Target detection method and device in multipath environment, equipment and storage medium

Abstract

The invention discloses a target detection method and device in a multipath environment, equipment and a storage medium. The method comprises the following steps: acquiring a first signal of a to-be-detected target; determining a first M / N detector corresponding to the first signal based on the first signal and a preset combination parameter; the combined parameter is determined based on an M parameter and an N parameter in a second M / N detector; determining a first detection probability of a first parameter corresponding to the first M / N detector; determining a target parameter of the first signal by using the first detection probability and the combined parameter; and determining whether the to-be-detected target exists in a preset space based on the target parameter. The parameters of the M / N detector are adaptively adjusted according to the environment, cognitive deviation caused by artificial experience is avoided, and the detection accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of wireless technology, and in particular to a method, device, equipment and storage medium for target detection in a multi-level environment. Background Art

[0002] In related technologies, the purpose of low-altitude detection is to determine whether there is a target from echoes with complex interference. However, due to the multipath effect, multiple phases will be superimposed or offset, and unprocessed echo signals are prone to false detection and misjudgment. Usually, an M / N detector is used for judgment, but the hyperparameter selection of the M / N detector mainly adopts a fixed formula model and empirical parameters. This method strictly relies on manual experience, and it is difficult to adjust the hyperparameters according to the actual situation when there are large changes in the external environment. There is currently no effective solution to this problem. Summary of the invention

[0003] To solve the related technical problems, the embodiments of the present application provide a method, apparatus, device and storage medium for target detection in a multipath environment.

[0004] To achieve the above purpose, the technical solution of the embodiment of the present application is implemented as follows:

[0005] The present invention provides a method for detecting a target in a multipath environment, the method comprising:

[0006] Acquire a first signal of a target to be detected;

[0007] Determine a first M / N detector corresponding to the first signal based on the first signal and a preset combination parameter; the combination parameter is determined based on an M parameter and an N parameter in a second M / N detector;

[0008] determining a first detection probability of a first parameter corresponding to the first M / N detector;

[0009] determining a target parameter of the first signal using the first detection probability and the combined parameter;

[0010] It is determined based on the target parameters whether the target to be detected exists in the preset space.

[0011] In the above solution, after determining the first detection probability of the first parameter corresponding to the first M / N detector, the method further includes:

[0012] Determining whether the value of the first detection probability is greater than or equal to a preset threshold;

[0013] In a case where the value of the first detection probability is less than the preset threshold, determining a target parameter of the first signal using the first detection probability and the combined parameter;

[0014] When the value of the first detection probability is greater than or equal to the preset threshold, the first parameter is used as the target parameter.

[0015] In the above solution, the determining the target parameter of the first signal by using the first detection probability and the combined parameter includes:

[0016] The first detection probability and the combined parameter are input into a preset strategy model to obtain a target parameter of the first signal.

[0017] In the above solution, the step of inputting the first detection probability and the combination parameter into a preset strategy model to obtain the target parameter of the first signal includes:

[0018] In the combined parameters, determining a second parameter adjacent to the first parameter;

[0019] determining a second probability of detection of the second parameter;

[0020] When the second parameter meets the preset condition, the target parameter is obtained.

[0021] In the above solution, the preset condition includes at least one of the following:

[0022] The value of the second detection probability is greater than or equal to a preset threshold;

[0023] The first step number between the first parameter and the second parameter is greater than or equal to a preset step number.

[0024] In the above scheme, the method further includes:

[0025] When the first detection probability and / or the second detection probability is greater than or equal to the preset threshold, a first reward parameter is obtained; the first reward parameter is used to determine whether the first parameter and / or the second parameter is the target parameter.

[0026] In the above scheme, the method further includes:

[0027] In a case where the target to be detected exists in the preset space, position information and speed information corresponding to the target to be detected are determined according to the first signal.

[0028] In the above solution, determining the position information and speed information corresponding to the target to be detected according to the first signal includes:

[0029] Determine a third parameter according to the first signal and the Doppler shift parameter caused by the target to be detected;

[0030] Performing discrete Fourier transform processing on the third parameter to obtain a fourth parameter corresponding to the target to be detected;

[0031] determining the speed information based on the fourth parameter;

[0032] Perform inverse discrete Fourier transform processing on the fourth parameter to obtain the position information.

