Distance measuring system and distance measuring method

Abstract

The application provides a ranging system and a ranging method. The ranging system comprises: a frequency-modulated continuous wave radar arranged on a trolley, used for transmitting a detection signal when the trolley is in a running process, and receiving at least a return signal emitted from an active radar tag; and at least one active radar tag arranged at a target position, used for receiving the detection signal, modulating the detection signal, generating and transmitting a response signal with a preset frequency offset; wherein the target position is arranged along the running direction of the trolley; wherein the frequency-modulated continuous wave radar is used for determining effective return signals by detecting the frequency modulation characteristics of the response signal, and determining distance information and / or speed information between the target position and the trolley according to beat signals between the return signals and the detection signals. By using the above technical scheme, the target recognition accuracy can be improved, and the implementation cost can be reduced.

Description

Technical Field

[0001] This invention relates to the field of wireless ranging and radio frequency identification technology, specifically to a ranging system and ranging method. Background Technology

[0002] Frequency Modulated Continuous Wave (FMCW) radar is widely used in industrial ranging and collision avoidance due to its advantages such as simple structure, low cost, and ability to achieve unambiguous ranging.

[0003] However, in complex environments with a large number of metal reflectors, such as steel mills, warehouses, and construction sites, the wide beam signal of frequency modulated continuous wave radar will cause serious multipath reflection problems and even generate false target signals, which severely restricts its application in critical safety scenarios (such as crane collision avoidance).

[0004] Currently, existing solutions typically focus on improving the signal processing algorithms or antenna design of frequency modulated continuous wave radar in an attempt to separate the real target from the mixed signal, but this is ineffective and costly in extreme multipath environments. Summary of the Invention

[0005] In view of this, the present invention provides a ranging system and a ranging method that can improve target recognition accuracy while reducing implementation costs.

[0006] In a first aspect, the present invention provides a ranging system, comprising: a frequency-modulated continuous wave radar and at least one active radar tag, wherein:

[0007] The frequency-modulated continuous wave radar, mounted on the overhead crane, is used to transmit detection signals and receive at least echo signals emitted from at least one of the active radar tags while the overhead crane is in motion. At least one of the active radar tags is disposed at the target location to receive the detection signal, modulate the detection signal, generate and transmit a response signal with a preset frequency offset; wherein the target location is disposed along the travel direction of the overhead crane; The frequency-modulated continuous wave radar is used to determine the effective echo signal by detecting the frequency modulation characteristics of the response signal, and to determine the distance information and / or velocity information between the echo signal and the target position based on the beat frequency signal between the echo signal and the detection signal.

[0008] Optionally, at least one of the said active radar tags includes: A receiving unit is configured to receive the detection signal and amplify the detection signal to obtain an amplified detection signal; A modulation unit is configured to, upon receiving the amplified detection signal, mix the amplified detection signal with its own generated intermediate frequency signal, such that the frequency of the mixed signal is the superposition of the frequency of the amplified detection signal and the frequency of the intermediate frequency signal; wherein the frequency of the intermediate frequency signal is the preset frequency; The transmitting unit is used to amplify the power of the mixed signal and then transmit the response signal.

[0009] Optionally, the modulation unit includes a mixer, which performs frequency conversion superposition of the received amplified detection signal and the intermediate frequency signal generated by the local oscillator to obtain the mixed signal.

[0010] Optionally, when there are multiple active radar tags, each active radar tag is set at a corresponding target location, and each active radar tag is configured with its own corresponding identification information, and / or configured so that the frequency of the mixing signal generated by each active radar tag is different.

[0011] Optionally, the frequency-modulated continuous wave radar includes an antenna array for direction finding, used to determine the spatial direction of arrival corresponding to each response signal when a response signal of each active radar tag is received.

[0012] Optionally, the ranging system further includes: a processor coupled to the frequency modulated continuous wave radar and the active radar tag, respectively; The processor is used to acquire distance and velocity information of multiple targets detected by the frequency-modulated continuous wave radar, and construct a multi-target set; wherein, the multiple targets include at least targets formed by the target positions corresponding to the active radar tags, and include environmental scattering targets detected by the frequency-modulated continuous wave radar; based on the multi-target set and the crane's own operating state, the processor predicts the relative motion state of each target and the crane at multiple future moments, and based on the predicted relative motion state, assesses the collision risk of each target to determine potential collision targets.

[0013] Optionally, the processor is used to: A target observation structure is constructed for each target location corresponding to the active radar tag and for the environmental scattering target detected by the frequency-modulated continuous wave radar. The target observation structure includes at least the target range, radial velocity, signal-to-noise ratio, detection timestamp, and target category identifier. Based on the target category identifier, the observed targets are divided into active radar tag target class and environmental scattering target class, and different initial confidence weights are set for different categories of observed targets; Within multiple consecutive detection cycles, based on the continuity of distance and velocity changes, the observation results of each observed target are time-correlated and matched to form a target observation sequence. For each of the target observation sequences, the observation stability index of the corresponding observation target is calculated. The observation stability index includes at least the distance variance, velocity variance, and detection missing rate. The initial confidence weight is dynamically corrected based on the observation stability index, and the target distance information and velocity information are weighted and fused according to the corrected confidence weight to form a unified state input quantity for each observed target in the multi-target set.

[0014] Optionally, the processor is used to: For each observed target in the multi-target set, an extended state vector containing target position, target velocity, and corresponding covariance information is constructed. Establish at least two target motion assumption models, wherein the target motion assumption models include at least a uniform motion model and a motion model containing an acceleration perturbation term; The matching degree of each target motion hypothesis model is calculated based on the target historical state sequence, and the model probability corresponding to each target motion hypothesis model is determined by the matching degree. Based on each motion hypothesis model and its corresponding model probability, the position and velocity of each observed target are predicted in parallel at multiple future prediction times, and the covariance information is propagated synchronously to obtain the prediction uncertainty range at each prediction time. The prediction results under different motion assumption models are fused by probability weighting to obtain the comprehensive prediction state of each target at each prediction time. Based on the crane's own operating state model, the position of the crane at the corresponding prediction time is predicted synchronously, thereby obtaining the relative predicted state distribution between each observation target and the crane.

