A method for calculating a target signal received by a sensor in a motion condition

Abstract

The application discloses a kind of computing method of sensor receiving target signal under motion condition, belong to signal processing field.First, the target signal sequence, signal sampling rate, signal propagation speed and the motion trajectory of target and sensor are obtained;Second, the motion trajectory of target and sensor is interpolated according to signal sampling time;Third, the first sampling time when sensor starts receiving target signal is searched;Then, the target signal time corresponding to the signal received by sensor at each receiving time and signal propagation distance are calculated and stored in the corresponding relationship set;Finally, according to the corresponding relationship set, the target signal is interpolated and amplitude adjusted to obtain the target signal received by sensor under motion condition.The method solves the problem of existing sensor signal receiving calculation method under motion condition in the accuracy of space-time matching, signal reconstruction accuracy, and is suitable for radar, sonar and other types of sensing scene.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and in particular to a method for calculating the target signal received by a sensor under motion conditions. Background Technology

[0002] In technologies such as radar detection, sonar positioning, and intelligent sensing, the relative motion between sensors and targets is a ubiquitous core scenario. When a target radiates signals (such as acoustic or electromagnetic signals) to its surroundings, the signal undergoes two key physical effects during its propagation to the sensor due to the real-time dynamic changes in the spatial positions of the target and sensor: first, dynamic fluctuations in propagation delay, meaning the time it takes for the signal to travel from the target to the sensor adjusts in real time as the relative distance between them changes; and second, signal amplitude attenuation, meaning the signal energy follows the laws of wave propagation, with amplitude attenuation becoming more significant over greater distances. These two effects result in a difference between the sensor-received signal and the original radiated signal from the target. Accurate sensor signal reception is a fundamental prerequisite for subsequent core tasks such as target identification, parameter estimation, and trajectory tracking.

[0003] In recent years, with the deep integration of dynamic signal processing technology and computer simulation technology, sensor signal receiving calculation methods based on motion trajectory modeling have gradually become a research focus in the field of target detection. Researchers construct parameterized signal receiving calculation models by analyzing the motion patterns of targets and sensors, and the characteristics of signal propagation media (such as the speed of sound and electromagnetic waves in air and water). The simulated signals generated by these models have been widely used in system performance testing, target recognition algorithm training, and signal feature analysis in complex environments, demonstrating significant application value in fields such as intelligent transportation, military defense, environmental monitoring, and smart homes.

[0004] However, existing methods for calculating sensor signals under motion conditions still have significant limitations. For example, the patented method and device for measuring moving objects based on a 24G millimeter-wave radar module (publication number: CN118642093A) does not adequately consider the precise matching of signal sampling time and target spatial position when processing moving target signals. It uses a method that directly corresponds discrete trajectory points to sampling times without fitting continuous motion trajectories using smooth interpolation techniques, which may lead to distance calculation errors. Furthermore, its handling of signal propagation delay is not fully integrated with real-time adjustments based on dynamic trajectories. In high-speed motion or complex trajectory scenarios, delay calculation deviations can easily affect the accuracy of velocity and state estimation. In addition, some methods use simple interpolation to restore signals at non-integer moments, making it difficult to accurately preserve the signal's frequency domain characteristics, resulting in received signal distortion. These problems restrict the improvement of sensing system accuracy and make it difficult to meet the requirements of high-precision detection. Summary of the Invention

[0005] This invention provides a method for calculating the target signal received by a sensor under motion conditions, in order to solve the problems of deficiencies in the spatiotemporal matching accuracy and signal reconstruction accuracy of existing methods for calculating the sensor signal received under motion conditions.

[0006] This invention provides a method for calculating target signals received by a sensor under motion conditions, comprising the following steps:

[0007] Step 1: Acquire the target signal sequence, target signal sampling rate, target signal propagation speed, and the motion trajectories of the target and the sensor;

[0008] Step 2: Interpolate the motion trajectories of the target and the sensor according to the sampling time of the target signal;

[0009] Step 3: Search for the first sampling moment when the sensor starts receiving the target signal, calculate the target signal time and the target signal propagation distance corresponding to the signal received by the sensor at each receiving moment, and store them in the corresponding relationship set;

[0010] Step 4: Based on the set of correspondences, interpolate and adjust the amplitude using the target signal to obtain the target signal received by the sensor under motion conditions.