[0033] The present application also provides a target detection device in a multipath environment, the device comprising:

[0034] A first acquisition unit, used to acquire a first signal of a target to be detected;

[0035] A first determining unit, configured to determine a first M / N detector corresponding to the first signal based on the first signal and a preset combination parameter; the combination parameter is determined based on an M parameter and an N parameter in a second M / N detector;

[0036] a second determining unit, configured to determine a first detection probability of a first parameter corresponding to the first M / N detector;

[0037] a third determining unit, configured to determine a target parameter of the first signal using the first detection probability and the combined parameter;

[0038] The fourth determining unit is used to determine whether the target to be detected exists in a preset space based on the target parameter.

[0039] The embodiment of the present application also provides a target detection device in a multi-level environment, comprising: a processor and a memory for storing a computer program that can be run on the processor;

[0040] Wherein, when the processor is used to run the computer program, it executes any step of the above-mentioned method.

[0041] An embodiment of the present application also provides a storage medium storing a computer program for implementing any step of the above-described method when executed by a processor.

[0042] The embodiment of the present application provides a method for target detection in a multipath environment, the method comprising: acquiring a first signal of a target to be detected; determining a first M / N detector corresponding to the first signal based on the first signal and a preset combination parameter; the combination parameter is determined based on the M parameter and the N parameter in the second M / N detector; determining a first detection probability of the first parameter corresponding to the first M / N detector; determining a target parameter of the first signal using the first detection probability and the combination parameter; determining whether the target to be detected exists in a preset space based on the target parameter. The scheme of the embodiment of the present application determines the target parameter based on the detection probability of the first parameter and the combination parameter, realizes an M / N detector that is adaptively tuned according to the environment, greatly reduces the workload of traditional traversal parameters, and improves the accuracy of establishment. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of the detection process of the M / N detector provided in an embodiment of the present application;

[0044] Figure 2 A method for detecting a target in a multipath environment is provided in an embodiment of the present application;

[0045] Figure 3 A schematic diagram of a target detection simulation result provided in an embodiment of the present application;

[0046] Figure 4 A schematic diagram of a target detection device in a multi-stage environment provided in an embodiment of the present application;

[0047] Figure 5 A schematic diagram of a hardware entity structure of a target detection device in a multipath environment in an embodiment of the present application. DETAILED DESCRIPTION

[0048] The present application is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] The low-altitude environmental factors are complex, and multi-level factors have a greater impact on target detection. In low-altitude scenarios, multipath effect is an important issue in wireless communication. Multipath effect refers to the fact that wireless signals propagate through multiple paths from transmitter to receiver, and the signal arrival time and strength of each path are different, resulting in interference and fading of the received signal. In low-altitude scenarios, wireless signals will be blocked by objects such as the ground and buildings. These obstacles will cause signal fading and multiple reflections, increase the signal propagation path, and increase the impact of multipath effect. Weather conditions in low-altitude scenarios may have an impact on multipath effect. For example, weather conditions such as rain, snow, and fog can cause signals to refract, reflect, and scatter, thereby increasing the signal propagation path and increasing the impact of multipath effect. Since the transmitting and receiving devices are usually located at relatively low altitudes, the signal needs to pass through more obstacles, resulting in more reflections and scattering. In addition, the frequency of the signal is also one of the factors that affect the multipath effect. In low-altitude scenarios, higher-frequency signals are often blocked by the ground and buildings, thereby increasing the signal propagation path and increasing the impact of multipath effect. The multipath echo signals are coherently superimposed at the receiving end, causing the amplitude of the received signal to fluctuate between enhancement and attenuation, accompanied by phase changes. The drastic fluctuations in the signal affect the stability of the force detection results. In special cases, the amplitudes of the multipath signal and the direct signal may even cancel each other out, posing a challenge to target detection.