[0015] Optionally, the processor is used to: Based on the relative predicted state distribution, the collision probability value between each observed target and the crane is calculated at each predicted time. The collision probability value is determined by the predicted relative distance distribution between each observed target and the crane and a preset safe distance threshold. A target temporal risk sequence is constructed for the collision probability values ​​of the same observed target at multiple prediction times; The target time-series risk sequence is subjected to time-weighted cumulative processing to obtain a comprehensive risk index of the target within the prediction time window, wherein the prediction risk weight closer to the current moment is greater than the prediction risk weight in the long term. Based on the prediction uncertainty range corresponding to each target, the comprehensive risk index is subject to risk confidence constraint judgment. When the comprehensive risk index of a target exceeds a preset risk threshold under the condition of satisfying the risk confidence constraint, the observed target is identified as a potential collision target.

[0016] Secondly, the present invention provides a ranging method applied to a ranging system according to any of the foregoing embodiments, the ranging system comprising a frequency-modulated continuous wave radar and at least one active radar tag, the ranging method comprising: Along the direction of travel of the overhead crane, the frequency-modulated continuous wave radar is used to transmit detection signals; wherein, the frequency-modulated continuous wave radar is installed on the overhead crane; The detection signal is received by at least one of the active radar tags, and the detection signal is modulated to generate and transmit a response signal with a preset frequency offset; wherein at least one of the active radar tags is disposed at the target position, and the target position is disposed along the travel direction of the overhead crane; The response signal is received, and the frequency modulation characteristics of the response signal are detected using the frequency-modulated continuous wave radar to determine the valid echo signal. Based on the beat frequency signal between the echo signal and the detection signal, the distance information and / or velocity information between the target position and the target position are determined.

[0017] Compared with the prior art, the technical solution of the embodiments of the present invention has the following advantages: In the ranging scheme provided by this invention, while the overhead crane is in motion, a frequency-modulated continuous wave (FM-CW) radar mounted on the crane can emit a detection signal. An active radar tag positioned at the target location can receive the detection signal and modulate it to generate and emit a response signal with a preset frequency offset. Thus, when the FM-CW radar receives multiple signals, it can determine the valid echo signal by detecting the frequency modulation characteristics of the response signal, and determine the distance and / or velocity information between the effective echo signal and the detection signal based on the beat frequency signal between the valid echo signal and the detection signal. In other words, the FM-CW radar can selectively process target echo signals, solving the multipath interference problem, improving target identification accuracy, and providing collision avoidance, without significantly modifying the FM-CW radar itself, thereby reducing implementation costs. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a ranging system provided in an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the structure of an active radar tag provided in an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of a ranging scenario provided by an embodiment of the present invention.

[0021] Figure 4 This is a flowchart of a ranging method provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0023] 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 technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0024] As described in the background section, frequency modulated continuous wave radar is widely used in industrial ranging and collision avoidance.

[0025] However, in complex environments with a large number of metal reflectors, such as steel mills, warehouses, and construction sites, the wide beam signal of frequency modulated continuous wave radar will produce serious multipath reflection problems. The receiver will receive reflected echoes from the target and multiple non-target objects at the same time, resulting in confusion of ranging results, decreased accuracy, and even the generation of false target signals, which seriously restricts its application in critical safety scenarios (such as crane collision avoidance).

[0026] Although it is possible to separate the real target from the mixed signal by improving the signal processing algorithm or antenna design of the frequency modulated continuous wave radar, this is effective but costly in extreme multipath environments.

[0027] Therefore, how to fundamentally avoid interference from non-target echoes and achieve reliable ranging only for specific authorized targets has become an urgent problem to be solved.

[0028] Against this backdrop, embodiments of the present invention provide a ranging scheme in which a frequency-modulated continuous wave (FM-CRW) radar mounted on a crane emits a detection signal while the crane is in motion. An active radar tag positioned at the target location receives the detection signal and modulates it to generate and emit a response signal with a preset frequency offset. Thus, when the FM-CRW radar receives multiple signals, it can determine the valid echo signal by detecting the frequency modulation characteristics of the response signal, and determine the distance and / or velocity information between the effective echo signal and the detection signal based on the beat frequency signal between the valid echo signal and the detection signal. In other words, the FM-CRW radar can selectively process target echo signals, solving the multipath interference problem, improving target identification accuracy, and providing collision avoidance, without significantly modifying the FM-CRW radar itself, thereby reducing implementation costs.

[0029] To enable those skilled in the art to better understand and implement this solution, the following detailed description of the specific solution, principles, advantages, and effects of the present invention is provided with reference to the accompanying drawings and specific embodiments.

[0030] First, let me briefly describe the working mechanism of frequency modulated continuous wave radar.

[0031] Frequency-modulated continuous wave (FM-CHW) radar is a type of radar based on frequency modulation that uses continuous wave signals for target detection. During operation, the transmitter generates a continuous wave signal (i.e., a frequency-modulated signal) whose frequency varies with time (e.g., linearly). This signal is transmitted through an antenna to the target object and reflected back to the receiver. Based on the received echo signal and the frequency difference (i.e., frequency offset) between the original transmitted signal and the received echo signal, the target's range and relative velocity are calculated.

[0032] Specifically, the frequency of the frequency-modulated signal increases from the starting frequency to the ending frequency, and then restarts. This frequency modulation generates a time-dependent frequency difference, which the receiver compares with the frequency difference of the transmitted signal, using the Doppler effect to calculate the target's distance and relative velocity.

[0033] Figure 1 This is a schematic diagram of a ranging system provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the ranging system 100 may include a frequency-modulated continuous wave radar 110 and at least one active radar tag (e.g., Figure 1 The diagram illustrates the first active radar tag 121, the second active radar tag 122, ..., the nth active radar tag 12n, where n is an integer greater than or equal to 1. Frequency modulated continuous wave radar 110, installed on the overhead crane ( Figure 1On (not shown), for transmitting detection signals and receiving at least one of the active radar tags (e.g., the first active radar tag 121, the second active radar tag 122, ..., the nth active radar tag 12n) while the crane is in motion; At least one active radar tag is set at the target location to receive detection signals, modulate the detection signals, generate and transmit a response signal with a preset frequency offset; wherein the target location is set along the travel direction of the crane. The frequency-modulated continuous wave radar 110 is used to determine the effective echo signal by detecting the frequency modulation characteristics of the response signal, and to determine the distance information and / or velocity information between the echo signal and the target position based on the beat frequency signal between the echo signal and the detection signal.

