Obstacle detection method and system based on frequency modulated continuous wave laser radar

Abstract

The present invention relates to the field of laser radar technology, and in particular to a method and system for obstacle detection based on frequency-modulated continuous wave laser radar. The method comprises transmitting a chaotic frequency-modulated continuous wave signal into a detection area to obtain a reflected echo signal of a target object; performing decoupling calculations on a difference frequency electrical signal to obtain a target time-frequency distribution matrix; predicting the Doppler frequency shift of the target object at the next moment based on the target time-frequency distribution matrix; obtaining a ranging deviation compensation amount based on a chaotic frequency-modulated adjustment signal; the chaotic frequency-modulated adjustment signal is obtained by dynamically adjusting the chaotic frequency-modulated slope of the chaotic frequency-modulated continuous wave signal by the Doppler frequency shift amount; and compensating the frequency value of the target object difference frequency electrical signal based on the ranging deviation compensation amount and environmental noise data to calculate obstacle distance and speed detection information. The present invention achieves precise compensation for ranging deviation by predicting the Doppler frequency shift amount in real time and dynamically adjusting the parameters of the chaotic frequency-modulated continuous wave signal, thereby improving the accuracy of obstacle distance and speed detection.

Description

Technical Field

[0001] The present invention relates to the field of laser radar technology, and in particular to an obstacle detection method and system based on frequency modulated continuous wave laser radar. Background Art

[0002] Obstacle detection technology plays a vital role in many fields such as autonomous driving, robot navigation, and security monitoring. As a high-precision detection device, frequency-modulated continuous-wave lidar has been widely used in obstacle detection due to its advantages such as high resolution and strong anti-interference ability.

[0003] However, the existing obstacle detection methods based on frequency-modulated continuous-wave lidar still have some problems that need to be solved urgently. During the detection process, when the target object is in motion, the frequency of the echo signal received by the lidar will shift due to the influence of the Doppler effect. This frequency shift will cause deviations in the ranging results, thereby affecting the accuracy of obstacle distance measurement. Especially when the target moves at a fast speed or in a complex direction, the ranging deviation caused by the frequency shift will be more obvious, reducing the reliability of obstacle detection. In addition, when dealing with Doppler frequency shift, the existing methods often lack real-time and dynamic adjustment capabilities. Traditional frequency shift compensation methods are usually based on fixed parameters or preset models, which are difficult to flexibly adjust according to the real-time motion state of the target. As a result, in practical applications, when the target motion state changes, the compensation effect is poor, and the ranging deviation caused by the frequency shift cannot be effectively eliminated, thereby affecting the accuracy and efficiency of obstacle detection.

[0004] In summary, the existing obstacle detection methods have many shortcomings. There is an urgent need for an obstacle detection method based on frequency-modulated continuous-wave lidar that can predict the Doppler frequency shift in real time and dynamically adjust signal parameters to compensate for ranging deviations, thereby improving detection accuracy and reliability in complex scenarios. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides an obstacle detection method and system based on frequency modulated continuous wave laser radar.

[0006] In a first aspect, the present invention provides an obstacle detection method based on a frequency modulated continuous wave laser radar, the method comprising the following steps:

[0007] Transmitting a chaotic frequency-modulated continuous wave signal with non-periodic characteristics to the detection area to obtain the reflected echo signal of the target object;

[0008] Performing decoupling calculation on the difference frequency electrical signal to obtain a target time-frequency distribution matrix; wherein the difference frequency electrical signal is obtained by extracting the reflected echo signal and the chaotic frequency modulated continuous wave signal by coherent optical mixing;

[0009] Predicting the Doppler frequency shift of the target object at the next moment according to the target time-frequency distribution matrix;

[0010] Based on the chaotic frequency modulation adjustment signal, a ranging deviation compensation amount is calculated; wherein the chaotic frequency modulation adjustment signal is obtained by dynamically adjusting the chaotic frequency modulation slope of the chaotic frequency modulation continuous wave signal by the Doppler frequency shift amount;

[0011] The target object difference frequency electric signal frequency value is compensated according to the ranging deviation compensation amount and the environmental noise data to calculate the obstacle distance and speed detection information.

[0012] In a further embodiment, the step of emitting a chaotic frequency-modulated continuous wave signal having a non-periodic characteristic to the detection area to obtain a reflected echo signal of the target object includes:

[0013] Generate an initial chaotic sequence with non-periodic characteristics, and dynamically adjust the instantaneous frequency offset of the initial chaotic sequence through a phase modulator to generate a chaotic frequency-modulated continuous wave signal;

[0014] The chaotic frequency-modulated continuous wave signal is modulated by adopting a hybrid modulation method combining orthogonal frequency division multiplexing and high-order orthogonal amplitude modulation to obtain a modulated chaotic frequency-modulated continuous wave signal;

[0015] The modulated chaotic frequency-modulated continuous wave signal is loaded onto a multi-beam transducer array for space-time coding, and a chaotic encrypted positioning pilot sequence is embedded into the chaotic frequency-modulated continuous wave signal loaded onto the multi-beam transducer array to obtain a chaotic frequency-modulated continuous wave transmission signal;

[0016] The chaotic frequency-modulated continuous wave transmission signal is directionally transmitted to the detection area through the multi-beam transducer array, and the reflected echo signal of the target object reflected from the detection area is received.