[0050] In terms of multipath signal detection, some people have studied the detection performance of M / N detectors under different parameters after establishing the propagation model of multipath signals, but have not given a specific parameter selection method; some people have derived the probability density function (PDF) of the received radar signal under multipath conditions. The probability density function is used to give the detection probability of non-fluctuating targets under different reflection coefficients, and the impact of multipath effects on radar detection is analyzed. Some people have proposed a low-angle target detection method based on broadband multi-frequency radar, which uses multiple pulses of different frequencies to continuously transmit in order to eliminate the coherence of direct echoes and reflected echoes. After receiving all echoes, based on the idea of ​​M / N detectors, multi-frequency samples are arranged in descending order of amplitude. Some people have proposed introducing a scaling factor to deal with the problem of detecting point targets in the presence of diffuse multipath, and modeling the target echo as a superposition of direct plus multipath components. In partially uniform environments, this detector can adapt well to diffuse multipath conditions. Some people have proposed a joint detector using multipath signals based on prior knowledge of the multipath environment, which has better detection performance than traditional detectors. Existing specular reflection is mainly considered and modeled as prior knowledge of multipath environment. Subsequently, a new joint detector combining line of sight (LOS) and multipath signal was explored using the generalized likelihood ratio test (GLRT). Some people proposed an adaptive generalized likelihood ratio detection method to detect targets in an environment with strong multipath scattering. Some people analyzed the multipath effect of continuous wave radar and proposed a method for multipath cancellation. Some people gave a detection parameter selection method for M / N detectors in free space. The empirical parameter method has certain limitations when the N value is large, and the strategy is relatively fixed, which is difficult to adjust for different environments.

[0051] Due to the lack of effective regulatory measures, incidents threatening public safety caused by the "illegal flying" of small drones have occurred frequently. As a typical low, slow, and small target, it is difficult to detect. The purpose of low-altitude detection is to determine whether there is a target from the echo with complex interference. The multipath effect causes the superposition or cancellation of multiple phases, and the unprocessed echo signal is very likely to cause false detection and misjudgment. The existing single-pulse low-altitude target detector has a high misjudgment rate. The hyperparameter selection of the M / N detector based on multiple detections and judgments mainly adopts a fixed formula model and empirical parameters. This method strictly relies on manual experience. When there are large changes in the external environment, it is difficult to adjust the hyperparameters according to the actual situation. Under the condition of achieving the target detection probability, a higher signal-to-noise ratio is required.

[0052] The M / N detector is a classic multi-pulse detector with higher detection capability than the single-pulse detector. Figure 1 The detection process diagram of the M / N detector provided in the embodiment of the present application is as follows: Figure 1 As shown. The M / N detector performs two-level inspections on the received signal at each resolution unit, where the first-level inspection is a single pulse inspection, and the second-level inspection is a determination link composed of a bit judger and an accumulator. When the detection base station obtains the echo signal of the detection target through the receiver, it first obtains the matched filtering result through a matched filter, and then determines the hyperparameters M and N of the M / N detector to construct the detector. The constant false alarm rate (CFAR) detection method is used to perform a first-level single pulse inspection on the N echoes. When the result exceeds the threshold, it is determined that the echo contains both signal components and thermal noise and clutter components. When the accumulated result does not exceed the threshold, it is determined to be a false alarm, that is, it only contains thermal noise and clutter components. Next, the first-level inspection results are accumulated. When the cumulative number of first-level inspection results k that exceed the threshold is greater than M, it is finally determined that there is a target, otherwise it is determined that there is no target. Assume that the false alarm probability of the M / N detector is P fa , the corresponding false alarm probability of single pulse detection is P s,fa , the detection probability is P s,D , then the detection probability P of the M / N detector D and false alarm probability P fa It can be expressed as follows using formulas (1) and (2) respectively:

[0053]

[0054]

[0055] Compared with the single pulse detector, the M / N detector has better detection performance in a multipath environment, but its performance is subject to the detection parameters, that is, the values ​​of M and N. The selection of different M and N has a greater impact on the minimum signal-to-noise ratio required for the detector to achieve the same detection probability. When the required signal-to-noise ratio is smaller when the specified detection rate is achieved, that is, the lower the requirement for the signal proportion in the echo signal, the better the performance of the detector.

[0056] Based on this, the present application adopts an improved M / N detector, which constructs a reinforcement learning model with the minimum signal-to-noise ratio as the reward mechanism, and updates the parameter selection strategy in a round-based iterative manner that does not rely on human experience, so as to achieve a higher detection probability with a lower signal-to-noise ratio requirement.

[0057] Figure 2 A method for detecting a target in a multipath environment is provided in an embodiment of the present application, and the method includes:

[0058] S201: Acquire a first signal of a target to be detected;

[0059] S202: Determine a first M / N detector corresponding to the first signal based on the first signal and a preset combination parameter; the combination parameter is determined based on an M parameter and an N parameter in a second M / N detector;

[0060] S203: Determine a first detection probability of a first parameter corresponding to the first M / N detector;

[0061] S204: Determine a target parameter of the first signal using the first detection probability and the combined parameter;

[0062] S205: Determine whether the target to be detected exists in a preset space based on the target parameters.