[0034] To better understand and illustrate the working mechanism of the ranging system 100 in the embodiments of the present invention, the interaction process between the frequency modulated continuous wave radar 110 and the first active radar tag 121 is used as an example for explanation.

[0035] Overhead cranes are used within factories to facilitate the transfer and transport of materials. During their movement, there are obstacles that may cause collisions with the cranes.

[0036] In this configuration, a frequency-modulated continuous wave radar 110 is installed on the overhead crane. While the crane is in motion, the frequency-modulated continuous wave radar 110 can transmit detection signals (wherein the detection signals are radio frequency signals with linearly changing frequencies) to the surrounding environment. When the detection signals detect an object, they are reflected, thus forming an echo signal, which is received by the frequency-modulated continuous wave radar 110.

[0037] As mentioned above, the environment in a factory is quite complex, and there are multiple echo signals.

[0038] Based on this, by setting a first active radar tag 121 at the target location, upon receiving a detection signal, the first active radar tag 121 can modulate the detection signal, generating and transmitting a response signal with a preset frequency offset. That is, the first active radar tag 121 can apply an additional frequency offset to the detection signal, making the response signal returned by the first active radar tag 121 distinct from other signals. This is because the beat frequencies generated by other environmental reflection signals are random and dispersed.

[0039] In this way, when all echo signals are received, the frequency modulated continuous wave radar 110 can identify the valid echo signal by detecting the frequency modulation characteristics of the response signal, and can ignore stray echoes that are passively reflected by other objects in the environment and do not have this characteristic.

[0040] Thus, the frequency-modulated continuous wave radar 110 can determine the distance and / or velocity information between the target position based on the beat frequency signal between the echo signal and the detection signal. That is, the frequency-modulated continuous wave radar 110 can almost perfectly extract the effective signal from the first active radar tag 121, and then calculate the accurate flight time and distance.

[0041] Using the ranging system in the above example, the frequency modulated continuous wave radar 110 can identify the preset feature and uniquely and accurately extract the effective signal from the first active radar tag from the echo signal containing a large amount of environmental clutter, and perform ranging. This fundamentally eliminates the influence of multipath interference on ranging in complex reflection environments, and achieves high-reliability measurement only for specific targets. It is especially suitable for fields such as industrial collision avoidance, target identification and tracking.

[0042] It should be noted that, firstly, the second active radar tag 122, ..., the nth active radar tag 12n have the same working mechanism as the first active radar tag 121; secondly, the main body of the radar host still adopts the mature and low-cost FMCW architecture, without the need for extremely complex antennas or ultra-wideband processing, the cost of the newly added active tags is controllable, and the overall system has a high cost-performance ratio; thirdly, the travel process in this scheme includes: the travel state and the stop state between the travel states, that is, the travel process in this scheme has a broad meaning; of course, it is only in the travel state that the frequency modulated continuous wave radar 110 can be driven to send detection signals through the control of the processor.

[0043] In some embodiments, see Figure 2 The diagram shown is a structural schematic of an active radar tag provided by an embodiment of the present invention. Figure 2 As shown, at least one active radar tag 200 may include: The receiving unit 210 is used to receive the detection signal and amplify the detection signal to obtain an amplified detection signal; The modulation unit 220 is used to mix the amplified detection signal with its own generated intermediate frequency signal when it receives the amplified detection signal, so that the frequency of the mixed signal is the superposition of the frequency of the amplified detection signal and the frequency of the intermediate frequency signal; wherein the frequency of the intermediate frequency signal is a preset frequency. The transmitting unit 230 is used to amplify the power of the mixed signal and then transmit the response signal.

[0044] Specifically, the receiving unit 210 acts as a receiving antenna, capable of receiving detection signals from the air and amplifying the detection signals (e.g., low-noise amplification) to generate amplified detection signals, providing high-quality input signals for subsequent processing steps.

[0045] For example, the receiving unit 210 may include: a receiving antenna for receiving a detection signal; and a radio frequency front-end circuit connected to the receiving antenna for low-noise amplification of the received radio frequency signal.

[0046] The modulation unit 220 obtains a new frequency combination signal by mixing the amplified detection signal with the intermediate frequency signal. The mixing process changes the frequency characteristics of the detection signal by superimposing it with a preset intermediate frequency signal, so as to facilitate subsequent signal processing and signal transmission.

[0047] After receiving the mixed signal, the transmitting unit 230 amplifies its power to ensure that the signal can propagate within the required range and sends a response signal to the outside. This response signal can be used to provide feedback on changes in the detection signal, which can then be received by the frequency-modulated continuous wave radar 110.

[0048] For example, the transmitting unit 230 may include: a power amplifier for amplifying the mixed signal; and a transmitting antenna connected to the power amplifier for actively radiating the amplified response signal into space.

[0049] Through this process, the active radar tag 200 can process the detection signal and effectively transmit it after certain frequency modulation and power amplification, thereby realizing the response and feedback to the status of the frequency modulated continuous wave radar 110.

[0050] In some embodiments, the receiving unit 210 is typically composed of a low-noise amplifier (LNA), through which the received probe signal is initially amplified. The main function of the LNA is to amplify the signal gain while maintaining signal quality and minimizing the generation of additional noise. The amplified signal retains as many characteristics of the original signal as possible in the frequency spectrum, providing a high-quality input signal for subsequent modulation and transmission steps.

[0051] The modulation unit 220 includes a mixer, which converts and superimposes the received amplified probe signal with the intermediate frequency signal generated by the local oscillator to obtain a mixed signal.

[0052] Specifically, the modulation unit 220 mixes the received amplified detection signal with the intermediate frequency signal generated by the local oscillator (LO).