[0017] In a further embodiment, the process of acquiring the difference frequency electrical signal is specifically as follows:

[0018] performing time domain alignment on the reflected echo signal according to timestamp information of the reflected echo signal and the chaotic frequency modulation continuous wave signal to obtain a target reflected echo time domain aligned signal;

[0019] Using a mixer to perform coherent optical mixing on the target reflected echo time-domain aligned signal and the chaotic frequency-modulated continuous wave signal to obtain an original mixed signal;

[0020] The high-frequency component in the original mixed signal is filtered out by a low-pass filter to extract the difference frequency electrical signal.

[0021] In a further embodiment, the target time-frequency distribution matrix is ​​extracted by a deep neural network, and the deep neural network includes a first channel branch and a second channel branch adopting a dual-channel parallel branch architecture, the first channel branch is a branch structure composed of a cascade of a long short-term memory network and a three-dimensional convolutional neural network, and the second channel branch is a branch structure based on fractional Fourier transform.

[0022] In a further embodiment, the step of performing decoupling calculation on the difference frequency electrical signal to obtain a target time-frequency distribution matrix includes:

[0023] According to the amplitude range of the difference frequency electrical signal, a maximum-minimum value normalization algorithm is used to normalize the difference frequency electrical signal to obtain a standardized difference frequency electrical signal;

[0024] Capturing the temporal dependency in the standardized difference frequency electrical signal through a long short-term memory network and extracting the time domain features of the difference frequency electrical signal;

[0025] Using a three-dimensional convolutional neural network to perform a space-frequency domain convolution operation on the time domain features of the difference frequency electrical signal to obtain a space-time frequency joint feature vector;

[0026] Inputting the joint time-space-frequency feature vector into a fully connected layer for nonlinear transformation to obtain a channel state parameter vector;

[0027] Based on the channel state parameter vector, an optimal fractional Fourier transform order is calculated using a gradient descent method, and a fractional Fourier transform is performed on the standardized difference frequency electrical signal according to the optimal fractional Fourier transform order to obtain a preliminary time-frequency distribution matrix;

[0028] A morphological filtering algorithm is used to perform spatial domain filtering on the preliminary time-frequency distribution matrix to obtain a target time-frequency distribution matrix.

[0029] In a further embodiment, the step of predicting the Doppler frequency shift of the target object at the next moment according to the target time-frequency distribution matrix is:

[0030] A peak search algorithm is used to search for energy concentration points in the target time-frequency distribution matrix and extract characteristic parameters of the energy concentration points; the characteristic parameters of the energy concentration points include the time center position, the frequency center value, the energy intensity and the time-frequency distribution variance;

[0031] A state vector of a Kalman filter is defined, and a state equation is established based on the state vector. The frequency center position and the time center position of the characteristic parameters of the energy accumulation point are used as observation variables to establish an observation equation; the state vector includes the target distance, radial velocity, and radial acceleration to be estimated;

[0032] Integrating the state equation and the observation equation to form a target motion state space model;

[0033] Based on the characteristic parameters of the energy accumulation point, the target motion state is initially estimated using the target motion state space model to obtain an initial state estimation value at the previous moment;

[0034] According to the target state estimation value at the previous moment and the state equation, the target state estimation value at the current moment is predicted by using a Kalman filter;

[0035] The radial velocity information is calculated according to the target state estimation value at the current moment, and the radial velocity information is converted into a frequency shift amount using the Doppler effect principle to obtain the Doppler frequency shift amount at the next moment.

[0036] In a further embodiment, the acquisition process of the chaotic frequency modulation adjustment signal is specifically as follows:

[0037] The chaotic frequency modulation continuous wave signal is processed by a spectrum analysis method to extract the initial chaotic frequency modulation slope;

[0038] Calculating a chaotic frequency modulation slope adjustment amount according to the Doppler frequency shift amount, and obtaining a chaotic frequency modulation slope correction value according to the chaotic frequency modulation slope adjustment amount and the initial chaotic frequency modulation slope;

[0039] According to the chaotic frequency modulation slope correction value and the preset signal center frequency, a chaotic frequency modulation adjustment signal is generated by utilizing a logistic chaotic map.

[0040] In a further embodiment, the step of calculating the ranging deviation compensation amount based on the chaotic frequency modulation adjustment signal includes:

[0041] Using Hilbert transform to extract the first instantaneous frequency sequence of the chaotic frequency modulation adjustment signal and the second instantaneous frequency sequence of the preset standard chaotic frequency modulation signal;

[0042] Performing time domain alignment on the first instantaneous frequency sequence and the second instantaneous frequency sequence to obtain corresponding first time domain aligned instantaneous frequency sequence and second time domain aligned instantaneous frequency sequence;

[0043] performing a point-by-point difference operation on the first time-domain aligned instantaneous frequency sequence and the second time-domain aligned instantaneous frequency sequence to obtain a frequency difference sequence;

[0044] The ranging deviation at each moment is calculated according to the frequency difference sequence and the laser wavelength to obtain the ranging deviation compensation amount.

[0045] In a further embodiment, the step of compensating the target object difference frequency signal frequency value according to the ranging deviation compensation amount and the environmental noise data to calculate the obstacle distance and speed detection information includes:

[0046] Collecting environmental noise data in real time, and performing preliminary compensation on the original frequency value of the difference frequency electrical signal according to the ranging deviation compensation amount to obtain a compensated frequency value of the difference frequency electrical signal;

[0047] Performing power spectral density estimation on the ambient noise data to obtain ambient noise power spectral density, and constructing an adaptive filter using a minimum mean square error algorithm based on the ambient noise power spectral density;

[0048] Using the adaptive filter to perform noise correction on the compensation frequency value of the difference frequency electrical signal to obtain a correction frequency value of the difference frequency electrical signal;

[0049] Performing a fractional Fourier transform on the correction frequency value of the difference frequency electrical signal to obtain a frequency offset characteristic of the difference frequency electrical signal in a fractional Fourier transform domain;

[0050] Calculating an initial distance estimate based on the frequency offset characteristics, frequency modulation bandwidth, and frequency modulation period of the difference frequency electrical signal, and calculating a target speed based on a deviation between a corrected frequency value of the difference frequency electrical signal and an original frequency value of the difference frequency electrical signal;

[0051] Obstacle distance and speed detection information is obtained based on the initial distance estimation value, the target speed, and the frequency modulated continuous wave laser radar geometric position.