[0063] In S201, the target to be detected may be any device that transmits a wireless signal in a multipath environment, which is not limited here. As an example, the target to be detected may be a drone. The first signal may be understood as a signal transmitted by the detection base station from the target to be detected.

[0064] In S202, the preset combination parameters can be understood as a preset parameter pairing matrix, and the parameters are M parameters and N parameters in the M / N detector; the preset combination parameters can be exemplified as a pairing matrix constructed by M*N parameter combinations.

[0065] The second M / N detector is a detector with known M parameters and N parameters, and may be one M / N detector or multiple M / N detectors. The combined parameter is determined based on the M parameters and N parameters in the second M / N detector, which can be understood as determining the combined parameter based on the M parameters and N parameters in one and / or multiple known M / N detectors.

[0066] The first M / N detector corresponding to the first signal is determined based on the first signal and the preset combination parameters; it can be understood that based on the first signal, any position is selected in the preset combination parameters for initialization, and the first M / N detector is constructed with the values ​​of M and N at the initialization point.

[0067] In S203, the first parameter may be understood as the values ​​of M and N corresponding to the first M / N detector. The determining of the first detection probability of the first parameter corresponding to the first M / N detector may be understood as inputting the values ​​of M and N corresponding to the first M / N detector into formula (1) to calculate and obtain the first detection probability.

[0068] In S204, the determining the target parameter of the first signal by using the first detection probability and the combined parameter can be understood as inputting the first detection probability and the combined parameter into a preset strategy model to obtain the target parameter of the first signal.

[0069] In S205, the preset space can be understood as low altitude. The determining whether the target to be detected exists in the preset space based on the target parameter can be illustrated as, after obtaining the target parameters, i.e., the values ​​of the target M and N, determining whether the target to be detected exists in the low altitude environment by the values ​​of the target M and N.

[0070] By adopting the technical solution of the embodiment of the present application, the target parameter is determined based on the detection probability of the first parameter and the combined parameter, and an M / N detector that is adaptively tuned according to the environment is implemented, which greatly reduces the workload of traditional traversal parameters and improves the accuracy of establishment.

[0071] In one embodiment, after determining the first detection probability of the first parameter corresponding to the first M / N detector, the method further includes:

[0072] Determining whether the value of the first detection probability is greater than or equal to a preset threshold;

[0073] In a case where the value of the first detection probability is less than the preset threshold, determining a target parameter of the first signal using the first detection probability and the combined parameter;

[0074] When the value of the first detection probability is greater than or equal to the preset threshold, the first parameter is used as the target parameter.

[0075] The preset threshold can be determined according to actual conditions and is not limited here.

[0076] When the value of the first detection probability is less than the preset threshold, the target parameter of the first signal is determined by using the first detection probability and the combined parameter; when the value of the first detection probability is greater than or equal to the preset threshold, the first parameter is used as the target parameter; it can be illustrated by an example that when the value of the first detection probability is less than the detection probability threshold, the target parameter of the first signal is determined by using the first detection probability and the combined parameter, and when the value of the first detection probability is greater than or equal to the detection probability threshold, the first parameter is the target parameter.

[0077] In one embodiment, the determining a target parameter of the first signal by using the first detection probability and the combined parameter includes:

[0078] The first detection probability and the combined parameter are input into a preset strategy model to obtain a target parameter of the first signal.

[0079] In this embodiment, the preset strategy model can be understood as being established by forced learning. The inputting of the first detection probability and the combination parameter into the preset strategy model to obtain the target parameter of the first signal can be understood as, in the combination parameter, determining a second parameter adjacent to the first parameter; determining a second detection probability of the second parameter; and obtaining the target parameter when the second parameter meets a preset condition.

[0080] By adopting the technical solution of the embodiment of the present application, a reinforcement learning model is constructed based on the single pulse detection probability, and a turn-based iterative update parameter selection strategy that does not rely on human experience is proposed, which greatly reduces the workload of traditional traversal parameters. At the same time, it has the ability of automatic adjustment, avoids the cognitive bias caused by human experience, and improves the accuracy of detection.

