is used as the time when the acoustic pulse signal arrives at the microphone, and the relative pulse arrival delay of the diagonal microphone is calculated;

[0009] Based on the relative arrival time delay of the pulses from the diagonal microphone, a three-dimensional coordinate system is established using the moving object, and the coordinate positions of the microphone and the moving object in the three-dimensional coordinate system are determined.

[0010] The moving object is located based on the coordinates of the microphone and the moving object in the three-dimensional coordinate system.

[0011] According to the present invention, a long-range acoustic localization method based on a moving object is provided, wherein the step of generating an acoustic pulse signal by modulating a preset pseudo-random noise model and transmitting it outward specifically includes:

[0012] Obtain the identification code of the moving object;

[0013] A random seed is set using the identification code of the moving object to generate multiple consecutive Gaussian random variables;

[0014] An acoustic pulse signal is generated based on the Gaussian random variable and then transmitted outward.

[0015] According to the present invention, a long-range acoustic localization method based on a moving object is provided, wherein the acoustic pulse signal is received through a matched filter in a preset microphone, and Doppler distortion compensation is performed to detect a significant correlation peak, specifically including:

[0016] Acoustic pulse signals are received through the matched filter;

[0017] The signal-to-noise ratio gain of the matched filter is improved by compensating for Doppler distortion in the acoustic pulse signal.

[0018] Based on the improved signal-to-noise ratio gain, a clearly correlated peak was selected.

[0019] According to the present invention, a long-range acoustic localization method based on a moving object is provided, which uses the position of the correlation peak as the time when the acoustic pulse signal arrives at the microphone, and calculates the relative arrival time delay of the pulse at the diagonal microphone, specifically including:

[0020] The position of the relevant peak is obtained, and the position of the relevant peak is used as the time when the acoustic pulse signal arrives at the microphone;

[0021] Diagonal microphones have the largest microphone interval. By grouping diagonal microphones together, the relative arrival time delay of pulses from diagonal microphones can be calculated.

[0022] According to the present invention, a long-range acoustic localization method based on a moving object is provided. Based on the relative time delay of pulse arrival from the diagonal microphone, a three-dimensional coordinate system is established using the moving object, and the coordinate positions of the microphone and the moving object in the three-dimensional coordinate system are determined. Specifically, the method includes:

[0023] Based on the relative time delay of the pulse arrival from the diagonal microphone, two sets of hyperboloid equations are established using a moving object;

[0024] A three-dimensional coordinate system is established using the two sets of hyperboloid equations;

[0025] Determine the planar coordinate position of the microphone and the planar coordinate position of the moving object within the three-dimensional coordinate system;

[0026] Obtain the height information of the moving object, and determine the three-dimensional coordinate position of the moving object based on the height information.

[0027] According to the present invention, a long-range acoustic localization method based on a moving object is provided, which locates the moving object based on the coordinate positions of the microphone and the moving object in a three-dimensional coordinate system, specifically including:

[0028] Determine the planar coordinate positions of multiple microphones and the three-dimensional coordinate positions of the moving object;

[0029] Calculate the horizontal and vertical distances between the moving object and each microphone;

[0030] The moving object is located based on the horizontal and vertical distances.

[0031] The present invention also provides a long-range acoustic positioning system based on moving objects, the system comprising:

[0032] The pulse signal generation module is used to generate acoustic pulse signals by modulation using a preset pseudo-random noise model and then transmit them outwards.

[0033] The pulse signal receiving module is used to receive the acoustic pulse signal through a matched filter in a preset microphone, perform Doppler distortion compensation, and detect obvious correlation peaks.

[0034] The delay calculation module is used to take the position of the relevant peak as the time when the acoustic pulse signal arrives at the microphone, and to calculate the relative delay of the pulse arrival at the diagonal microphone;

[0035] The coordinate determination module, based on the relative time delay of the pulse arrival of the diagonal microphone, establishes a three-dimensional coordinate system through the moving object, and determines the coordinate positions of the microphone and the moving object in the three-dimensional coordinate system;

[0036] The positioning module locates the moving object based on the coordinate positions of the microphone and the moving object in a three-dimensional coordinate system.