[0053] The intermediate frequency signal is usually a fixed preset frequency (e.g., 10MHz). The mixer adds or subtracts the frequency of the amplified probe signal from the fixed frequency output by the local oscillator to generate a new signal.

[0054] Specifically, assuming the frequency of the received amplified detection signal is The local oscillator signal frequency is The frequency of the output mixing signal will be (If upconversion mode is used). The purpose of this process is to convert the signal frequency to a specific signal that is different from other signal frequencies.

[0055] The transmitting unit 230 typically consists of a power amplifier (PA). The power amplifier amplifies the modulated mixed signal to ensure that the signal power reaches a certain level so that it can be effectively radiated through the transmitting antenna. After power amplification, the signal is transmitted at the required amplitude, thus forming a response signal, which is then propagated into the external environment for reception by the frequency-modulated continuous wave radar 110.

[0056] Through the above process, the receiving unit, modulation unit, and transmitting unit work together to effectively process and transmit the received detection signal. First, the received signal undergoes low-noise amplification, then it is mixed with a preset intermediate frequency signal, and finally, the processed signal is transmitted through a power amplifier. This system design ensures signal quality and transmission range, and can be widely used in systems such as active radar tags for efficient target detection and feedback.

[0057] As a specific embodiment, a frequency-modulated continuous wave radar transmits a radio frequency signal with a linearly varying frequency (e.g., in the 24 GHz band). An active radar tag deployed at the location of the target to be measured (e.g., a crane hook, a mobile platform) receives this radio frequency signal.

[0058] At this point, the radio frequency front-end inside the active radar tag performs low-noise amplification, and the mixer upconverts the frequency of the radio frequency signal with a specific intermediate frequency signal (e.g., fixed at 10MHz) generated by a local oscillator. The resulting new signal (frequency of the received radio frequency signal + 10MHz) is amplified by power and actively radiated by the transmitting antenna of the active radar tag.

[0059] Frequency-modulated continuous wave radar receives response signals from active radar tags and a large amount of ambient reflection signals. In the receiver, only the signal from the active radar tag, with a precise offset (e.g., 10 MHz), can generate a stable, predictable intermediate frequency beat signal with the currently transmitted signal. The beat frequencies generated by the ambient reflection signals are random and dispersed.

[0060] By using a narrowband filter with a center frequency matched to the expected beat, the effective signal from the active radar tag can be extracted almost perfectly, thus allowing for the calculation of the precise flight time and distance.

[0061] Specifically, in order to extract the effective signal from the received complex signal of the active radar tag, a narrowband filter is required. The center frequency of this filter is set to the expected beat frequency (e.g., the 10MHz beat signal generated by the tag). In this way, the narrowband filter's function is to allow only the portion of the signal that matches the frequency of the target signal to pass through, while filtering out other unwanted signals (such as random signals reflected from the environment).

[0062] In some embodiments, such as Figure 1 As shown, the number of active radar tags can be multiple, so as to achieve simultaneous measurement of multiple obstacle avoidance locations.

[0063] Specifically, when there are multiple active radar tags, each active radar tag is set at the corresponding target location, and each active radar tag is configured with its own corresponding identification information, and / or configured so that the frequency of the mixing signal generated by each active radar tag is different.

[0064] For example, an active radar tag is set at a corresponding target location, and each active radar tag has its own corresponding identification information. Then, the frequency modulated continuous wave radar can determine the distance information and / or velocity information of the corresponding target location based on the received echo signal and the corresponding identification information.

[0065] For example, if each active radar tag generates a different frequency of mixed signal, then the frequency modulated continuous wave radar can determine the actual number of active radar tags based on the determined frequency offset, thereby determining the corresponding target location.

[0066] For example, each active radar tag has its own corresponding identification information, and the frequency of the generated mixed signal is different, so as to more easily identify different active radar tags and thus more accurately determine the corresponding target location.

[0067] For example, in a steel plant workshop, a frequency-modulated continuous wave radar is installed on overhead crane A, and multiple active radar tags are installed at key locations where collision avoidance is required.

[0068] For example, one active radar tag is set on a crane B (tag ID: Δf1), one active radar tag is set on a plant column (tag ID: Δf2), and one active radar tag is set on the material pile boundary (tag ID: Δf3). Each active radar tag is pre-programmed with a different identity ID (i.e., a different intermediate frequency offset, such as Δf1=5MHz, Δf2=15MHz, Δf3=25MHz).

[0069] When the frequency-modulated continuous wave radar on crane A broadcasts a detection signal, each active radar tag receives the detection signal, modulates it with the frequency offset corresponding to its own ID, and replies with a response signal.

[0070] Frequency-modulated continuous wave radar processes all response signals in parallel. Through a set of parallel matched filters or digital spectrum analysis, it simultaneously separates the three effective signal channels corresponding to Δf1, Δf2, and Δf3, thereby calculating the precise distances between crane A and crane B, the column, and the material pile in real time and synchronously, forming a complete collision avoidance situational awareness.

[0071] For example, frequency modulated continuous wave radar performs spectrum analysis on the received wave signal through fast Fourier transform, and extracts the spectrum components corresponding to Δf1, Δf2 and Δf3 in the spectrum domain. The extracted spectrum components are used as the outputs of the three effective signal channels.

[0072] Alternatively: Frequency modulated continuous wave radar includes multiple digital down-conversion units, each with local oscillator frequencies of Δf1, Δf2, and Δf3, which perform down-conversion and filtering on the received wave signals.

[0073] In some embodiments, the frequency modulated continuous wave radar may include an antenna array for direction finding, used to determine the spatial direction of arrival for each response signal when a response signal of each active radar tag is received.

[0074] Specifically, the antenna array includes at least two spatially spaced receiving antenna elements for acquiring phase and amplitude information of the response signal at different spatial locations.

[0075] Due to the spatial differences in the location of each receiving antenna element, the response signal emitted by the same active radar tag will have different propagation path lengths when it reaches different receiving antenna elements, thus forming a corresponding phase difference in each receiving channel.