[0081] In one embodiment, inputting the first detection probability and the combined parameter into a preset strategy model to obtain a target parameter of the first signal includes:

[0082] In the combined parameters, determining a second parameter adjacent to the first parameter;

[0083] determining a second probability of detection of the second parameter;

[0084] When the second parameter meets the preset condition, the target parameter is obtained.

[0085] In this embodiment, in the combination parameter, the second parameter adjacent to the first parameter is determined. It can be understood that when the value of the first detection probability of the first parameter is less than the detection probability threshold, the first parameter performs a state transfer to the next adjacent parameter in the preset combination parameter. It should be noted that adjacent can be understood as up, down, left and right in the pairing matrix.

[0086] It should be noted that the second parameter can be a parameter obtained by transferring the first parameter in the combined parameter by one step, or can be understood as a parameter obtained by transferring the first parameter in the combined parameter by multiple steps. No limitation is made here, and the first parameter can be transferred to the second parameter only through an adjacent method.

[0087] The determining of the second detection probability of the second parameter may be illustrated as: obtaining the second detection probability by calculating according to the values ​​of M and N corresponding to the second parameter using formula (1).

[0088] The preset condition includes at least one of the following: the value of the second detection probability is greater than or equal to a preset threshold; the first step number between the first parameter and the second parameter is greater than or equal to a preset step number.

[0089] In one embodiment, the preset condition includes at least one of the following:

[0090] The value of the second detection probability is greater than or equal to a preset threshold;

[0091] The first step number between the first parameter and the second parameter is greater than or equal to a preset step number.

[0092] It should be noted that the preset threshold is the same as the preset threshold for comparison with the value of the first detection probability. The preset threshold can be determined according to actual conditions and is not limited here. The preset number of steps is determined according to the combination parameter and is not limited here.

[0093] It should be noted that the preset number of steps is determined according to the combination parameter, and can be illustrated as the preset number of steps being determined according to the size of the pairing matrix. The setting of the preset number of steps can prevent loops from occurring during the iteration process.

[0094] In one embodiment, the method further comprises:

[0095] When the first detection probability and / or the second detection probability is greater than or equal to the preset threshold, a first reward parameter is obtained; the first reward parameter is used to determine whether the first parameter and / or the second parameter is the target parameter.

[0096] The first reward parameter may be understood as a state transfer reward value.

[0097] When the first detection probability and / or the second detection probability is greater than or equal to the preset threshold, the first reward parameter is obtained; it can be illustrated by an example that when the value of the first detection probability is greater than or equal to the preset threshold, that is, the first parameter is the target parameter, and the first state transfer reward is obtained.

[0098] It should be noted that, when the value of the first detection probability and / or the value of the second detection probability is less than the preset threshold, the second reward parameter is obtained. The second reward parameter can also be understood as a state transition reward value. An example can be given as follows: when the value of the first detection probability is less than the detection probability threshold, the first parameter is transferred to the next adjacent parameter state through the combination parameter, and a second state transition reward is obtained.

[0099] It should be noted that, when the number of steps between the first parameter and the second parameter is greater than or equal to the preset number of steps, the second parameter can also be understood as the target parameter to obtain the first state transfer reward.

[0100] The values ​​of the first reward parameter and the second reward parameter can be determined according to actual conditions and are not limited here. As an example, the value of the first reward parameter can be 1 / SNR, where SNR is the signal-to-noise ratio (SNR), and the value of the second reward parameter can be -0.1.

[0101] By adopting the technical solution of the embodiment of the present application, a reinforcement learning model is constructed based on the single pulse detection probability and the minimized signal-to-noise ratio, which has a lower requirement on the signal-to-noise ratio while achieving the target detection probability.

[0102] For ease of understanding, here is an example of a practical application in which a policy model is established through reinforcement learning, and the parameter selection strategy is adaptively adjusted according to the changing environmental data.

[0103] The strategy model is initialized from any position in the M*N matrix, and the M / N detector is constructed with the values ​​of M and N at the initialization point. The detection probability P under the hyperparameter is calculated according to formula (1): D . Assume that the initial action strategy is equal probability transfer, that is, π * (s) is when P D When the detection probability threshold is not met, the state is transferred to the next adjacent parameter state with a probability of 0.25. Assuming that the current state is s and the next state is s′, the transfer probability p(s′|s,a)=0.25, and the state transfer reward r(s,a)=-0.1; if the detection probability threshold is met or the maximum step length is exceeded, the round ends, and a reward of 1 / SNR is obtained at the end of the round.