[0037] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the long-distance acoustic positioning method based on moving objects as described above.

[0038] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the long-range acoustic localization method based on moving objects as described above.

[0039] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the long-range acoustic positioning method based on moving objects as described above.

[0040] This invention provides a long-range acoustic localization method and system based on moving objects. The moving object generates acoustic pulse signals through pseudo-random noise model modulation and transmits them outward. A microphone array set on the ground receives the acoustic pulse signals, performs Doppler distortion compensation, improves the signal-to-noise ratio gain of the matched filter, and selects obvious correlation peaks. By establishing a three-dimensional coordinate system, the coordinate positions of the moving object and the microphones are accurately determined, achieving accurate localization of the moving object. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0042] Figure 1This is one of the flowcharts of a long-range acoustic localization method based on moving objects provided by the present invention;

[0043] Figure 2 This is the second flowchart of a long-range acoustic localization method based on moving objects provided by the present invention;

[0044] Figure 3 This is the third flowchart of a long-range acoustic localization method based on moving objects provided by the present invention;

[0045] Figure 4 This is the fourth flowchart of a long-range acoustic localization method based on moving objects provided by the present invention;

[0046] Figure 5 This is the fifth flowchart of a long-range acoustic localization method based on moving objects provided by the present invention;

[0047] Figure 6 This is the sixth flowchart of a long-range acoustic localization method based on moving objects provided by the present invention;

[0048] Figure 7 This is a schematic diagram of the module connection of a long-range acoustic positioning system based on moving objects provided by the present invention;

[0049] Figure 8 This is a schematic diagram of the structure of the electronic device provided by the present invention;

[0050] Figure 9 This is a schematic diagram of UAV positioning based on acoustic signals provided by the present invention;

[0051] Figure 10 This is a schematic diagram of asynchronous codewords provided by the present invention;

[0052] Figure 11 This is a schematic diagram of the matched filter bank provided by the present invention.

[0053] Figure label:

[0054] 110: Pulse signal generation module; 120: Pulse signal receiving module; 130: Time delay calculation module; 140: Coordinate determination module; 150: Positioning module;

[0055] 810: Processor; 820: Communication interface; 830: Memory; 840: Communication bus. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0057] The following is combined Figures 1-6 The present invention describes a long-range acoustic localization method based on a moving object, comprising:

[0058] S100: Generate an acoustic pulse signal by modulating it using a preset pseudo-random noise model and send it outward;

[0059] S200: The acoustic pulse signal is received through the matched filter in the preset microphone, and Doppler distortion compensation is performed to detect obvious correlation peaks;

[0060] S300. The position of the relevant peak is used as the time when the acoustic pulse signal arrives at the microphone, and the relative delay of the pulse arrival at the diagonal microphone is calculated.

[0061] S400. Based on the relative time delay of the pulse arrival of the diagonal microphone, a three-dimensional coordinate system is established through the moving object, and the coordinate positions of the microphone and the moving object in the three-dimensional coordinate system are determined.

[0062] S500. Based on the coordinate positions of the microphone and the moving object in the three-dimensional coordinate system, the moving object is located.

[0063] refer to Figure 9 In this invention, the moving object is exemplified by a drone. A speaker is mounted on the drone to transmit acoustic pulse signals for auxiliary positioning. Multiple microphones are deployed at the ground airport as positioning anchor points. The ground airport detects the acoustic pulses from the signals collected by each microphone and calculates the relative time delay of the pulse signals to locate the drone. This method is unaffected by lighting conditions, has a wider horizontal positioning range, supports positioning of multiple drones, and ensures positioning accuracy.