[0052] In a second aspect, the present invention provides an obstacle detection system based on a frequency modulated continuous wave laser radar, the system comprising:

[0053] The signal acquisition module is used to transmit a chaotic frequency-modulated continuous wave signal with non-periodic characteristics to the detection area to obtain the reflected echo signal of the target object;

[0054] a difference frequency analysis module, configured to perform decoupling calculations on the difference frequency electrical signal to obtain a target time-frequency distribution matrix; wherein the difference frequency electrical signal is extracted by performing coherent optical mixing of the reflected echo signal and the chaotic frequency modulated continuous wave signal;

[0055] A frequency shift estimation module is used to predict the Doppler frequency shift of the target object at the next moment according to the target time-frequency distribution matrix;

[0056] A deviation compensation module is used to calculate a ranging deviation compensation amount based on a chaotic frequency modulation adjustment signal; wherein the chaotic frequency modulation adjustment signal is obtained by dynamically adjusting the chaotic frequency modulation slope of the chaotic frequency modulation continuous wave signal by the Doppler frequency shift amount;

[0057] The target detection module is used to compensate the target object's difference frequency signal frequency value according to the ranging deviation compensation amount and the environmental noise data to calculate the obstacle distance and speed detection information.

[0058] The present invention provides an obstacle detection method and system based on a frequency modulated continuous wave laser radar. The method transmits a chaotic frequency modulated continuous wave signal with non-periodic characteristics to a detection area to obtain a reflected echo signal of a target object; decouples and calculates a difference frequency electrical signal to obtain a target time-frequency distribution matrix; wherein the difference frequency electrical signal is obtained by extracting the reflected echo signal and the chaotic frequency modulated continuous wave signal through coherent optical mixing; predicts the Doppler frequency shift of the target object at the next moment based on the target time-frequency distribution matrix; calculates a ranging deviation compensation amount based on a chaotic frequency modulation adjustment signal; wherein the chaotic frequency modulation adjustment signal is obtained by dynamically adjusting the chaotic frequency modulation slope of the chaotic frequency modulated continuous wave signal according to the Doppler frequency shift amount; and compensates the frequency value of the target object difference frequency electrical signal based on the ranging deviation compensation amount and environmental noise data to calculate obstacle distance and speed detection information. Compared with existing technologies, this method achieves precise compensation for ranging deviation by real-time prediction of Doppler frequency shift and dynamic adjustment of chaotic frequency-modulated continuous wave signal parameters, significantly improving the accuracy of obstacle distance and speed detection in complex scenarios, and meeting the needs of high-dynamic scenarios such as autonomous driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 1 is a flow chart of an obstacle detection method based on a frequency modulated continuous wave laser radar provided in an embodiment of the present invention;

[0060] Figure 2 This is a block diagram of an obstacle detection system based on a frequency modulated continuous wave lidar provided in an embodiment of the present invention.

[0061] Explanation of reference numerals: 101, signal acquisition module; 102, difference frequency analysis module; 103, frequency shift estimation module; 104, deviation compensation module; 105, target detection module. DETAILED DESCRIPTION

[0062] The following describes the embodiments of the present invention in detail with reference to the accompanying drawings. The embodiments are provided for illustrative purposes only and are not to be construed as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of protection of the present invention. Many changes may be made to the present invention without departing from the spirit and scope of the present invention.

[0063] Figure 1: is a flow chart of an obstacle detection method based on a frequency modulated continuous wave laser radar provided by an embodiment of the present invention. An obstacle detection method based on a frequency modulated continuous wave laser radar is provided by an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0064] S1. Transmit a chaotic frequency-modulated continuous wave signal with non-periodic characteristics to the detection area to obtain the reflected echo signal of the target object.

[0065] In some embodiments, the step of emitting a chaotic frequency-modulated continuous wave signal having a non-periodic characteristic to the detection area to obtain a reflected echo signal of the target object includes:

[0066] A chaotic sequence generator is used to generate an initial chaotic sequence with non-periodic characteristics, and a phase modulator is used to dynamically adjust the instantaneous frequency offset of the initial chaotic sequence to generate a chaotic frequency modulated continuous wave signal;

[0067] The chaotic frequency-modulated continuous wave signal is modulated by adopting a hybrid modulation method combining orthogonal frequency division multiplexing and high-order orthogonal amplitude modulation to obtain a modulated chaotic frequency-modulated continuous wave signal;

[0068] The modulated chaotic frequency-modulated continuous wave signal is loaded onto a multi-beam transducer array for space-time coding, and a chaotic encrypted positioning pilot sequence is embedded into the chaotic frequency-modulated continuous wave signal loaded onto the multi-beam transducer array to obtain a chaotic frequency-modulated continuous wave transmission signal;

[0069] The chaotic frequency-modulated continuous wave transmission signal is directionally transmitted to the detection area through the multi-beam transducer array, and the reflected echo signal of the target object reflected from the detection area is received.