[0076] After obtaining the response signals corresponding to different active radar tags, the frequency-modulated continuous wave radar further utilizes the phase difference and / or amplitude difference between each receiving channel to determine the spatial direction of arrival of the active radar tag's response signal. The spatial direction of arrival can be represented by azimuth, elevation, or a combination thereof.

[0077] In this way, the frequency modulated continuous wave radar system can simultaneously obtain the range information and spatial direction of arrival information corresponding to the response signal of each active radar tag when it receives the response signal of each active radar tag, thereby realizing the positioning or tracking of the active radar tag in space.

[0078] In some embodiments, the antenna array can be a linear array, a surface array, or a ring array. The number and arrangement of the receiving antenna elements can be adjusted according to the direction finding accuracy requirements and system cost, and the present invention does not limit this.

[0079] In the above embodiments, by combining the frequency-modulated continuous wave radar installed on the crane with the active radar tag installed at the target location, the distance information and / or speed information of the target location can be obtained.

[0080] However, in real-world applications, overhead cranes often need to face multiple targets simultaneously during their operation. Simply obtaining the distance and speed information of each target at the current moment is insufficient to meet the comprehensive assessment requirements for the safety of crane operation.

[0081] Therefore, the present invention also provides a solution that, based on the above-mentioned ranging system, processes and predicts the motion states of multiple targets to assess potential collision risks.

[0082] In some embodiments, the ranging system further includes a processor coupled to a frequency-modulated continuous wave radar and an active radar tag, the processor being capable of controlling the movement of the overhead crane.

[0083] Accordingly, the processor acquires the distance and velocity information of multiple targets detected by the frequency-modulated continuous wave radar and constructs a multi-target set. The multiple targets include targets formed by the target positions corresponding to active radar tags and environmental scattering targets detected by the frequency-modulated continuous wave radar. Based on the multi-target set and the crane's own operating status, the processor predicts the relative motion state of each target and the crane at multiple future moments, and assesses the collision risk of each target based on the predicted relative motion state to determine potential collision targets.

[0084] Specifically, frequency-modulated continuous wave (FM-CW) radar scans the surrounding environment and detects multiple targets. By analyzing the reflected signals, FM-CW radar can calculate the distance to each target and its relative velocity.

[0085] The processor processes signals received from the frequency-modulated continuous wave radar, identifies and extracts relevant information about multiple targets, including parameters such as target range and velocity. The processor then constructs this information into a multi-target dataset.

[0086] This set includes two types of targets: Targets identified by active radar tags: These targets are located using active radar tags that communicate with the overhead crane. The location information of these targets is fed back to the overhead crane via the radar signals they emit, and the processor can determine the precise location of the targets based on these signals.

[0087] Environmental scattering targets detected by frequency-modulated continuous wave radar: These targets mainly include stationary or moving objects in the surrounding environment (such as buildings, obstacles, pedestrians, etc.). The presence of these targets is obtained through the scattering of radar reflected signals, and the processor calculates the target's distance and relative velocity based on these reflected signals.

[0088] Through radar data and processor calculations, a set of multiple targets is formed. This set includes not only targets from active radar tags but also all environmental targets detected by frequency-modulated continuous wave radar. Information for each target in this set includes its position, velocity, and other available radar measurements.

[0089] Based on the obtained multi-target set and the crane's own operating status, the processor further predicts the relative motion state of each target. The crane's operating status includes its current position, velocity, acceleration, heading, and other information, which is usually provided by the crane's navigation system (such as inertial measurement unit, GPS, etc.).

[0090] The processor combines the current operating status of the crane with the current position and velocity information of each target, and uses kinematic models or other suitable prediction algorithms to calculate the relative motion state of each target and the crane at multiple future moments.

[0091] This process involves the following steps: Calculating the trajectory of each target: The processor analyzes the target's current velocity information to predict its future trajectory. These trajectories are dynamically updated based on the target's current velocity, direction, and other parameters.

[0092] Predicting relative distance and velocity: Based on the target's trajectory and the crane's motion state, the processor predicts the relative distance and velocity between each target and the crane. This process may need to consider the target's motion type (e.g., uniform linear motion, accelerated motion, etc.).

[0093] Based on the predicted relative motion state of each target at future moments, the processor assesses the collision risk of each target. The goal of the collision risk assessment is to determine whether there is a possibility of a collision between the target and the crane by analyzing the relative position, velocity, and direction of motion of each target and the crane.

[0094] More specifically, the processor is used to: construct target observation structures for targets formed at the target locations corresponding to each active radar tag and for environmental scattering targets detected by frequency-modulated continuous wave radar, wherein the target observation structure includes at least target range, radial velocity, signal-to-noise ratio, detection timestamp, and target category identifier; classify the observed targets into active radar tag target class and environmental scattering target class according to the target category identifier, and set different initial confidence weights for different categories of observed targets; perform time correlation matching on the observation results of each observed target within multiple consecutive detection cycles based on the continuity of range change and velocity change to form a target observation sequence; calculate the observation stability index of the corresponding observed target for each target observation sequence, wherein the observation stability index includes at least range variance, velocity variance, and detection missing rate; dynamically correct the initial confidence weights based on the observation stability index, and perform weighted fusion of target range information and velocity information according to the corrected confidence weights to form a unified state input quantity for each observed target in the multi-target set.

[0095] For example, the target location corresponding to each active radar tag and the environmental scattering target detected by the frequency-modulated continuous wave radar together constitute a target observation structure.

[0096] The target observation structure should include at least the following: Target distance: Calculated from the radar return signal, representing the spatial distance between the target and the radar.

[0097] Radial velocity: Calculated based on the frequency offset of the radar signal, representing the target's velocity relative to the radar.

[0098] Signal-to-noise ratio (SNR): A quality indicator of radar detection signals, reflecting the ratio of the target's returned signal to noise.

[0099] Detection timestamp: Records the time of target observation for time-series correlation.

[0100] Targets can be categorized into active radar tagged targets and environmental scattering targets by identifying and labeling them using algorithms.

[0101] Target category classification and initial confidence weight setting: Based on the target category identifier, the processor classifies all observed targets into two categories: Active radar tag target type: This type of target corresponds to the signal emitted by active radar tags, which usually has high reliability.