[0064] The acoustic pulse signal is generated by modulation using a preset pseudo-random noise model and then transmitted outward, specifically including:

[0065] S101. Obtain the identification code of the moving object;

[0066] S102. Set a random seed using the identification code of the moving object to generate multiple consecutive Gaussian random variables;

[0067] S103. Generate an acoustic pulse signal based on the Gaussian random variable and send it outward.

[0068] In practical positioning, the acoustic pulse signals transmitted by drones need to be human-friendly, as drones operate in urban areas and their sounds must not cause auditory discomfort to residents. The system must also support concurrent detection and identification: multiple drones may take off and land in the same airspace. Multiple drones may transmit acoustic pulse signals simultaneously, therefore the system needs to be able to detect the pulses of each drone from conflicting signals and identify which drone each detected pulse belongs to. Furthermore, the positioning system should prevent malicious attackers from spoofing drone pulses to create misleading information; otherwise, drones guided by incorrect information are likely to crash.

[0069] In this invention, a pseudo-random noise (PRN) model is used to modulate and generate acoustic pulse signals emitted by a drone. Here, S represents the acoustic pulse signal transmitted by a particular drone.

[0070] s = [s0, s1, ... s n ,…,s N-1 ]

[0071] Where s n This represents a codeword in the pulse signal, where N is the number of codewords in one pulse. The pseudo-random seed is set using each drone's identification code (ID), generating a series of N Gaussian random variables, which serve as the pulse signal transmitted by that drone. In the specific implementation, the bit rate is equal to the speaker's sampling rate, i.e., 48kHz.

[0072] Since PRN is essentially a series of Gaussian random signals, it shares the same acoustic characteristics as white noise. Therefore, PRN pulses are difficult for the human ear to detect, especially when masked by propeller noise, and thus pose no noise pollution problem for residents. Another characteristic of PRN is that multiple PRN pulses are orthogonal to each other, provided they are statistically independent. Similar to the principle of Code Division Multiple Access (CDMA), this characteristic allows for parallel detection and identification of pulses. Because PRN pulses are pseudo-randomly generated, it is difficult for a malicious attacker to generate identical pulses without knowing the random seed.

[0073] The acoustic pulse signal is received through a matched filter within a preset microphone, and Doppler distortion compensation is performed to detect a significant correlation peak, specifically including:

[0074] S201. Receive acoustic pulse signals through the matched filter;

[0075] S202. Based on the acoustic pulse signal, the signal-to-noise ratio gain of the matched filter is improved through Doppler distortion compensation;

[0076] S203. Based on the improved signal-to-noise ratio gain, a significant correlation peak is selected.

[0077] This invention employs a matched filter to detect acoustic pulse signals, using the transmitted acoustic pulse signal as a template and correlating it with the received signal. Formally, the signal received by a microphone can be written as...

[0078] x=αs+w (1)

[0079] Where α represents the pulse attenuation coefficient, w = [w0, w1, ..., s N-1 ] represents Gaussian random noise. To detect the pulse signal s, the matched filter performs a correlation operation between the signal s and the received signal x. The correlation result can be expressed as:

[0080] y = s T x=αs T s+s T w(2)

[0081] By streaming the received signal into a matched filter, the matched filter streams the correlation result y. If a clear correlation peak is found in the output, it can be determined that the pulse signal has been detected. In practical applications, the signal-to-noise ratio of the received signal is very low. This problem is reflected in the matched filter as the correlation peak is masked by the noise layer, and without a clear correlation peak, it is difficult to detect.

[0082] To address the low signal-to-noise ratio (SNR) problem, an effective solution is to increase the pulse length N, thereby increasing the SNR gain of the matched filter. By comparing the SNR of the input and output signals of the matched filter, considering signal x, its SNR is...

[0083]

[0084] Among them |·| 2 σ represents the 2-norm of a vector. 2 Let w represent the variance of the noise. Similarly, the signal-to-noise ratio of the signal y is...