[0070] Specifically, this embodiment uses the logistic mapping algorithm as a chaotic sequence generator, and uses the logistic mapping to generate an initial chaotic sequence with non-periodic characteristics. After receiving the initial chaotic sequence, the phase modulator maps the value of the initial chaotic sequence to an instantaneous frequency offset, and modulates the phase of the initial chaotic sequence continuous wave laser according to the instantaneous frequency offset, thereby generating a chaotic frequency-modulated continuous wave signal according to the instantaneous frequency of the modulated laser signal. Since orthogonal frequency division multiplexing is a multi-carrier modulation technology, it divides a high-speed data stream into multiple low-speed sub-data streams, and then modulates these sub-data streams to multiple mutually orthogonal sub-carriers. The chaotic frequency modulation continuous wave signal is transmitted on the network, which can improve the anti-interference ability and spectrum utilization of the signal. Therefore, in this embodiment, the generated chaotic frequency modulation continuous wave signal is segmented according to the preset time interval to obtain multiple signal segments, and each signal segment is subjected to fast Fourier transform to convert each signal segment into a frequency domain signal. The spectrum is divided into multiple subcarriers in the frequency domain, and each subcarrier corresponds to a sub-data stream. In this embodiment, the spectrum energy of the chaotic frequency modulation continuous wave signal is distributed to each subcarrier according to the frequency characteristics of each subcarrier, thereby obtaining a signal after orthogonal frequency division multiplexing processing, and the signal after orthogonal frequency division multiplexing processing is distributed in multiple subcarriers in the frequency domain. On orthogonal subcarriers, for the signal on each subcarrier after orthogonal frequency division multiplexing processing, this embodiment performs modulation according to the constellation diagram rule of high-order orthogonal amplitude modulation. For example, for 16-QAM modulation, there are sixteen constellation points on the constellation diagram, and each constellation point corresponds to four bits of information. This embodiment adjusts the signal amplitude and phase on the subcarrier to the corresponding constellation point position so that the signal contains specific bit information. Specifically, this embodiment finds the corresponding constellation point in the constellation diagram according to the bit sequence to be transmitted, and then adjusts the signal amplitude and phase on the subcarrier to match the amplitude and phase of the constellation point, thereby obtaining The method obtains a signal processed by high-order orthogonal amplitude modulation, and performs inverse fast Fourier transform on each subcarrier signal processed by high-order orthogonal amplitude modulation, converts the frequency domain signal back into a time domain signal, and after completing orthogonal frequency division multiplexing and high-order orthogonal amplitude modulation of each subcarrier, merges the signals of all subcarriers to finally obtain a chaotic frequency modulated continuous wave signal after mixed modulation. This embodiment combines orthogonal frequency division multiplexing and high-order orthogonal amplitude modulation, fully utilizing the anti-interference and spectrum utilization advantages of orthogonal frequency division multiplexing and the high data transmission rate advantage of high-order orthogonal amplitude modulation, thereby improving the transmission performance of the chaotic frequency modulated continuous wave signal.

[0071] Space-time coding is a coding technology that combines the space domain and the time domain. It uses space diversity and time diversity to improve the reliability and transmission efficiency of the signal by sending the encoded signal simultaneously on multiple antennas (or transducers). The multi-beam transducer array is composed of multiple transducer units, which are arranged according to a preset geometric layout, such as a linear array or a circular array. Each transducer can be regarded as an antenna. Space-time coding can be achieved by encoding the signals sent from different transducers. In this embodiment, the modulated chaotic frequency-modulated continuous wave signal is distributed to each transducer of the multi-beam transducer array according to a preset coding rule. For example, this embodiment can adopt the Alamouti space-time coding method. The coding scheme divides the modulated chaotic frequency-modulated continuous wave signal into two time slots. In the first time slot, the modulated chaotic frequency-modulated continuous wave signal is sent to the two transducers respectively; in the second time slot, the modulated chaotic frequency-modulated continuous wave signal is conjugated and negated, and then sent to the two transducers respectively, so that the original signal can be restored by performing corresponding decoding processing on the received signal at the receiving end, and the anti-interference ability of the signal is improved by using spatial diversity and time diversity, thereby obtaining the chaotic frequency-modulated continuous wave signal after space-time coding processing, and the chaotic frequency-modulated continuous wave signal after space-time coding processing is distributed to each transducer of the multi-beam transducer array in preparation for transmission.

[0072] At the same time, this embodiment generates a chaotically encrypted positioning pilot sequence. This positioning pilot sequence can be obtained by using different chaotic equations (such as the Lorenz system) or performing an exclusive-OR operation on the chaotic sequence generated by the logistic map, etc., to obtain an encrypted positioning pilot sequence. The encrypted positioning pilot sequence is then embedded into the chaotic frequency-modulated continuous wave signal after space-time coding. The embedding method can be to insert the encrypted positioning pilot sequence at a specific position in the signal. For example, this embodiment inserts an encrypted positioning pilot sequence at the beginning or end of the signal to obtain a chaotic frequency-modulated continuous wave transmission signal. The positioning pilot sequence proposed in this embodiment is used for signal synchronization and positioning at the receiving end. Chaotic encryption can improve the security of the positioning pilot sequence and prevent malicious interference or cracking. Chaotic encryption utilizes the characteristics of the chaotic system to encrypt the pilot sequence, making the encrypted pilot sequence random and unpredictable.