[0011] Optionally, in one embodiment of the present invention, step 1 specifically includes:

[0012] Acquire the target signal sequence emitted by the target s ( k ),in, k =0,1,2,…,K-1, where K is the total number of sampling points in the target signal sequence;

[0013] Obtain the target signal sampling rate f s and the target signal propagation speed c;

[0014] Obtain the three-dimensional coordinate sequence of the target's motion trajectory within the corresponding time period of the target signal sequence. x s ( t i ), y s ( t i ), z s ( t i )],in, t i relative target signal s (0) The offset time of the acquisition time. i =0,1,2,…, I -1,I The number of discrete coordinate points of the target's motion trajectory. , x s ( t i ) for the target at offset time t i x-axis coordinates at time y s ( t i ) for the target at offset time t i y-axis coordinate at time z s ( t i ) for the target at offset time t i z-axis coordinate at time;

[0015] Obtain the three-dimensional coordinate sequence of the sensor's motion trajectory within the corresponding time period of the target signal sequence. x r ( t j ), y r ( t j ), z r ( t j )],in, t j relative target signal s (0) The offset time of the acquisition time. j =0,1,2,…, J -1, J The number of discrete coordinate points of the sensor's motion trajectory. , x r ( t j ) represents the sensor at offset time t j x-coordinate at time y r ( t j ) represents the sensor at offset time t j y-axis coordinate at time z r ( t j ) represents the sensor at offset time t j The z-axis coordinate at that time.

[0016] Optionally, in one embodiment of the present invention, step 2 specifically includes:

[0017] For the target motion trajectory, the offset time t i Converted to sampling sequence number The conversion method is as follows: The three-dimensional coordinate sequence of the target's motion trajectory [ x s ( t i ), y s ( t i ), z s ( t i Convert )] to ,in, The target is the sampled number after conversion. The x-axis coordinate at the corresponding time. The target is the sampled number after conversion. The y-coordinate at the corresponding moment. The target is the sampled number after conversion. The z-axis coordinate at the corresponding time; using cubic spline interpolation, , and Interpolation is performed according to the sampling time of the target signal to obtain... x s ( k ), y s ( k )and z s ( k After interpolation, the three-dimensional coordinate sequence of the target motion trajectory is obtained. x s ( k ), y s ( k ), z s ( k )],in, x s ( k ) as the goal in the k The x-axis coordinates at each sampling time. y s ( k ) as the goal in the k The y-axis coordinate at each sampling time. z s (k ) as the goal in the k The z-axis coordinate at each sampling time;

[0018] Referring to the transformation and interpolation process of the target motion trajectory, the three-dimensional coordinate sequence of the sensor motion trajectory is [ x r ( t j ), y r ( t j ), z r ( t j The transformation and interpolation are performed to obtain the three-dimensional coordinate sequence of the sensor's motion trajectory. x r ( k ), y r ( k ), z r ( k )], x r ( k () represents the x-axis coordinate of the sensor at the i-th sampling time. y r ( k () represents the y-coordinate of the sensor at the i-th sampling time. z r ( k () represents the z-axis coordinate of the sensor at the first sampling time.

[0019] Optionally, in one embodiment of the present invention, step 3, the first sampling moment when the search sensor begins to receive the target signal, specifically includes:

[0020] Definition of the first m The time delay relationship between the target signal received by the sensor at each sampling time and the target signal at the 0th sampling time. t ( m The calculation formula is: ,0).

[0021] ;

[0022] in, x r ( m ) for the sensor in the first m The x-axis coordinates at each sampling time. x s (0) represents the x-axis coordinate of the target at the 0th sampling time. y r ( m() represents the y-coordinate of the sensor at the i-th sampling time. y s (0) represents the y-coordinate of the target at the 0th sampling time. z r ( m Let be the z-axis coordinate of the sensor at the i-th sampling time. z s (0) represents the z-axis coordinate of the target at the 0th sampling time. f s Where c is the target signal sampling rate, and c is the target signal propagation speed;

[0023] make m Starting from 1 and incrementing, calculate... t ( m ,0) until satisfied t ( m -1,0)<0≤ t ( m ,0), record the current value. m for m 0, m 0 represents the sequence number of the first sampling moment when the sensor begins to receive the target signal.