[0085]

[0086] Among them E[ww T ]=σ 2 I represents the covariance of the noise w. Therefore, the signal-to-noise ratio (SNR) gain G of the matched filter is:

[0087]

[0088] The above equation reveals a fundamental fact: the signal-to-noise ratio (SNR) gain G equals the pulse length N. In principle, a longer pulse length can be used to achieve a higher SNR. However, when Doppler distortion is present, increasing the pulse length does not necessarily result in a higher SNR. Therefore, Doppler distortion compensation is required to ensure a high SNR gain.

[0089] The movement of drones introduces Doppler distortion into acoustic pulses. The severity of the Doppler effect is inversely proportional to the propagation speed of the wireless signal. Due to the slow speed of sound, acoustic signals will suffer severe Doppler distortion. The Doppler effect gradually causes the received pulse to become out of sync with the original transmitted pulse in the time domain, resulting in the received codeword being out of sync with the transmitted codeword.

[0090] When the Doppler effect is present, the duration of the received codeword may expand or contract:

[0091]

[0092] Where T c and T′ c These represent the duration of the original codeword and the duration of the codeword affected by the Doppler effect, respectively. v is the radial velocity of the UAV (relative to the microphone), and c is the speed of sound. Note that the microphone operates at a fixed sampling rate of 1 / T. c The sampling of codewords means that each codeword introduces a time misalignment of value Δ.

[0093]

[0094] Timing misalignment accumulates as the number of characters increases, eventually leading to asynchronous character generation, such as... Figure 10 As shown. It can be calculated that when the codeword sequence number... When the accumulated time misalignment is greater than the length T′ of the received codeword. c This means that all codewords following this codeword are out of sync with the original codeword. Let the codeword sampled by the receiver be s′. n (n=0,1,2,…), then the following formula holds.

[0095]

[0096] Asynchronous codewords will severely degrade the signal-to-noise ratio (SNR) gain of the matched filter. The derivation of the SNR gain above assumes that the codewords in the received signal are synchronous with the original codewords. This assumption may no longer hold when the Doppler effect is present. In the current case, the SNR gain of the matched filter needs to be rewritten as:

[0097]

[0098] Where s′ represents the received pulse [s′0, s′1, ..., s′] N-1 ].

[0099] When the number of codewords N is less than L, there is no codeword asynchrony issue, i.e., s′ = s, and G′ = G = N. However, when N ≥ L, only the first L codewords of s′ are synchronized with the first L codewords of s, while the remaining codewords are not synchronized.

[0100]

[0101] It is evident that once the codeword length N exceeds L, the signal-to-noise ratio (SNR) gain of the matched filter actually decreases with increasing codeword length. Therefore, a longer codeword is needed to address the low SNR problem. On the other hand, due to the Doppler effect, when the codeword length exceeds L, the SNR gain decreases with increasing codeword length. It is important to note... The value of L is largely limited by the low sound speed c.

[0102] Due to the Doppler effect, the received pulse codewords are out of sync with the matched filter template, thus reducing the performance of the matched filter.

[0103] The performance of the matched filter is improved by compensating for Doppler distortion. Equation (6) shows that the Doppler effect scales the duration of the pulse codeword. If the radial velocity v of the UAV relative to the microphone is known, the actual duration of the codeword can be calculated. The original PRN pulse template is then resampled using this parameter to generate a signal template synchronized with the received pulse codeword. The template for detecting the PRN pulse using the new signal template compensates for the interference of the Doppler effect, thereby allowing the PRN pulse length to be increased as needed, thus improving the signal-to-noise ratio gain.

[0104] In the actual measurement process, since the radial velocity v of the microphone is unknown, a linear search method is used to traverse the microphone's radial velocity. For example... Figure 11 As shown, let V = {v0, v1, ..., v} n ,…,v N-1 Let} be the set of possible velocities of the drone. For each velocity v n Perform resampling and related calculations.

[0105] The specific resampling process is as follows: According to formula (6), the length of the received pulse codeword is estimated to be... Using this length as the new sampling interval, the original PRN pulse signal is resampled, and the newly generated pulse is denoted as s′. n .