[0073] Finally, this embodiment calculates the emission phase and amplitude of each transducer in the multi-beam transducer array according to the direction and distance of the detection area. For example, this embodiment uses a beamforming algorithm to determine the phase difference of each transducer according to the direction of the detection area, so that the signals emitted by each transducer are coherently superimposed in the direction of the detection area to form a stronger beam. At the same time, the emission amplitude of each transducer is adjusted according to the distance of the detection area to ensure that the signal has sufficient energy in the detection area. The calculated emission phase and amplitude are set to each transducer of the multi-beam transducer array, and then each transducer is driven to emit a chaotic frequency modulated continuous wave emission signal. The transducer converts the chaotic frequency modulated continuous wave emission signal into an optical signal or an electromagnetic wave signal, and according to The multi-beam transducer array transmits the signal to the detection area in the set beam direction, and the transmitted signal propagates in space and covers various positions in the detection area, waiting for the target object to reflect the echo signal. After transmitting the signal, the multi-beam transducer array continuously receives the signal reflected from the detection area. After receiving the reflected echo signal, the transducer array converts the optical signal or electromagnetic wave signal into an electrical signal. Since the direction and intensity of the reflected signal are related to the position and characteristics of the obstacle, the received signal will contain this information. In this embodiment, the signal received by the multi-beam transducer array is pre-processed by amplification and filtering to improve the signal quality and signal-to-noise ratio, and the pre-processed signal is used as the reflected echo signal of the target object for subsequent signal processing and obstacle detection.

[0074] S2. Decoupling calculation is performed on the difference frequency electrical signal to obtain a target time-frequency distribution matrix; wherein, the difference frequency electrical signal is obtained by coherently optically mixing the reflected echo signal with the chaotic frequency-modulated continuous wave signal.

[0075] In some embodiments, the process of acquiring the difference frequency electrical signal is specifically as follows:

[0076] performing time domain alignment on the reflected echo signal according to timestamp information of the reflected echo signal and the chaotic frequency modulation continuous wave signal to obtain a target reflected echo time domain aligned signal;

[0077] Using a mixer to perform coherent optical mixing on the target reflected echo time-domain aligned signal and the chaotic frequency-modulated continuous wave signal to obtain an original mixed signal;

[0078] The high-frequency component in the original mixed signal is filtered out by a low-pass filter to extract the difference frequency electrical signal.

[0079] Since the reflected echo signal of the target object will have a certain time delay during the propagation process, there will be a time offset compared with the originally transmitted chaotic frequency modulation continuous wave signal. Therefore, this embodiment uses the timestamp information to time-domain align the reflected echo signal of the target object. Specifically, the chaotic frequency modulation continuous wave signal in this embodiment uses a timestamp to indicate the transmission time of the signal when it is transmitted. Similarly, the reflected echo signal of the target object uses a timestamp to indicate the reception time of the signal when it is received. The difference between the two timestamps is the propagation delay of the signal. This embodiment calculates the reflected echo signal of the target object based on the transmitted and received timestamp information. The time delay of the target object's reflected echo signal relative to the chaotic frequency-modulated continuous wave signal is calculated, and according to the calculated time delay, the reflected echo signal of the target object is shifted in the time domain. If the time delay is positive, it means that the reflected echo signal of the target object is delayed relative to the chaotic frequency-modulated continuous wave signal, and the reflected echo signal of the target object needs to be shifted leftward on the time axis by the time delay. If the time delay is negative, it means that the reflected echo signal of the target object arrives in advance. In this embodiment, the target reflected echo time domain alignment signal can be obtained by buffering or filling zero values, and the target reflected echo time domain alignment signal is aligned with the chaotic frequency-modulated continuous wave signal in the time dimension.

[0080] Coherent optical mixing uses a mixer to multiply two signals to obtain a mixed signal containing sum frequency components and difference frequency components. In the lidar system, coherent mixing can be used to extract the difference frequency signal reflecting the target distance and speed information. Therefore, in this embodiment, the reflected echo signal of the target object after time domain alignment and the original chaotic frequency modulated continuous wave signal are input to the two input ports of the mixer respectively. The mixer performs multiplication on the two input signals. Assuming that the reflected echo signal of the target object is , the chaotic FMCW signal is , then the output signal after mixing is , this multiplication operation will produce the sum frequency component Sum and difference frequency components There are two main frequency components. In this embodiment, the output of the mixer is the original mixed signal, which contains two frequency components: the sum frequency component and the difference frequency component. The difference frequency component includes key information about the target distance and speed. Next, this embodiment sets the cutoff frequency of the low-pass filter according to the preset frequency range of the difference frequency electrical signal. The cutoff frequency should be set above the highest frequency of the difference frequency electrical signal to ensure that the difference frequency signal can pass through while filtering out all high-frequency components higher than this frequency. In this embodiment, the original mixed signal is input into the low-pass filter. The low-pass filter performs frequency analysis on the input original mixed signal, allowing signals with frequencies lower than the cutoff frequency to pass through, while suppressing signals with frequencies higher than the cutoff frequency, thereby retaining the difference frequency component (low frequency) in the original mixed signal and filtering out the sum frequency component (high frequency). After processing by the low-pass filter, the difference frequency electrical signal is output. The difference frequency electrical signal can reflect the distance and speed information between the target and the laser radar.