[0104] The algorithm flow of this round is as follows:

[0105] Input: M*N parameter matrix, M / N detector probability model P D and the initial strategy π * (s);

[0106] Output: Optimal parameter selection strategy π′ * (s).

[0107] 1. State initialization:

[0108] Initialize a random position in the matrix with a random initial state value v 0 (s);

[0109] End of round state value v 0 (S 终止 )=0.

[0110] 2. Perform round iterations, for k = 0, 1, 2, 3, ...; calculate the current P D And determine whether the conditions are met. If not, update the action value function for each action, which can be expressed by formula (3) as follows:

[0111]

[0112] If the detection probability threshold is met or the maximum step length is exceeded, the round ends; and the deterministic strategy π′ is output according to the value function * (s), expressed by formula (4) as follows:

[0113]

[0114] If π * (s)≠π′ * (s), update π * (s)←π′ * (s). Output the deterministic selection strategy π′ through reinforcement learning * (s), adaptively adjust the detector hyperparameter selection strategy in a multipath environment according to the continuously updated data, so as to achieve a higher detection probability with a lower signal-to-noise ratio requirement.

[0115] In one embodiment, the method further comprises:

[0116] In a case where the target to be detected exists in the preset space, position information and speed information corresponding to the target to be detected are determined according to the first signal.

[0117] In this embodiment, determining the position information and speed information corresponding to the target to be detected based on the first signal can be understood as determining a third parameter based on the first signal and the Doppler shift parameter caused by the target to be detected; performing discrete Fourier transform processing on the third parameter to obtain a fourth parameter corresponding to the target to be detected; determining the speed information based on the fourth parameter; and performing inverse discrete Fourier transform processing on the fourth parameter to obtain the position information.

[0118] It should be noted that the position information and the speed information are used to visualize the target to be detected.

[0119] In one embodiment, determining the position information and speed information corresponding to the target to be detected according to the first signal includes:

[0120] Determine a third parameter according to the first signal and the Doppler shift parameter caused by the target to be detected;

[0121] Performing discrete Fourier transform processing on the third parameter to obtain a fourth parameter corresponding to the target to be detected;

[0122] determining the speed information based on the fourth parameter;

[0123] Perform inverse discrete Fourier transform processing on the fourth parameter to obtain the position information.

[0124] In this embodiment, for ease of understanding, an example can be given as using multiple transmitting antennas and receiving antennas through Massive MIMO technology during the detection process, so that the signal is transmitted and received through multiple antennas at the transmitting end and the receiving end. This method gives full play to the advantages of spatial resources and provides system channel capacity in multiples without increasing spectrum resources and antenna transmission power. At present, the number of channels of antennas used in the fifth generation mobile communication technology (5th Generation Mobile Communication Technology, 5G) base stations is generally 64, which can reach 128 / 256 in the future. Each sub-antenna obtains waveform diversity capability by transmitting different orthogonal signals, significantly reducing the probability of multi-level echo and direct echo cancellation, effectively weakening the multipath effect, and overcoming the flickering phenomenon of radar detection results.

[0125] Here, it is assumed that there are P transmitting antennas and Q receiving antennas. The transmitting end contains N subcarriers. The subcarrier number of a single antenna is n. p =p+i·p, 0≤i≤(N / P-1), assuming that a frame contains M OFDM symbols, H is the detected target, and the echo signal contains D of factors such as delay and Doppler frequency shift in addition to the transmitted signal symbol. q,p It can be expressed by formulas (5) and (6):

[0126]

[0127]

[0128] In formulas (5) and (6), α h is the attenuation coefficient, where the distance R(t m ) The total distance is decomposed into r p,h and r q,h , respectively represent the distances from the transmitting and receiving antennas to the target h, c o is the speed of light, f D,q,p,h is the Doppler shift caused by the target h, T is the duration of an OFDM symbol with a cyclic prefix, Z(n p,m ) represents additive high-speed white noise.