[0106] The specific calculation process is as follows: the newly generated pulse s′ nUsing the template of the PyR filter, correlation calculations are performed with a segment of the signal collected by the microphone to obtain the corresponding correlation function.

[0107] If v n When the radial velocity of the UAV is consistent, the newly generated pulse s′ n This will restore codeword synchronization with the received pulse s′. n The correlation function between s and s′ has the largest correlation peak. After performing the above operations on all velocities in set V, N correlation functions are obtained. Only the correlation function with the largest correlation peak needs to be retained. The largest correlation value means that the Doppler distortion of the pulse has been compensated to the greatest extent, and its corresponding search velocity v n It also most closely approximates the radial velocity of a real drone. It achieves compensation for Doppler distortion, increases the gain of the matched filter, and ensures the reception of acoustic pulse signals.

[0108] The position of the relevant peak is used as the time when the acoustic pulse signal arrives at the microphone, and the relative arrival time delay of the pulse at the diagonal microphone is calculated, specifically including:

[0109] S301. Obtain the position of the relevant peak and use the position of the relevant peak as the time when the acoustic pulse signal arrives at the microphone;

[0110] S302. The diagonal microphones have the largest microphone interval. Treat the diagonal microphones as a group and calculate the relative arrival time delay of the pulses of the diagonal microphones.

[0111] In this invention, a microphone is installed at each of the four corners of the landing platform, and these microphones are respectively labeled Mic0, Mic1, Mic2, and Mic3 in a clockwise direction. For each microphone... i The sound source is subjected to the aforementioned Doppler velocity compensation and pulse detection, resulting in a correlation function. From this correlation function, the correlation peak is identified, and its position is used as the pulse signal to reach the microphone. i The arrival time (ToA) is used to determine the arrival times of the pulse signal at the four microphones Mic0, Mic1, Mic2, and Mic3. For convenience, the arrival times are denoted as ToA0, ToA1, ToA2, and ToA3, respectively.

[0112] Calculate the Time Difference of Arrival (TDoA). In this invention, only the TDoA of diagonal microphone pairs is calculated, i.e., the microphone pair...<Mic0,Mic2> and<Mic1,Mic3> Because geometrically, diagonal microphones have the largest microphone spacing (i.e., aperture), they possess the finest spatial granularity.<Mic0,Mic2> and<Mic1,Mic3> The TDoA is defined as follows:

[0113]

[0114] Based on the relative arrival time delay of the pulses from the diagonal microphone, a three-dimensional coordinate system is established using a moving object, and the coordinate positions of the microphone and the moving object in the three-dimensional coordinate system are determined, specifically including:

[0115] S401. Based on the relative time delay of the pulse arrival of the diagonal microphone, establish two hyperboloid equations using the moving object;

[0116] S402. Establish a three-dimensional coordinate system using the two hyperboloid equations;

[0117] S403. Determine the planar coordinate position of the microphone and the planar coordinate position of the moving object in the three-dimensional coordinate system;

[0118] S404. Obtain the height information of the moving object, and determine the three-dimensional coordinate position of the moving object based on the height information.

[0119] In this invention, the relative arrival time τ of the two calculated pulses is... ＜0,2> and τ ＜1,3> The data is transmitted to the drone via WiFi. Based on this information, the drone can establish two sets of hyperboloid equations. Based on these equations, a 3D coordinate system is established, with the center of the landing platform as the origin. The x, y, and z coordinates of the four microphones are defined as M0 = (d, 0, 0), M1 = (0, -d, 0), M2 = (-d, 0, 0), and M3 = (0, d, 0), respectively. Given these, the definitions of the two hyperboloids along the x-axis and y-axis are as follows:

[0120]

[0121] Where, P = (P x ,P y ,P z P represents the coordinates of the drone. z The sign of is positive, while P x and P y The symbol is τ <0,2> and τ ＜1,3> The sign of P is determined by:x The symbol and τ ＜0,2> The signs are opposite; P y The symbol and τ ＜1,3＞ The signs are opposite. The system of equations (12) is undetermined because it only provides two constraints while the coordinates of the UAV have three unknowns (P). x ,P y , and P z However, P z This can be obtained through the drone's altitude sensors, such as barometers and LiDAR. Once P z Once confirmed, P can be solved. x and P y The three-dimensional coordinates of the drone were determined.