[0081] In some embodiments, the target time-frequency distribution matrix is ​​extracted by a deep neural network, and the deep neural network includes a first channel branch and a second channel branch using a dual-channel parallel branch architecture, wherein the first channel branch is a branch structure composed of a cascade of a long short-term memory network and a three-dimensional convolutional neural network, and the second channel branch is a branch structure based on a fractional Fourier transform. In this embodiment, the step of performing decoupling calculation on the difference frequency electrical signal to obtain the target time-frequency distribution matrix includes:

[0082] According to the amplitude range of the difference frequency electrical signal, a maximum-minimum value normalization algorithm is used to normalize the difference frequency electrical signal to obtain a standardized difference frequency electrical signal;

[0083] Capturing the temporal dependency in the standardized difference frequency electrical signal through a long short-term memory network and extracting the time domain features of the difference frequency electrical signal;

[0084] Using a three-dimensional convolutional neural network to perform a space-frequency domain convolution operation on the time domain features of the difference frequency electrical signal to obtain a space-time frequency joint feature vector;

[0085] Inputting the joint time-space-frequency feature vector into a fully connected layer for nonlinear transformation to obtain a channel state parameter vector;

[0086] Based on the channel state parameter vector, an optimal fractional Fourier transform order is calculated using a gradient descent method, and a fractional Fourier transform is performed on the standardized difference frequency electrical signal according to the optimal fractional Fourier transform order to obtain a preliminary time-frequency distribution matrix;

[0087] A morphological filtering algorithm is used to perform spatial domain filtering on the preliminary time-frequency distribution matrix to obtain a target time-frequency distribution matrix.

[0088] Specifically, this embodiment traverses all data points of the difference frequency electrical signal to find the maximum amplitude value and the minimum amplitude value of the difference frequency electrical signal within a preset observation window. This embodiment can set the observation window length to twice the current frequency modulation period, and normalize the amplitude of each data point in the difference frequency electrical signal according to the maximum amplitude value and the minimum amplitude value to obtain a standardized difference frequency electrical signal. At the same time, this embodiment constructs a deep neural network. The deep neural network adopts a dual-channel parallel branch architecture. The first channel branch adopts a cascade branch structure of a long short-term memory network and a three-dimensional convolutional neural network, and the second channel branch adopts a branch structure based on a fractional-order Fourier transform. The cascade branch structure of the long short-term memory network and the three-dimensional convolutional neural network includes a cascade-connected long short-term memory network and a three-dimensional convolutional neural network. The long short-term memory network is constructed according to the timing characteristics of the difference frequency electrical signal. The long short-term memory network is composed of multiple long short-term memory units. Each long short-term memory unit includes an input gate, a forget gate, and an output gate. The input gate controls the degree to which new information enters the unit, the forget gate determines the degree to which old information is forgotten, and the output gate Control the contribution of the unit state to the output. During the training process, this embodiment can adjust the weights of these gates through a large amount of time series data, so that the long short-term memory network can capture the long-term dependencies in the time series data; this embodiment cascades a three-dimensional convolutional neural network after the long short-term memory network. The three-dimensional convolutional neural network includes multiple three-dimensional convolutional layers and pooling layers. The three-dimensional convolutional layer uses a three-dimensional convolution kernel to perform convolution operations on the time dimension, frequency dimension and spatial dimension of the data to extract the spatiotemporal features of the data, wherein the width of the time convolution kernel corresponds to three frequency modulation cycles, and the frequency convolution kernel covers seven adjacent frequency points. The pooling layer is used to reduce the dimension and computational complexity of the data while retaining important features. The three-dimensional convolutional neural network can further perform spatial-frequency domain feature extraction on the features output by the long short-term memory network; the second channel branch mainly processes the signal based on the fractional-order Fourier transform. The fractional-order Fourier transform is a generalized Fourier transform that can better handle the time-frequency localization problem. By performing a fractional-order Fourier transform operation on the input difference frequency electrical signal, the features of the signal in the fractional-order Fourier domain are extracted.

[0089] In this embodiment, the standardized difference frequency electrical signal is input into the long short-term memory network according to the time step. The long short-term memory network processes the input standardized difference frequency electrical signal time step by time step through its internal forget gate, input gate and output gate mechanism. The output of each time step depends not only on the current input, but also on the hidden state of the previous time step, thereby capturing the temporal dependency. After processing by the long short-term memory network, the time domain features of the difference frequency electrical signal containing multi-cycle dependencies are obtained. These time domain features of the difference frequency electrical signal reflect the change law and dependency of the signal in the time dimension. Then, in this embodiment, the time domain features of the difference frequency electrical signal output by the long short-term memory network are used as input, and the three-dimensional convolution layer of the three-dimensional convolutional neural network is used to perform convolution operations in the space-frequency domain. The three-dimensional convolutional neural network is provided with multiple three-dimensional convolution kernels. Each three-dimensional convolution kernel slides in the three dimensions of time, space and frequency, and performs weighted summation and other operations on the features of each local area. After the convolution operation, this embodiment reduces the feature dimension through pooling operation while retaining the main features. After multiple layers After processing by the three-dimensional convolution layer and the pooling layer, a joint space-time frequency feature vector is obtained. The joint space-time frequency feature vector integrates the characteristic information of the signal in the three dimensions of time, space and frequency, and can comprehensively describe the characteristics of the difference frequency electrical signal in the joint space-time frequency domain, providing richer information for subsequent tasks such as target state estimation. In this embodiment, the joint space-time frequency feature vector is input into the fully connected layer. In the fully connected layer, this embodiment performs nonlinear mapping on the input joint space-time frequency feature vector through the ReLU activation function. The ReLU activation function can set the negative values ​​in the input joint space-time frequency feature vector to zero, retain the positive values, and perform a linear transformation on the positive values. This nonlinear transformation enables the network to learn more complex feature representations and improve the expressive power of the model, thereby obtaining a channel state parameter vector that characterizes the current channel state. The channel state parameter vector includes features such as Doppler shift, channel attenuation and noise power. These parameters can reflect the state changes of the signal during transmission and provide a basis for subsequent fractional Fourier transform order calculation.