[0129] Right D q,p The Doppler frequency shift calculated by row-by-row discrete Fourier transform can be expressed by formula (7) as follows:

[0130]

[0131] V q,p (i, l) Perform inverse discrete Fourier transform along the frequency axis to obtain the target's distance and Doppler matrix R q,p (k,l), can be expressed by formula (8) as follows:

[0132]

[0133] After obtaining the Range Doppler (RD) map in actual measurement, the position and velocity information of the target are determined by the highest point in the map. Figure 3 A schematic diagram of a target detection simulation result provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, Figure 3 The horizontal axis represents speed, the vertical axis represents distance, and the vertical axis represents amplitude.

[0134] By adopting the technical solution of the embodiment of the present application, an adaptive M / N detector based on reinforcement learning is proposed. The detector constructs a reinforcement learning model with the minimum signal-to-noise ratio as a reward mechanism, and updates the parameter selection strategy in a turn-based iterative manner that does not rely on human experience. The signal-to-noise ratio requirement is lower under the premise of achieving the target detection probability. Based on historical data, an M / N detector that is adaptively tuned according to the environment is implemented, which greatly reduces the workload of traditional traversal parameters. At the same time, it has the ability to automatically adjust, avoid cognitive bias caused by human experience, and improve the accuracy of detection. After the target is screened and confirmed, the target position and speed are calculated, and the visual display of the detected target on the drone cloud platform is completed.

[0135] In order to implement the method of the embodiment of the present application, the embodiment of the present application also provides a target detection device in a multi-level environment. Figure 4 A schematic diagram of a target detection device in a multi-stage environment provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, the device 400 includes:

[0136] A first acquisition unit 401 is used to acquire a first signal of a target to be detected;

[0137] A first determining unit 402 is configured to determine a first M / N detector corresponding to the first signal based on the first signal and a preset combination parameter; the combination parameter is determined based on an M parameter and an N parameter in a second M / N detector;

[0138] A second determining unit 403, configured to determine a first detection probability of a first parameter corresponding to the first M / N detector;

[0139] A third determining unit 404 is configured to determine a target parameter of the first signal using the first detection probability and the combined parameter;

[0140] The fourth determining unit 405 is configured to determine whether the target to be detected exists in a preset space based on the target parameter.

[0141] In one embodiment, the device 400 also includes a judgment unit for judging whether the value of the first detection probability is greater than or equal to a preset threshold; when the value of the first detection probability is less than the preset threshold, the target parameter of the first signal is determined using the first detection probability and the combined parameter; when the value of the first detection probability is greater than or equal to the preset threshold, the first parameter is used as the target parameter.

[0142] In one embodiment, the third determination unit 404 is further configured to input the first detection probability and the combination parameter into a preset strategy model to obtain a target parameter of the first signal.

[0143] In one embodiment, the third determination unit 404 is further used to determine a second parameter adjacent to the first parameter in the combined parameter; determine a second detection probability of the second parameter; and obtain the target parameter when the second parameter meets a preset condition.

[0144] In one embodiment, the preset condition includes at least one of the following: the value of the second detection probability is greater than or equal to a preset threshold; the first step number between the first parameter and the second parameter is greater than or equal to a preset step number.

[0145] In one embodiment, the device 400 also includes a second acquisition unit, which is used to acquire a first reward parameter when the first detection probability and / or the second detection probability is greater than or equal to the preset threshold; the first reward parameter is used to determine that the first parameter and / or the second parameter is the target parameter.

[0146] In one embodiment, the device 400 further includes a fifth determining unit, configured to determine, when the target to be detected exists in the preset space, position information and speed information corresponding to the target to be detected according to the first signal.

[0147] In one embodiment, the fifth determination unit is further used to determine a third parameter based on the first signal and the Doppler shift parameter caused by the target to be detected; perform discrete Fourier transform processing on the third parameter to obtain a fourth parameter corresponding to the target to be detected; determine the speed information based on the fourth parameter; and perform inverse discrete Fourier transform processing on the fourth parameter to obtain the position information.

[0148] The target detection device in a multipath environment provided in the above embodiment and the target detection method embodiment in a multipath environment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0149] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the target detection method in a multipath environment provided in the above embodiment are implemented.

[0150] Figure 5 FIG. 1 is a schematic diagram of a hardware entity structure of a target detection device in a multipath environment in an embodiment of the present application. Figure 5 As shown, the hardware entity of the device 500 includes: a processor 501 and a memory 503 . Optionally, the target detection device 500 in a multipath environment may further include a communication interface 502 .