[0122] Based on the coordinate positions of the microphone and the moving object in the three-dimensional coordinate system, the moving object is located, specifically including:

[0123] S501. Determine the planar coordinate positions of multiple microphones and the three-dimensional coordinate positions of the moving object;

[0124] S502, Calculate the horizontal and vertical distances between the moving object and each microphone;

[0125] S503. Position the moving object according to the horizontal and vertical distances.

[0126] This invention determines the coordinates of the drone and the four microphones, and continuously updates the coordinate information to accurately locate the drone. Since the landing platform is generally located in an open area, there will be no obstruction or interference of acoustic pulse signals, and it will not be affected by lighting conditions, thus achieving precise landing.

[0127] refer to Figure 7 The present invention also discloses a long-range acoustic positioning system based on a moving object, the system comprising:

[0128] The pulse signal generation module 110 is used to generate an acoustic pulse signal by modulation through a preset pseudo-random noise model and send it outward;

[0129] The pulse signal receiving module 120 is used to receive the acoustic pulse signal through the matched filter in the preset microphone, perform Doppler distortion compensation, and detect obvious correlation peaks.

[0130] The delay calculation module 130 is used to take the position of the correlation peak as the time when the acoustic pulse signal arrives at the microphone, and to calculate the relative delay of the pulse arrival at the diagonal microphone;

[0131] The coordinate determination module 140, based on the relative time delay of the pulse arrival of the diagonal microphone, establishes a three-dimensional coordinate system through the moving object, and determines the coordinate positions of the microphone and the moving object in the three-dimensional coordinate system.

[0132] The positioning module 150 locates the moving object based on the coordinate positions of the microphone and the moving object in a three-dimensional coordinate system.

[0133] Among them, the pulse signal generation module 110 acquires the identification code of the moving object;

[0134] A random seed is set using the identification code of the moving object to generate multiple consecutive Gaussian random variables;

[0135] An acoustic pulse signal is generated based on the Gaussian random variable and then transmitted outward.

[0136] The pulse signal receiving module 120 receives acoustic pulse signals through the matched filter;

[0137] The signal-to-noise ratio gain of the matched filter is improved by compensating for Doppler distortion in the acoustic pulse signal.

[0138] Based on the improved signal-to-noise ratio gain, a clearly correlated peak was selected.

[0139] The delay calculation module 130 obtains the position of the correlation peak and uses the position of the correlation peak as the time when the acoustic pulse signal arrives at the microphone;

[0140] Diagonal microphones have the largest microphone interval. By grouping diagonal microphones together, the relative arrival time delay of pulses from diagonal microphones can be calculated.

[0141] The coordinate determination module 140 establishes two hyperboloid equations based on the relative time delay of the pulse arrival of the diagonal microphone and the moving object.

[0142] A three-dimensional coordinate system is established using the two sets of hyperboloid equations;

[0143] Determine the planar coordinate position of the microphone and the planar coordinate position of the moving object within the three-dimensional coordinate system;

[0144] Obtain the height information of the moving object, and determine the three-dimensional coordinate position of the moving object based on the height information.

[0145] The positioning module 150 determines the planar coordinate positions of multiple microphones and the three-dimensional coordinate positions of the moving object;

[0146] Calculate the horizontal and vertical distances between the moving object and each microphone;

[0147] The moving object is located based on the horizontal and vertical distances.