[0090] This embodiment sets the initial fractional Fourier transform order according to the numerical distribution of the channel state parameter vector, and constructs a loss function for measuring the difference between the time-frequency distribution obtained under the current fractional Fourier transform order and the expected time-frequency distribution. The initial fractional Fourier transform order is continuously adjusted by the gradient descent method to maximize the energy concentration, and the optimal fractional Fourier transform order that makes the signal energy most concentrated is obtained. Specifically, this embodiment calculates the gradient of the loss function with respect to the fractional Fourier transform order, and then adjusts the order value along the direction of gradient descent. For example, the amplitude of the order value adjusted in each iteration can be 0.01. After multiple iterations, the optimal fractional Fourier transform order is gradually found. The fractional Fourier transform order that minimizes the loss function is the optimal fractional Fourier transform order. In this embodiment, the standardized difference frequency electrical signal is subjected to a fractional Fourier transform based on the calculated optimal fractional Fourier transform order. The fractional Fourier transform can decompose the standardized difference frequency electrical signal into different fractional frequency components to obtain a two-dimensional preliminary time-frequency distribution matrix. The horizontal axis of the preliminary time-frequency distribution matrix can represent time, and the vertical axis represents fractional frequency. Each element in the preliminary time-frequency distribution matrix represents the signal energy at the corresponding time and fractional frequency. The preliminary time-frequency distribution matrix reflects the time-frequency characteristics of the signal in the fractional domain.

[0091] Finally, this embodiment sequentially performs morphological filtering operations, such as opening and closing, on the preliminary time-frequency distribution matrix. The opening operation involves erosion followed by dilation, which can remove small noise points and disconnected connections. The closing operation involves dilation followed by erosion, which can fill small holes and connect adjacent areas. For example, in the opening operation, this embodiment uses a cross-shaped structuring element to perform a dilation operation on the entire preliminary time-frequency distribution matrix. For each position, if all pixels in the area covered by the structuring element are greater than a certain threshold, the pixel at that position is retained; otherwise, it is set to zero. The dilation operation uses a square structuring element to perform an erosion operation, assigning the maximum pixel value within the area covered by the square structuring element to the pixel at the center position. Through morphological filtering operations, the time-frequency distribution matrix is ​​processed in the spatial domain to remove noise and unreasonable components. After morphological filtering, a target time-frequency distribution matrix is ​​obtained, after which discrete noise points are removed. In the target time-frequency distribution matrix, the row vectors independently represent the target distance distribution, and the column vectors independently represent the velocity distribution. Therefore, the distance and velocity parameters can be independently analyzed, providing a more reliable basis for subsequent target recognition, positioning, and velocity estimation.

[0092] It should be noted that a variety of information is coupled in the difference frequency electrical signal, which includes not only the target's motion state information such as distance, speed, radial acceleration, etc., but also interference factors such as environmental noise and channel characteristics. The deep neural network, through its nonlinear mapping structure, can separate useful target motion state-related features from these mixed signal features and remove the influence of noise and interference components. For example, the long short-term memory network captures temporal dependencies, extracts time domain features, and decouples the relationship between the continuity of the signal and the coherence of the target motion in the time dimension; the three-dimensional convolutional neural network performs convolution operations in the spatial frequency domain, further decomposes the time domain features into spatial and frequency features, and separates the association between the frequency components and the target motion state. This process gradually separates the multiple factors that were originally entangled from the signal.

[0093] S3. Predicting the Doppler frequency shift of the target object at the next moment based on the target time-frequency distribution matrix.

[0094] In some embodiments, the step of predicting the Doppler frequency shift of the target object at the next moment based on the target time-frequency distribution matrix includes:

[0095] A peak search algorithm is used to search for energy concentration points in the target time-frequency distribution matrix and extract characteristic parameters of the energy concentration points; the characteristic parameters of the energy concentration points include the time center position, the frequency center value, the energy intensity and the time-frequency distribution variance;

[0096] A state vector of a Kalman filter is defined, and a state equation is established based on the state vector. The frequency center position and the time center position of the characteristic parameters of the energy accumulation point are used as observation variables to establish an observation equation; the state vector includes the target distance, radial velocity, and radial acceleration to be estimated;

[0097] Integrating the state equation and the observation equation to form a target motion state space model;

[0098] Based on the characteristic parameters of the energy accumulation point, the target motion state is initially estimated using the target motion state space model to obtain an initial state estimation value at the previous moment;

[0099] According to the target state estimation value at the previous moment and the state equation, the target state estimation value at the current moment is predicted by using a Kalman filter;

[0100] The radial velocity information is calculated according to the target state estimation value at the current moment, and the radial velocity information is converted into a frequency shift amount using the Doppler effect principle to obtain the Doppler frequency shift amount at the next moment.