[0024] Optionally, in one embodiment of the present invention, in step 3, the target signal time and the target signal propagation distance corresponding to the signals received by the sensor at each receiving time are calculated and stored in the corresponding relationship set, specifically including:

[0025] Calculate the sensor in sequence n = m 0, m 0+1, m 0+2,…, K The position at time -1, and the time when the target emitted the signal corresponding to the signal received by the sensor at each time, where, K The total number of sampling points for the target signal sequence is determined using the following method:

[0026] Step 31: Create an empty set as the initial state of the correspondence set A;

[0027] Step 32, let in n = m 0;

[0028] Step 33, calculate the first... n The position of the sensor at the sampling time and the first n Distance of the target's location at time 0 ,in, x r ( n ) for the sensor in the first nThe x-axis coordinates at each sampling time. y r ( n ) for the sensor in the first n The y-axis coordinate at each sampling time. z r ( n ) for the sensor in the first n The z-axis coordinate at each sampling time. x s ( n 0) is the target in the first n x-axis coordinates at 0 sampling times y s ( n 0) is the target in the first n y-axis coordinates at 0 sampling times z s ( n 0) is the target in the first n The z-axis coordinate at time 0;

[0029] Step 35, calculate the time delay relationship ;

[0030] Step 36, if t ( n , n 0)=0, construct vector [ n,n 0,r( n,n 0)] is added as an element to the corresponding relation set A, proceed to step 37; if t ( n , n If 0) > 0, then it will continuously increase. n 0. Repeat steps 34 and 35 until the condition is met. t ( n , n 0)≤0< t ( n , n 0-1), construct vector Add it as an element to the corresponding relation set A, then proceed to step 37; if t ( n , n If 0) < 0, then it will continuously decrease. n 0. Repeat steps 34 and 35 until the condition is met. t ( n , n 0+1)<0≤ t ( n , n 0), construct vector Add it as an element to the corresponding relation set A, and proceed to step 37;

[0031] Step 37, when n < K When -1, let n = n +1, jump to step 33 and execute sequentially downwards; when n ≥ K When -1 is reached, the step ends.

[0032] Optionally, in one embodiment of the present invention, step 4 specifically includes:

[0033] Step 41, Initialize sensor receiving signal r ( k )=0, k =0,1,2,…,K-1;

[0034] Step 42: Sequentially retrieve each element from the corresponding relationship set A and update the sensor received signal. r ( k The value of ) is determined by the following method: let a certain element taken from the correspondence set A be [ a 1, a 2, a 3], let b =round( a 2) `round` is a function that rounds the data to the nearest integer, using the target signal sequence. s ( k ) Calculate the sensor received signal r ( a 1) The formula is as follows:

[0035] ;

[0036] in, , M Let A be a positive integer, max be the function to find the maximum value, and min be the function to find the minimum value. This is done when all elements in the correspondence set A have been retrieved and used to update the sensor's received signal. r ( k After that, the target signal received by the sensor under motion conditions is obtained.

[0037] The method for calculating target signals received by a sensor under motion conditions according to an embodiment of the present invention has the following beneficial effects:

[0038] (1) High matching accuracy: Cubic spline interpolation is used to fit the motion trajectory, ensuring that the second derivative of the trajectory is continuous and passes through the original discrete points. The error of "sampling time-spatial position" is small, which is better than traditional linear interpolation, providing an accurate data basis for the calculation of propagation distance and time delay.

[0039] (2) High signal reconstruction fidelity: The first reception time is located by quantizing the time delay relationship to avoid interference from invalid time; combined with function interpolation and dynamic amplitude adjustment of propagation distance, the reconstructed signal has a high similarity to the actual received signal and conforms to the physical laws of wave propagation.