[0151] It can be understood that the memory 503 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAM bus random access memory (DRRAM, Direct Rambus Random Access Memory).The memory 503 described in the embodiments of the present application is intended to include but is not limited to these and any other suitable types of memories.

[0152] The method disclosed in the above embodiment of the present application can be applied to the processor 501, or implemented by the processor 501. The processor 501 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 501. The above processor 501 may be a general processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 501 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general processor can be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present application, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in the memory 503, and the processor 501 reads the information in the memory 503 and completes the steps of the above method in combination with its hardware.

[0153] In an exemplary embodiment, the device may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD), field programmable gate array (FPGA), general processor, controller, microcontroller (MCU), microprocessor, or other electronic components to execute the aforementioned method.

[0154] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0155] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0156] The methods disclosed in several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0157] The features disclosed in several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0158] The features disclosed in several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0159] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A target detection method in a multipath environment, It is characterized in that The method comprises: Acquire a first signal of a target to be detected; Determine a first M / N detector corresponding to the first signal based on the first signal and a preset combination parameter; the combination parameter is determined based on an M parameter and an N parameter in a second M / N detector; determining a first detection probability of a first parameter corresponding to the first M / N detector; determining a target parameter of the first signal using the first detection probability and the combined parameter; It is determined based on the target parameters whether the target to be detected exists in the preset space.

2. The method according to claim 1, It is characterized in that After determining the first detection probability of the first parameter corresponding to the first M / N detector, the method further includes: Determining whether the value of the first detection probability is greater than or equal to a preset threshold; In a case where the value of the first detection probability is less than the preset threshold, determining a target parameter of the first signal using the first detection probability and the combined parameter; When the value of the first detection probability is greater than or equal to the preset threshold, the first parameter is used as the target parameter.

3. The method according to claim 1, It is characterized in that The determining the target parameter of the first signal by using the first detection probability and the combined parameter includes: The first detection probability and the combined parameter are input into a preset strategy model to obtain a target parameter of the first signal.

4. The method according to claim 3, It is characterized in that The step of inputting the first detection probability and the combined parameter into a preset strategy model to obtain a target parameter of the first signal includes: In the combined parameters, determining a second parameter adjacent to the first parameter; determining a second probability of detection of the second parameter; When the second parameter meets the preset condition, the target parameter is obtained.

5. The method according to claim 4, It is characterized in that The preset condition includes at least one of the following: The value of the second detection probability is greater than or equal to a preset threshold; The first step number between the first parameter and the second parameter is greater than or equal to a preset step number.

6. The method according to claim 4, It is characterized in that The method further comprises: When the first detection probability and / or the second detection probability is greater than or equal to the preset threshold, a first reward parameter is obtained; the first reward parameter is used to determine whether the first parameter and / or the second parameter is the target parameter.

7. The method according to claim 1, It is characterized in that The method further comprises: In a case where the target to be detected exists in the preset space, position information and speed information corresponding to the target to be detected are determined according to the first signal.

8. The method according to claim 7, It is characterized in that The determining, according to the first signal, the position information and the speed information corresponding to the target to be detected includes: Determine a third parameter according to the first signal and the Doppler shift parameter caused by the target to be detected; Performing discrete Fourier transform processing on the third parameter to obtain a fourth parameter corresponding to the target to be detected; determining the speed information based on the fourth parameter; Perform inverse discrete Fourier transform processing on the fourth parameter to obtain the position information.

9. A target detection device in a multipath environment, It is characterized in that The device comprises: A first acquisition unit, used to acquire a first signal of a target to be detected; A first determining unit, configured to determine a first M / N detector corresponding to the first signal based on the first signal and a preset combination parameter; the combination parameter is determined based on an M parameter and an N parameter in a second M / N detector; a second determining unit, configured to determine a first detection probability of a first parameter corresponding to the first M / N detector; a third determining unit, configured to determine a target parameter of the first signal using the first detection probability and the combined parameter; The fourth determining unit is used to determine whether the target to be detected exists in a preset space based on the target parameter.

10. A target detection device in a multipath environment, It is characterized in that include: a processor and a memory for storing a computer program capable of being executed on the processor; Wherein, when the processor is used to run the computer program, it executes the steps of the method described in any one of claims 1 to 8.

11. A storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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