[0102] Environmental scattering targets: These targets are signals reflected back by environmental scattering, and their reliability may be low.

[0103] Different initial confidence weights are set for different categories of target observations. For example, active radar-tagged targets may have higher initial confidence weights, while environmental scattering targets may have lower initial weights.

[0104] Over multiple consecutive detection cycles, the processor performs time-correlation matching on the observation results of each observed target based on the following two key indicators: Distance variation continuity: The smoothness of the target distance change over time. If the distance change of the target is relatively continuous over multiple periods, the observation results of the target are highly correlated.

[0105] Velocity change continuity: The smoothness of the radial velocity change of the target over time. By comparing the velocity changes in different periods, it can be determined whether the target is the same object.

[0106] Using these metrics, the processor can effectively correlate the target observation results of each cycle to form a continuous target observation sequence.

[0107] For each target observation sequence, the processor calculates an observation stability index for that target, which is used to evaluate the stability of the target observation results. The observation stability index includes at least the following aspects: Distance variance: reflects the degree of fluctuation in target distance over multiple observation periods. The smaller the variance, the more stable the target distance changes.

[0108] Velocity variance: reflects the degree of fluctuation in the radial velocity of a target over multiple observation periods. The smaller the variance, the more stable the velocity changes of the target.

[0109] Missing detection rate: This represents the proportion of a target that is not successfully detected across multiple periods. A lower missing rate indicates better observation stability of the target.

[0110] Based on the calculated observation stability index, the processor dynamically adjusts the initially set target confidence weights. Specifically: If the target's distance variance and velocity variance are small, and the detection missing rate is low, it indicates that the observation results of the target are relatively stable, and the confidence weight can be appropriately increased.

[0111] If the observation results fluctuate significantly or are missing, the confidence weight may be reduced.

[0112] After the target confidence weights are corrected, the processor performs weighted fusion of the target's distance and velocity information based on the corrected weights. This weighted fusion method integrates different information from multiple observed targets into a unified state input for subsequent multi-target tracking and prediction.

[0113] Ultimately, the processor uses these weighted and fused state inputs to build a more accurate and stable multi-target observation system, thereby improving the accuracy of multi-target detection, recognition, and tracking.

[0114] Through the above methods, the present invention can effectively process target observation information constructed by frequency-modulated continuous wave radar and active radar tags. By using time correlation, observation stability and dynamic reliability correction, etc., the stability and accuracy of multi-target tracking can be improved, and it is suitable for radar target detection and tracking systems in complex environments.

[0115] The processor is used to: construct an extended state vector containing target position, target velocity, and corresponding covariance information for each observed target in a multi-target set; establish at least two target motion hypothesis models, including at least a uniform motion model and a motion model containing acceleration perturbation terms; calculate the matching degree of each target motion hypothesis model based on the target's historical state sequence, and determine the model probability corresponding to each target motion hypothesis model based on the matching degree; perform parallel prediction of the position and velocity of each observed target at multiple future prediction times based on each motion hypothesis model and its corresponding model probability, and synchronously propagate covariance information to obtain the prediction uncertainty range at each prediction time; perform probability-weighted fusion of the prediction results under different motion hypothesis models to obtain the comprehensive prediction state of each target at each prediction time; and synchronously predict the position of the crane at the corresponding prediction time based on the crane's own operating state model, thereby obtaining the relative prediction state distribution between each observed target and the crane.

[0116] For each observed target, an extended state vector is first constructed based on its observation information (such as target position, velocity, acceleration, etc.), which includes the target position, target velocity, and corresponding covariance information. This state vector not only includes the target's current motion state but also the uncertainty of the target's state, i.e., covariance information.

[0117] In the process of predicting the motion of the target, at least two motion hypothesis models are established: Uniform motion model: It is assumed that the target's velocity remains constant in the future, and its position changes linearly according to the current velocity.

[0118] Acceleration perturbation model: It is assumed that the motion of the target is subject to acceleration perturbation. The motion state of the target in the future is not only affected by the current velocity, but also takes into account the acceleration perturbation term.

[0119] The selection of these models is based on an understanding of the target's motion characteristics and analysis of historical observation data. Each model contains different state dynamic equations and noise characteristics, thus requiring Kalman filtering or similar filtering algorithms to estimate the target's state.

[0120] Based on historical state sequences, for each target, the fit of each motion hypothesis model is evaluated in historical data. The fit is typically calculated using least squares or a probabilistic evaluation method based on Bayesian inference. By comparing the differences between historical and predicted states, the goodness of fit or error distribution of each model is calculated, and the model's fit is evaluated accordingly.

[0121] Based on historical data and the current state, combined with the Kalman gain matrix, a matching degree index can be calculated for each model, thereby determining the probability of each model (i.e., model probability). Generally, the model probability is proportional to the matching degree; models with a higher matching degree have a higher probability.

[0122] Based on multiple motion hypothesis models and their corresponding model probabilities for each target, parallel predictions are performed at multiple future time points. At each time point, predictions are made according to different models, yielding target position and velocity predictions under different models. For each motion hypothesis model, the target's state can be predicted using a state transition matrix (or motion model).

[0123] At the same time, each model will synchronously propagate covariance information and update the covariance matrix through algorithms such as Kalman filtering or extended Kalman filtering (EKF) to reflect the range of uncertainty in the prediction at each time step.

[0124] The prediction results under different motion hypothesis models are fused using a weighted average method, with the weight coefficients being the probabilities of each model. That is, the prediction results of different models are weighted according to the probability of each model to obtain a comprehensive predicted state.

[0125] When predicting the future state of each observed target, the crane's own motion state must also be considered. Based on the crane's own operational state model, the crane's position at the corresponding prediction time is predicted synchronously. The crane's state model typically includes parameters such as position, velocity, and acceleration, and this model makes predictions based on the crane's control input and dynamic characteristics. The synchronous prediction process uses the crane's current state, control commands, and system noise to calculate the position and velocity at future times.

[0126] Finally, based on the comprehensive predicted state of each target and the predicted state of the overhead crane, the relative predicted state between each target and the overhead crane is calculated.