[0040] (3) Strong scene adaptability: It supports various motion types such as uniform speed and variable speed, and can be adapted to different propagation media such as air and water (only the propagation speed needs to be adjusted). It is suitable for various sensing scenarios such as radar and sonar, and does not require modification of the core logic due to scene changes.

[0041] (4) Highly practical: The steps are clear and the parameters are quantifiable. The generated received signal can be directly used for testing, reducing the actual test cost.

[0042] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0043] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0044] Figure 1 A flowchart illustrating a method for calculating a target signal received by a sensor under motion conditions according to an embodiment of the present invention;

[0045] Figure 2 This is a time-domain waveform diagram of the target original signal in an embodiment of the present invention;

[0046] Figure 3 This is a diagram showing the original motion trajectory of the target and the sensor in an embodiment of the present invention;

[0047] Figure 4 This is an interpolated motion trajectory diagram of the target and the sensor in an embodiment of the present invention;

[0048] Figure 5 This is a time-domain waveform diagram of the final received signal from the sensor in an embodiment of the present invention. Detailed Implementation

[0049] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0050] In practical applications such as radar detection and sonar positioning, sensors and targets are often in dynamic relative motion, such as mobile radar tracking flying targets or drones carrying microphones receiving ground sound signals. In these situations, the signal radiates from the target to the sensor, and the real-time changes in their spatial positions cause dynamic fluctuations in propagation delay and signal amplitude attenuation. Existing technologies often have two limitations when dealing with signal reception under such moving conditions: first, directly using discretely sampled motion trajectory data leads to an inaccurate match between the signal sampling time and the spatial positions of the target and sensor; second, simplifying the signal propagation model ignores the impact of trajectory continuity on delay calculation, ultimately resulting in a significant deviation between the reconstructed sensor received signal and the actual physical scene, making it difficult to meet the requirements of high-precision detection and identification. Therefore, this invention, based on trajectory smoothing technology using cubic spline interpolation and physical propagation law modeling, constructs a method for calculating the sensor's reception of target signals under moving conditions. This method can accurately reproduce the true characteristics of the sensor's received signal in dynamic scenes, providing data support for subsequent signal analysis and algorithm verification.

[0051] Signal propagation under motion conditions exhibits significant dynamic characteristics, primarily manifested in two aspects: First, the propagation distance continuously changes with the relative motion between the target and the sensor. According to wave propagation laws, the signal amplitude attenuates with distance, and the degree of attenuation adjusts in real time according to the motion state. Second, the propagation delay, i.e., the time it takes for the signal to travel from the target to the sensor, dynamically changes with distance, resulting in a non-linear correlation between the sensor's reception time and the target's signal emission time. Practical analysis reveals that if the continuity of the trajectory is ignored and discrete trajectories are used directly for calculation, the propagation distance error is significant, leading to a large deviation between the amplitude and timing of the received signal, severely impacting the accuracy of subsequent target identification.

[0052] The computational method of this invention addresses two core issues in signal reception during motion scenarios: First, it achieves continuous fitting of discrete trajectories through cubic spline interpolation, ensuring that each signal sampling moment accurately corresponds to the spatial coordinates of the target and the sensor, eliminating computational errors caused by spatiotemporal mismatch. Second, based on the interpolated continuous trajectory, it constructs a quantized time-delay relationship model to accurately match the sensor reception moment with the target signal emission moment, and dynamically adjusts the signal amplitude in conjunction with the propagation distance, ensuring that the reconstructed received signal conforms to the actual physical propagation laws. Simultaneously, the sinc interpolation technique introduced in the method effectively restores the target signal values ​​at non-integer moments, avoiding signal distortion caused by time asynchrony. This modeling approach ensures both the spatiotemporal matching accuracy of the trajectory and signal and the physical realism of signal propagation, making it widely adaptable to various motion scenarios such as uniform speed, acceleration, and direction change.

[0053] Figure 1This is a flowchart illustrating a method for calculating a target signal received by a sensor under motion conditions, according to an embodiment of the present invention.