[0148] This invention provides a long-range acoustic positioning system based on a moving object. The moving object generates acoustic pulse signals through a pseudo-random noise model modulation and transmits them outward. A microphone array positioned on the ground receives the acoustic pulse signals, performs Doppler distortion compensation, improves the signal-to-noise ratio gain of the matched filter, and selects obvious correlation peaks. By establishing a three-dimensional coordinate system, the coordinate positions of the moving object and the microphones are accurately determined, achieving accurate positioning of the moving object.

[0149] Figure 8 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 8 As shown, the electronic device may include a processor 810, a communication interface 820, a memory 830, and a communication bus 840. The processor 810, communication interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a long-range acoustic localization method based on moving objects. This method includes: generating an acoustic pulse signal by modulating a preset pseudo-random noise model and transmitting it outwards.

[0150] The acoustic pulse signal is received by a matched filter in a preset microphone, and Doppler distortion compensation is performed to detect obvious correlation peaks.

[0151] The position of the relevant peak is used as the time when the acoustic pulse signal arrives at the microphone, and the relative pulse arrival delay of the diagonal microphone is calculated;

[0152] Based on the relative arrival time delay of the pulses from the diagonal microphone, a three-dimensional coordinate system is established using the moving object, and the coordinate positions of the microphone and the moving object in the three-dimensional coordinate system are determined.

[0153] The moving object is located based on the coordinates of the microphone and the moving object in the three-dimensional coordinate system.

[0154] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0155] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program, the computer program being able to be stored on a non-transitory computer-readable storage medium, the computer program being executed by a processor, the computer being able to execute a long-range acoustic localization method based on moving objects provided by the above methods, the method including: generating an acoustic pulse signal by modulation through a preset pseudo-random noise model and sending it outward;

[0156] The acoustic pulse signal is received by a matched filter in a preset microphone, and Doppler distortion compensation is performed to detect obvious correlation peaks.

[0157] The position of the relevant peak is used as the time when the acoustic pulse signal arrives at the microphone, and the relative pulse arrival delay of the diagonal microphone is calculated;

[0158] Based on the relative arrival time delay of the pulses from the diagonal microphone, a three-dimensional coordinate system is established using the moving object, and the coordinate positions of the microphone and the moving object in the three-dimensional coordinate system are determined.

[0159] The moving object is located based on the coordinates of the microphone and the moving object in the three-dimensional coordinate system.

[0160] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a long-range acoustic localization method based on a moving object provided by the methods described above, the method comprising: generating an acoustic pulse signal by modulation through a preset pseudo-random noise model and transmitting it outward;

[0161] The acoustic pulse signal is received by a matched filter in a preset microphone, and Doppler distortion compensation is performed to detect obvious correlation peaks.

[0162] The position of the relevant peak is used as the time when the acoustic pulse signal arrives at the microphone, and the relative pulse arrival delay of the diagonal microphone is calculated;

[0163] Based on the relative arrival time delay of the pulses from the diagonal microphone, a three-dimensional coordinate system is established using the moving object, and the coordinate positions of the microphone and the moving object in the three-dimensional coordinate system are determined.

[0164] The moving object is located based on the coordinates of the microphone and the moving object in the three-dimensional coordinate system.

[0165] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0166] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for long-range acoustic positioning of a moving object, characterized in that, include: An acoustic pulse signal is generated by modulation using a preset pseudo-random noise model and then transmitted outwards. Specifically, generating the acoustic pulse signal by modulation using the preset pseudo-random noise model includes: acquiring the identification code of a moving object; setting a random seed using the identification code of the moving object to generate multiple consecutive Gaussian random variables; generating an acoustic pulse signal based on the Gaussian random variables and transmitting it outwards. The acoustic pulse signal is received by a matched filter in a preset microphone and Doppler distortion compensation is performed. The Doppler distortion compensation is used to allow the length of the acoustic pulse signal to be increased to improve the signal-to-noise ratio gain of the matched filter, and a significant correlation peak is detected based on the improved signal-to-noise ratio gain. The position of the relevant peak is used as the time when the acoustic pulse signal arrives at the microphone, and the relative pulse arrival delay of the diagonal microphone is calculated; Based on the relative arrival time delay of the pulses from the diagonal microphone, a three-dimensional coordinate system is established using the moving object, and the coordinate positions of the microphone and the moving object in the three-dimensional coordinate system are determined. The moving object is located based on the coordinates of the microphone and the moving object in the three-dimensional coordinate system.