[0127] Specifically, for each observed target, at each predicted time, the relative position and velocity between the target and the crane are calculated, and combined with uncertainty information (such as the covariance matrix), a predicted distribution of the relative state is generated. This predicted relative state distribution is a probability distribution of the relative position and velocity between the crane and each observed target, reflecting the dynamic relationships that may occur in future time periods.

[0128] Thus, by constructing an extended target state vector, establishing multiple target motion hypothesis models, calculating the matching degree of each model, and performing parallel predictions, the comprehensive predicted state of each target is finally obtained through probability-weighted fusion. Simultaneously, by combining the crane's own motion state model, the relative state distribution between the target and the crane is accurately predicted, exhibiting high prediction accuracy and reliability, and effectively supporting multi-target prediction and navigation in complex scenarios.

[0129] Furthermore, the processor is used to: calculate the collision probability value between each observed target and the overhead crane at each prediction time based on the relative predicted state distribution, the collision probability value being determined by the predicted relative distance distribution between each observed target and the overhead crane and a preset safety distance threshold; construct a target time-series risk sequence for the collision probability values ​​corresponding to the same observed target at multiple prediction times; perform time-weighted accumulation processing on the target time-series risk sequence to obtain a comprehensive risk index for the target within the prediction time window, where the prediction risk weight closer to the current time is greater than the prediction risk weight further ahead; and, in conjunction with the prediction uncertainty range corresponding to each target, perform risk confidence constraint judgment on the comprehensive risk index. When the comprehensive risk index of a target exceeds a preset risk threshold under the condition of satisfying the risk confidence constraint, the observed target is identified as a potential collision target.

[0130] For example, the processor first calculates the collision probability value corresponding to each prediction time based on the relative predicted state distribution. Specifically, at each prediction time, the processor obtains the predicted relative distance distribution between each observed target and the crane, and compares it with a preset safe distance threshold to obtain the collision probability value between each observed target and the crane.

[0131] At each prediction time, based on the relative distance distribution between the observed target and the overhead crane, it is determined whether the predicted distance is less than the preset safe distance threshold. If the distance is less than the safety threshold, the collision probability is non-zero; otherwise, it is zero.

[0132] The processor constructs a temporal risk sequence for the same observed target based on its collision probability values ​​at multiple prediction times. This temporal risk sequence reflects the changes in the target's collision risk at each prediction time.

[0133] For each observed target, its collision probability value at multiple predicted times is collected; the collision probability values ​​at each time are arranged in chronological order to form a temporal risk sequence of the target.

[0134] The processor further performs time-weighted accumulation processing on the target time-series risk sequence to obtain a comprehensive risk index for the target over the entire prediction time window. The weighted accumulation processing steps are as follows: Each collision probability value in the time-series risk sequence is weighted according to its time distance from the current moment; the risk weight of the current moment is larger, and the risk weight of the future moment is smaller; the weighted collision probability values ​​are accumulated to obtain the comprehensive risk index of the target within the prediction time window.

[0135] After calculating the comprehensive risk index of the target, the processor further determines the risk confidence constraint by combining the prediction uncertainty range corresponding to each observed target. The specific method is as follows: The range of prediction uncertainty reflects the range of changes in the target's position and velocity within a given time window; The confidence level of the comprehensive risk indicators is adjusted based on the range of forecast uncertainty to ensure the accuracy of risk assessment.

[0136] Finally, when the comprehensive risk index of a target exceeds a preset risk threshold under the condition of satisfying preset risk confidence constraints, the processor identifies the observed target as a potential collision target. This determination process includes: If the comprehensive risk index exceeds the preset risk threshold and meets the preset risk confidence constraint, then the target is considered to have a high collision risk. The target is marked as a potential collision target, and further warning or avoidance measures are taken.

[0137] Through the above implementation method, the processor can effectively assess the collision risk of each observed target and make real-time judgments based on the time-series risk sequence and comprehensive risk indicators, providing decision support for the crane system to prevent potential collision risks.

[0138] See Figure 3 The illustrated embodiment of the present invention provides a ranging scenario diagram. A target position (i.e., an obstacle avoidance position) is located on the travel path of the overhead crane 30 (e.g., the overhead crane 30 is positioned on guide rail 33). The overhead crane 30 is equipped with a frequency-modulated continuous wave radar 31, and an active radar tag 32 is positioned at the target position. Through the interaction between the frequency-modulated continuous wave radar 31 and the active radar tag 32, the position of the active radar tag 32 and the relative distance between the overhead crane 30 and the target position where the active radar tag 32 is located can be determined in real time.

[0139] It should be noted that, Figure 3 This is for illustrative purposes only and does not represent the actual setup method.

[0140] The ranging system has been described above with reference to embodiments. To facilitate understanding and implementation by those skilled in the art, the ranging method corresponding to the ranging system is further described below.

[0141] See Figure 4 The flowchart shown in this embodiment of the invention provides a ranging method, as follows: Figure 4 As shown, the ranging method is applied to the ranging system described in any of the foregoing embodiments. The ranging system includes a frequency-modulated continuous wave radar and at least one active radar tag (see...). Figure 1 Distance measurement methods include: S401, along the direction of travel of the overhead crane, uses frequency-modulated continuous wave radar to transmit detection signals; the frequency-modulated continuous wave radar is installed on the overhead crane.

[0142] S402, at least one active radar tag is used to receive the detection signal, modulate the detection signal, generate and transmit a response signal with a preset frequency offset; wherein, at least one active radar tag is set at the target position, and the target position is set along the direction of travel of the crane.

[0143] S403 receives the response signal and uses frequency-modulated continuous wave radar to detect the frequency modulation characteristics of the response signal to determine the valid echo signal.

[0144] S404, based on the beat frequency signal between the echo signal and the detection signal, determines the distance information and / or velocity information between the target position and the target position.

[0145] For more details on distance measurement methods, please refer to the section on distance measurement systems in the aforementioned examples.