[0054] like Figure 1 As shown, the method for calculating the target signal received by the sensor under this motion condition includes the following steps:

[0055] Step 1: Acquire the target signal sequence, target signal sampling rate, target signal propagation speed, and the motion trajectories of the target and the sensor;

[0056] Step 2: Interpolate the motion trajectories of the target and the sensor according to the sampling time of the target signal;

[0057] Step 3: Search for the first sampling moment when the sensor starts receiving the target signal, calculate the target signal time and the target signal propagation distance corresponding to the signal received by the sensor at each receiving moment, and store them in the corresponding relationship set;

[0058] Step 4: Based on the set of correspondences, interpolate and adjust the amplitude using the target signal to obtain the target signal received by the sensor under motion conditions.

[0059] In an embodiment of the present invention, step 1 specifically includes:

[0060] Acquire the target signal sequence emitted by the target s ( k ),in, k =0,1,2,…,K-1, where K is the total number of sampling points in the target signal sequence;

[0061] Obtain the target signal sampling rate f s and the target signal propagation speed c;

[0062] Obtain the three-dimensional coordinate sequence of the target's motion trajectory within the corresponding time period of the target signal sequence. x s ( t i ), y s ( t i ), z s ( t i )],in, t i relative target signal s (0) The offset time of the acquisition time. i =0,1,2,…, I -1, I The number of discrete coordinate points of the target's motion trajectory. ,x s ( t i ) for the target at offset time t i x-axis coordinates at time y s ( t i ) for the target at offset time t i y-axis coordinate at time z s ( t i ) for the target at offset time t i z-axis coordinate at time;

[0063] Obtain the three-dimensional coordinate sequence of the sensor's motion trajectory within the corresponding time period of the target signal sequence. x r ( t j ), y r ( t j ), z r ( t j )],in, t j relative target signal s (0) The offset time of the acquisition time. j =0,1,2,…, J -1, J The number of discrete coordinate points of the sensor's motion trajectory. , x r ( t j ) represents the sensor at offset time t j x-coordinate at time y r ( t j ) represents the sensor at offset time t j y-axis coordinate at time z r ( t j ) represents the sensor at offset time t j The z-axis coordinate at that time.

[0064] In an embodiment of the present invention, step 2 specifically includes:

[0065] For the target motion trajectory, the offset time t i Converted to sampling sequence number The conversion method is as follows: The three-dimensional coordinate sequence of the target's motion trajectory [ x s ( t i ), y s ( t i ), z s ( t i Convert )] to ,in, The target is the sampled number after conversion. The x-axis coordinate at the corresponding time. The target is the sampled number after conversion. The y-coordinate at the corresponding moment. The target is the sampled number after conversion. The z-axis coordinate at the corresponding time; using cubic spline interpolation, , and Interpolation is performed according to the sampling time of the target signal to obtain... x s ( k ), y s ( k )and z s ( k After interpolation, the three-dimensional coordinate sequence of the target motion trajectory is obtained. x s ( k ), y s ( k ), z s ( k )],in, x s ( k ) as the goal in the k The x-axis coordinates at each sampling time. y s ( k ) as the goal in the k The y-axis coordinate at each sampling time. z s ( k ) as the goal in the k The z-axis coordinate at each sampling time;

[0066] Referring to the transformation and interpolation process of the target motion trajectory, the three-dimensional coordinate sequence of the sensor motion trajectory is [ x r ( t j ), y r ( t j ), z r ( t j The transformation and interpolation are performed to obtain the three-dimensional coordinate sequence of the sensor's motion trajectory. x r ( k ), y r ( k ), z r ( k )], x r ( k () represents the x-axis coordinate of the sensor at the i-th sampling time. y r ( k () represents the y-coordinate of the sensor at the i-th sampling time. z r ( k () represents the z-axis coordinate of the sensor at the first sampling time.

[0067] like Figure 2 The figure shows the time-domain waveform of the original signal of the target, which illustrates the original signal sequence s(k) of the target. Figure 3 This is the original motion trajectory diagram of the target and sensor, composed of discrete coordinate points. Combined with the cubic spline interpolation processing in step 2, Figure 4 The interpolated motion trajectory graph shows a continuous and smooth curve.