2. The method of claim 1, wherein, The process of receiving the acoustic pulse signal through a matched filter within a preset microphone, performing Doppler distortion compensation, and detecting a significant correlation peak specifically includes: Acoustic pulse signals are received through the matched filter; The signal-to-noise ratio gain of the matched filter is improved by compensating for Doppler distortion in the acoustic pulse signal. Based on the improved signal-to-noise ratio gain, a clearly correlated peak was selected.

3. The method of claim 1, wherein, The position of the relevant peak is used as the time when the acoustic pulse signal arrives at the microphone, and the relative arrival time delay of the pulse at the diagonal microphone is calculated, specifically including: The position of the relevant peak is obtained, and the position of the relevant peak is used as the time when the acoustic pulse signal arrives at the microphone; Diagonal microphones have the largest microphone interval. By grouping diagonal microphones together, the relative arrival time delay of pulses from diagonal microphones can be calculated.

4. The method of claim 1, wherein, Based on the relative arrival time delay of the pulses from the diagonal microphone, a three-dimensional coordinate system is established using a moving object. The coordinate positions of the microphone and the moving object in the three-dimensional coordinate system are then determined, specifically including: Based on the relative time delay of the pulse arrival from the diagonal microphone, two sets of hyperboloid equations are established using a moving object; A three-dimensional coordinate system is established using the two sets of hyperboloid equations; Determine the planar coordinate position of the microphone and the planar coordinate position of the moving object within the three-dimensional coordinate system; Obtain the height information of the moving object, and determine the three-dimensional coordinate position of the moving object based on the height information.

5. The method of claim 1, wherein, Based on the coordinate positions of the microphone and the moving object in the three-dimensional coordinate system, the moving object is located, specifically including: Determine the planar coordinate positions of multiple microphones and the three-dimensional coordinate positions of the moving object; Calculate the horizontal and vertical distances between the moving object and each microphone; The moving object is located based on the horizontal and vertical distances.

6. A long-range acoustic positioning system based on moving objects, characterized by The system includes: The pulse signal generation module is used to generate an acoustic pulse signal by modulation using a preset pseudo-random noise model and then transmit it outward. Specifically, the process of generating and transmitting the acoustic pulse signal by modulation using the preset pseudo-random noise model includes: acquiring the identification code of a moving object; setting a random seed using the identification code of the moving object to generate multiple consecutive Gaussian random variables; generating an acoustic pulse signal based on the Gaussian random variables and transmitting it outward. The pulse signal receiving module is used to receive the acoustic pulse signal through a matched filter in a preset microphone and perform Doppler distortion compensation. The Doppler distortion compensation is used to allow the length of the acoustic pulse signal to be increased to improve the signal-to-noise ratio gain of the matched filter, and to detect obvious correlation peaks based on the improved signal-to-noise ratio gain. The delay calculation module is used to take the position of the relevant peak as the time when the acoustic pulse signal arrives at the microphone, and to calculate the relative delay of the pulse arrival at the diagonal microphone; The coordinate determination module, based on the relative time delay of the pulse arrival of the diagonal microphone, establishes a three-dimensional coordinate system through the moving object, and determines the coordinate positions of the microphone and the moving object in the three-dimensional coordinate system; The positioning module locates the moving object based on the coordinate positions of the microphone and the moving object in a three-dimensional coordinate system.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the long-distance acoustic localization method based on moving objects as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by the processor, it implements the long-distance acoustic localization method based on moving objects as described in any one of claims 1 to 5.

9. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the long-distance acoustic localization method based on moving objects as described in any one of claims 1 to 5.

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