[0146] While the embodiments disclosed in this specification are as described above, the present invention is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A ranging system, characterized in that, Includes a frequency-modulated continuous wave radar and at least one active radar tag, wherein: The frequency-modulated continuous wave radar, mounted on the overhead crane, is used to transmit detection signals and receive at least the echo signals emitted from the active radar tag while the overhead crane is in motion. At least one of the active radar tags is disposed at the target location to receive the detection signal, modulate the detection signal, generate and transmit a response signal with a preset frequency offset; wherein the target location is disposed along the travel direction of the overhead crane; The frequency-modulated continuous wave radar is used to determine the effective echo signal by detecting the frequency modulation characteristics of the response signal, and to determine the distance information and / or velocity information between the echo signal and the target position based on the beat frequency signal between the echo signal and the detection signal.

2. The ranging system according to claim 1, characterized in that, At least one of the said active radar tags includes: A receiving unit is configured to receive the detection signal and amplify the detection signal to obtain an amplified detection signal; A modulation unit is configured to, upon receiving the amplified detection signal, mix the amplified detection signal with its own generated intermediate frequency signal, such that the frequency of the mixed signal is the superposition of the frequency of the amplified detection signal and the frequency of the intermediate frequency signal; wherein the frequency of the intermediate frequency signal is the preset frequency; The transmitting unit is used to amplify the power of the mixed signal and then transmit the response signal.

3. The ranging system according to claim 2, characterized in that, The modulation unit includes a mixer, which performs frequency conversion and superposition of the received amplified detection signal and the intermediate frequency signal generated by the local oscillator to obtain the mixed signal.

4. The ranging system according to claim 2 or 3, characterized in that, When there are multiple active radar tags, each active radar tag is set at a corresponding target location, and each active radar tag is configured with its own corresponding identification information, and / or configured so that the frequency of the mixing signal generated by each active radar tag is different.

5. The ranging system according to claim 4, characterized in that, The frequency-modulated continuous wave radar includes an antenna array for direction finding, used to determine the spatial direction of arrival corresponding to each response signal when a response signal of each active radar tag is received.

6. The ranging system according to claim 1, characterized in that, The ranging system further includes: a processor coupled to the frequency modulated continuous wave radar and the active radar tag respectively; The processor is used to acquire distance and velocity information of multiple targets detected by the frequency-modulated continuous wave radar, and construct a multi-target set; wherein, the multiple targets include at least targets formed by the target positions corresponding to the active radar tags, and include environmental scattering targets detected by the frequency-modulated continuous wave radar; based on the multi-target set and the crane's own operating state, the processor predicts the relative motion state of each target and the crane at multiple future moments, and based on the predicted relative motion state, assesses the collision risk of each target to determine potential collision targets.

7. The ranging system according to claim 6, characterized in that, The processor is used for: A target observation structure is constructed for each target location corresponding to the active radar tag and for the environmental scattering target detected by the frequency-modulated continuous wave radar. The target observation structure includes at least the target range, radial velocity, signal-to-noise ratio, detection timestamp, and target category identifier. Based on the target category identifier, the observed targets are divided into active radar tag target class and environmental scattering target class, and different initial confidence weights are set for different categories of observed targets; Within multiple consecutive detection cycles, based on the continuity of distance and velocity changes, the observation results of each observed target are time-correlated and matched to form a target observation sequence. For each of the target observation sequences, the observation stability index of the corresponding observation target is calculated. The observation stability index includes at least the distance variance, velocity variance, and detection missing rate. The initial confidence weight is dynamically corrected based on the observation stability index, and the target distance information and velocity information are weighted and fused according to the corrected confidence weight to form a unified state input quantity for each observed target in the multi-target set.

8. The ranging system according to claim 6, characterized in that, The processor is used for: For each observed target in the multi-target set, an extended state vector containing target position, target velocity, and corresponding covariance information is constructed. Establish at least two target motion assumption models, wherein the target motion assumption models include at least a uniform motion model and a motion model containing an acceleration perturbation term; The matching degree of each target motion hypothesis model is calculated based on the target historical state sequence, and the model probability corresponding to each target motion hypothesis model is determined by the matching degree. Based on each motion hypothesis model and its corresponding model probability, the position and velocity of each observed target are predicted in parallel at multiple future prediction times, and the covariance information is propagated synchronously to obtain the prediction uncertainty range at each prediction time. The prediction results under different motion assumption models are fused by probability weighting to obtain the comprehensive prediction state of each target at each prediction time. Based on the crane's own operating state model, the position of the crane at the corresponding prediction time is predicted synchronously, thereby obtaining the relative predicted state distribution between each observation target and the crane.

9. The ranging system according to claim 8, characterized in that, The processor is used for: Based on the relative predicted state distribution, the collision probability value between each observed target and the crane is calculated at each predicted time. The collision probability value is determined by the predicted relative distance distribution between each observed target and the crane and a preset safe distance threshold. A target temporal risk sequence is constructed for the collision probability values ​​of the same observed target at multiple prediction times; The target time-series risk sequence is subjected to time-weighted cumulative processing to obtain a comprehensive risk index of the target within the prediction time window, wherein the prediction risk weight closer to the current moment is greater than the prediction risk weight in the long term. Based on the prediction uncertainty range corresponding to each target, the comprehensive risk index is subject to risk confidence constraint judgment. When the comprehensive risk index of a target exceeds a preset risk threshold under the condition of satisfying the risk confidence constraint, the observed target is identified as a potential collision target.

10. A distance measurement method, characterized in that, Applied to the ranging system according to any one of claims 1 to 9, the ranging system comprising a frequency-modulated continuous wave radar and at least one active radar tag, the ranging method comprising: Along the direction of travel of the overhead crane, the frequency-modulated continuous wave radar is used to transmit detection signals; wherein, the frequency-modulated continuous wave radar is installed on the overhead crane; The detection signal is received by at least one of the active radar tags, and the detection signal is modulated to generate and transmit a response signal with a preset frequency offset; wherein at least one of the active radar tags is disposed at the target position, and the target position is disposed along the travel direction of the overhead crane; The response signal is received, and the frequency modulation characteristics of the response signal are detected using the frequency-modulated continuous wave radar to determine the valid echo signal. Based on the beat frequency signal between the echo signal and the detection signal, the distance information and / or velocity information between the target position and the target position are determined.

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