[0068] In an embodiment of the present invention, step 3, the first sampling moment at which the search sensor begins to receive the target signal, specifically includes:

[0069] Definition of the first m The time delay relationship between the target signal received by the sensor at each sampling time and the target signal at the 0th sampling time. t ( m The calculation formula is: ,0).

[0070] ;

[0071] in, x r ( m) for the sensor in the first m The x-axis coordinates at each sampling time. x s (0) represents the x-axis coordinate of the target at the 0th sampling time. y r ( m () represents the y-coordinate of the sensor at the i-th sampling time. y s (0) represents the y-coordinate of the target at the 0th sampling time. z r ( m Let be the z-axis coordinate of the sensor at the i-th sampling time. z s (0) represents the z-axis coordinate of the target at the 0th sampling time. f s Where c is the target signal sampling rate, and c is the target signal propagation speed;

[0072] make m Starting from 1 and incrementing, calculate... t ( m ,0) until satisfied t ( m -1,0)<0≤ t ( m ,0), record the current value. m for m 0, m 0 represents the sequence number of the first sampling moment when the sensor begins to receive the target signal.

[0073] In an embodiment of the present invention, step 3 involves calculating the target signal time and the target signal propagation distance corresponding to the signals received by the sensor at each receiving time, and storing them in a corresponding relationship set. Specifically, this includes:

[0074] Calculate the sensor in sequence n = m 0, m 0+1, m 0+2,…, K The position at time -1, and the time when the target emitted the signal corresponding to the signal received by the sensor at each time, where, K The total number of sampling points for the target signal sequence is determined using the following method:

[0075] Step 31: Create an empty set as the initial state of the correspondence set A;

[0076] Step 32, let in n = m 0;

[0077] Step 33, calculate the first... nThe position of the sensor at the sampling time and the first n Distance of the target's location at time 0 ,in, x r ( n ) for the sensor in the first n The x-axis coordinates at each sampling time. y r ( n ) for the sensor in the first n The y-axis coordinate at each sampling time. z r ( n ) for the sensor in the first n The z-axis coordinate at each sampling time. x s ( n 0) is the target in the first n x-axis coordinates at 0 sampling times y s ( n 0) is the target in the first n y-axis coordinates at 0 sampling times z s ( n 0) is the target in the first n The z-axis coordinate at time 0;

[0078] Step 35, calculate the time delay relationship ;

[0079] Step 36, if t ( n , n 0)=0, construct vector [ n,n 0,r( n,n 0)] is added as an element to the corresponding relation set A, proceed to step 37; if t ( n , n If 0) > 0, then it will continuously increase. n 0. Repeat steps 34 and 35 until the condition is met. t ( n , n 0)≤0< t ( n , n 0-1), construct vector Add it as an element to the corresponding relation set A, then proceed to step 37; if t ( n , n If 0) < 0, then it will continuously decrease. n 0. Repeat steps 34 and 35 until the condition is met. t (n , n 0+1)<0≤ t ( n , n 0), construct vector Add it as an element to the corresponding relation set A, and proceed to step 37;

[0080] Step 37, when n < K When -1, let n = n +1, jump to step 33 and execute sequentially downwards; when n ≥ K When -1 is reached, the step ends.

[0081] In an embodiment of the present invention, step 4 specifically includes:

[0082] Step 41, Initialize sensor receiving signal r ( k )=0, k =0,1,2,…,K-1;

[0083] Step 42: Sequentially retrieve each element from the corresponding relationship set A and update the sensor received signal. r ( k The value of ) is determined by the following method: let a certain element taken from the correspondence set A be [ a 1, a 2, a 3], let b =round( a 2) `round` is a function that rounds the data to the nearest integer, using the target signal sequence. s ( k ) Calculate the sensor received signal r ( a 1) The formula is as follows:

[0084] ;

[0085] in, , M Let A be a positive integer, max be the function to find the maximum value, and min be the function to find the minimum value. This is done when all elements in the correspondence set A have been retrieved and used to update the sensor's received signal. r ( k After that, the target signal received by the sensor under motion conditions is obtained.

[0086] like Figure 5 This is a waveform diagram of the signal ultimately received by the sensor.

[0087] This invention discloses a method for calculating target signals received by a sensor under motion conditions. First, the method acquires the target signal sequence, signal sampling rate, signal propagation speed, and the motion trajectories of the target and sensor. Second, it interpolates the motion trajectories of the target and sensor according to the signal sampling time. Third, it searches for the first sampling time at which the sensor begins to receive the target signal. Then, it calculates the target signal time and signal propagation distance corresponding to the received signal at each receiving time of the sensor and stores them in a correspondence set. Finally, based on the correspondence set, it uses the target signal for interpolation and amplitude adjustment to obtain the target signal received by the sensor under motion conditions. This method is applicable to various sensing scenarios such as radar and sonar, and does not require modification of the core logic due to changes in the scenario.

[0088] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0089] Furthermore, 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 indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0090] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

Claims

1. A method for calculating the target signal received by a sensor under motion conditions, characterized in that, Includes the following steps: Step 1: Acquire the target signal sequence, target signal sampling rate, target signal propagation speed, and the motion trajectories of the target and the sensor; Step 2: Interpolate the motion trajectories of the target and the sensor according to the sampling time of the target signal; Step 3: Search for the first sampling moment when the sensor starts receiving the target signal, calculate the target signal time and the target signal propagation distance corresponding to the signal received by the sensor at each receiving moment, and store them in the corresponding relationship set; Step 4: Based on the set of correspondences, interpolate and adjust the amplitude using the target signal to obtain the target signal received by the sensor under motion conditions; Step 2 specifically includes: For the target motion trajectory, the offset time t i Converted to sampling sequence number The conversion method is as follows: , f s The target signal sampling rate is used to obtain the three-dimensional coordinate sequence of the target motion trajectory. x s ( t i ), y s ( t i ), z s ( t i Convert )] to ,in, x s ( t i ) for the target at offset time t i x-axis coordinates at time y s ( t i ) for the target at offset time t i y-coordinate at time z s ( t i ) for the target at offset time t i z-axis coordinate at time The target is the sampled number after conversion. The x-axis coordinate at the corresponding time. The target is the sampled number after conversion. The y-coordinate at the corresponding moment. The target is the sampled number after conversion. The z-axis coordinate at the corresponding time; using cubic spline interpolation, , and Interpolation is performed according to the sampling time of the target signal to obtain... x s ( k ), y s ( k )and z s ( k After interpolation, the three-dimensional coordinate sequence of the target motion trajectory is obtained. x s ( k ), y s ( k ), z s ( k )],in, x s ( k ) as the goal in the k The x-axis coordinates at each sampling time. y s ( k ) as the goal in the k The y-axis coordinate at each sampling time. z s ( k ) as the goal in the k The z-axis coordinate at each sampling time; Referring to the transformation and interpolation process of the target motion trajectory, the three-dimensional coordinate sequence of the sensor motion trajectory is [ x r ( t j ), y r ( t j ), z r ( t j The transformation and interpolation are performed to obtain the three-dimensional coordinate sequence of the sensor's motion trajectory. x r ( k ), y r ( k ), z r ( k )],in, x r ( t j ) represents the sensor at offset time t j x-coordinate at time y r ( t j ) represents the sensor at offset time t j y-coordinate at time z r ( t j ) represents the sensor at offset time t j z-axis coordinate at time x r ( k () represents the x-axis coordinate of the sensor at the i-th sampling time. y r ( k () represents the y-coordinate of the sensor at the i-th sampling time. z r ( k () represents the z-axis coordinate of the sensor at the i-th sampling time; In step 3, the target signal time and the target signal propagation distance corresponding to the signal received by the sensor at each receiving time are calculated and stored in the corresponding relationship set, specifically including: Calculate the sensor in sequence n = m 0, m 0+1, m 0+2,…, K The position at time -1, and the time when the target emitted the signal corresponding to the signal received by the sensor at each time, where, K The total number of sampling points for the target signal sequence is determined using the following method: Step 31: Create an empty set as the initial state of the correspondence set A; Step 32, let in n = m 0; Step 33, calculate the first... n The position of the sensor at the sampling time and the first n Distance of the target's location at time 0 ,in, x r ( n ) for the sensor in the first n The x-axis coordinates at each sampling time. y r ( n ) for the sensor in the first n The y-axis coordinate at each sampling time. z r ( n ) for the sensor in the first n The z-axis coordinate at each sampling time. x s ( n 0) is the target in the first n x-axis coordinates at 0 sampling times y s ( n 0) is the target in the first n y-axis coordinates at 0 sampling times z s ( n 0) is the target in the first n The z-axis coordinate at time 0; Step 35, calculate the time delay relationship Where c is the propagation speed of the target signal; Step 36, if τ ( n , n 0)=0, construct vector [ n,n 0,r( n,n 0)] is added as an element to the corresponding relation set A, proceed to step 37; if τ ( n , n If 0) > 0, then it will continuously increase. n 0. Repeat steps 34 and 35 until the condition is met. τ ( n , n 0)≤0< τ ( n , n 0-1), construct vector Add it as an element to the corresponding relation set A, then proceed to step 37; if τ ( n , n If 0) < 0, then it will continuously decrease. n 0. Repeat steps 34 and 35 until the condition is met. τ ( n , n 0+1)<0≤ τ ( n , n 0), construct vector Add it as an element to the corresponding relation set A, and proceed to step 37; Step 37, when n < K When -1, let n = n +1, jump to step 33 and execute sequentially downwards; when n ≥ K When -1 is reached, the step ends; Step 4 specifically includes: Step 41, Initialize sensor receiving signal r ( k )=0, k =0,1,2,…,K-1; Step 42: Sequentially retrieve each element from the corresponding relationship set A and update the sensor received signal. r ( k The value of ) is determined by the following method: let a certain element taken from the correspondence set A be [ a 1, a 2, a 3], let b =round( a 2) `round` is a function that rounds the data to the nearest integer, using the target signal sequence. s ( k ) Calculate the sensor received signal r ( a 1) The formula is as follows: ； in, , M Let A be a positive integer, max be the function to find the maximum value, and min be the function to find the minimum value. This is done when all elements in the correspondence set A have been retrieved and used to update the sensor's received signal. r ( k After that, the target signal received by the sensor under motion conditions is obtained.

2. The method according to claim 1, characterized in that, Step 1 specifically includes: Acquire the target signal sequence emitted by the target s ( k ),in, k =0,1,2,…,K-1; Obtain the target signal sampling rate f s and the target signal propagation speed c; Obtain the three-dimensional coordinate sequence of the target's motion trajectory within the corresponding time period of the target signal sequence. x s ( t i ), y s ( t i ), z s ( t i )],in, t i relative target signal s (0) The offset time of the acquisition moment. i =0,1,2,…, I -1, I The number of discrete coordinate points of the target's motion trajectory. ; Obtain the three-dimensional coordinate sequence of the sensor's motion trajectory within the corresponding time period of the target signal sequence. x r ( t j ), y r ( t j ), z r ( t j )],in, t j relative target signal s (0) The offset time of the acquisition moment. j =0,1,2,…, J -1, J The number of discrete coordinate points of the sensor's motion trajectory. .

3. The method according to claim 1, characterized in that, In step 3, the search sensor begins to receive the first sampling moment of the target signal, specifically including: Definition of the first m The time delay relationship between the target signal received by the sensor at each sampling time and the target signal at the 0th sampling time. τ ( m The calculation formula is: ,0). ； in, x r ( m ) for the sensor in the first m The x-axis coordinates at each sampling time. x s (0) represents the x-axis coordinate of the target at the 0th sampling time. y r ( m () represents the y-coordinate of the sensor at the i-th sampling time. y s (0) represents the y-coordinate of the target at the 0th sampling time. z r ( m Let be the z-axis coordinate of the sensor at the i-th sampling time. z s (0) represents the z-axis coordinate of the target at the 0th sampling time; make m Starting from 1 and incrementing, calculate... τ ( m ,0) until satisfied τ ( m -1,0)<0≤ τ ( m ,0), record the current value. m for m 0, m 0 represents the sequence number of the first sampling moment when the sensor begins to receive the target signal.

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