[0101] Specifically, this embodiment traverses each element of the target time-frequency distribution matrix, performs a sliding window peak search on the target time-frequency distribution matrix, and calculates the energy value in the local area. These energy concentration points usually correspond to the characteristics of the target reflection signal. At the same time, this embodiment sets an energy threshold. For example, this embodiment can set the energy threshold to 30% of the maximum value of the target time-frequency distribution matrix, mark the local area with energy value higher than the energy threshold as a potential energy concentration point, extract the time center position, frequency center value, energy intensity and time-frequency distribution variance of the energy concentration point, and obtain the energy concentration point feature parameter set that characterizes the target motion characteristics, wherein the time center position is obtained by calculating the weighted average position of the energy concentration point on the time axis, and the weight of the time center position is the energy value of the corresponding position; the frequency center value is obtained by calculating the weighted average value of the energy concentration point on the frequency axis, and the weight of the frequency center value is the energy value of the corresponding frequency; the total energy intensity is obtained by summing all energy values ​​in the energy concentration point; the time-frequency distribution variance is obtained by calculating the variance of the energy concentration point in the time and frequency dimensions, and the time-frequency distribution variance reflects the degree of discreteness of energy around the energy concentration point.

[0102] Then, this embodiment defines the target distance, radial velocity and radial acceleration as state vectors. The three parameters of target distance, radial velocity and radial acceleration jointly describe the motion state of the target. According to the state vector, the target kinematic model is used to establish a state equation describing the continuous motion of the target. The state equation describes the relationship between the target state and time. Assuming that the target motion is uniformly accelerated in a short period of time, the target distance at the next moment is equal to the sum of the current target distance, the current radial velocity and the time interval, and the product of half the radial acceleration and the square of the time interval; the radial velocity at the next moment is equal to the sum of the current radial velocity and the current radial acceleration and the time interval; the radial acceleration at the current moment is equal to the radial acceleration at the previous moment. The time interval refers to the time difference between two state estimates. In this embodiment, the frequency center position and the time center position in the characteristic parameters of the energy accumulation point are used as observation variables to establish an observation equation. In the observation equation, the observation value is equal to the sum of the observation noise plus the product of the state vector and the observation matrix. Therefore, in this embodiment, the state equation and the observation equation of the Kalman filter are established, which provides a mathematical model for subsequent state estimation and prediction. In this embodiment, the state equation and the observation equation are combined into a target motion state space model, wherein the state equation describes the change law of the target state over time, and the observation equation describes the relationship between the observation value and the target state. In this embodiment, the target motion state space modulus parameters are set. The target motion state space modulus parameters include the process noise covariance matrix and the observation noise covariance matrix. These parameters reflect the uncertainty of the target motion state space model.

[0103] Next, this embodiment uses the extracted characteristic parameters of the energy accumulation point (time center position and frequency center value) as the initial observation data, uses the target motion state space model, and performs a preliminary estimation of the target state through the least squares method to obtain the initial state estimation value at the previous moment, which provides a starting point for the subsequent Kalman filter recursive estimation. The initial state estimation value at the previous moment includes the initial distance, initial radial velocity and initial radial acceleration of the target. This embodiment forward recursively propagates the state equation based on the state estimation value at the previous moment. Specifically, the target state estimation value obtained at the previous moment is used as input, and the target state prediction value at the current moment is calculated according to the state equation. The target state prediction value at the current moment is equal to the initial state estimation value at the previous moment and the sum of them. The sum of the product of the state change rate and the time interval at the previous moment. This embodiment calculates the covariance matrix of the predicted state based on the process noise covariance matrix. The covariance matrix of the predicted state reflects the uncertainty of the prediction, thereby obtaining the target state prediction value at the current moment and its covariance matrix. The target state prediction value includes the radial velocity prediction value at the current moment, which provides a basis for subsequent update steps. This embodiment extracts the radial velocity component from the target state estimate value at the current moment. According to the principle of the Doppler effect, the Doppler frequency shift is proportional to the radial velocity. The Doppler frequency shift at the next moment is equal to the ratio of twice the product of the radial velocity prediction value and the carrier frequency to the speed of light. This Doppler frequency shift can be used for further tracking and prediction of the target motion state.

[0104] S4. Based on the chaotic frequency modulation adjustment signal, the ranging deviation compensation amount is calculated; wherein, the chaotic frequency modulation adjustment signal is obtained by dynamically adjusting the chaotic frequency modulation slope of the chaotic frequency modulation continuous wave signal by the Doppler frequency shift amount.

[0105] In some implementations, the process of acquiring the chaotic frequency modulation adjustment signal is specifically as follows:

[0106] The chaotic frequency modulation continuous wave signal is processed by a spectrum analysis method to extract the initial chaotic frequency modulation slope;

[0107] Calculating a chaotic frequency modulation slope adjustment amount according to the Doppler frequency shift amount, and obtaining a chaotic frequency modulation slope correction value according to the chaotic frequency modulation slope adjustment amount and the initial chaotic frequency modulation slope;

[0108] According to the chaotic frequency modulation slope correction value and the preset signal center frequency, a chaotic frequency modulation adjustment signal is generated by utilizing a logistic chaotic map.

[0109] Specifically, this embodiment takes the chaotic frequency modulation continuous wave signal to be processed as input, performs Fourier transform on the chaotic frequency modulation continuous wave signal, obtains the spectrum distribution characteristics of the chaotic frequency modulation continuous wave signal, identifies the main frequency component of the chaotic frequency modulation continuous wave signal and its change trend in the spectrum distribution characteristics, and the spectrum of the chaotic frequency modulation continuous wave signal appears to be broadband and continuous. The initial chaotic frequency modulation slope can be obtained by measuring the ratio of the frequency change of the chaotic frequency modulation continuous wave signal to the corresponding time change. The initial chaotic frequency modulation slope reflects the speed of frequency change of the chaotic frequency modulation continuous wave signal before dynamic adjustment is performed, and provides a reference value for subsequent slope adjustment.

[0110] The Doppler frequency shift reflects the motion state of the target relative to the radar. This embodiment adjusts the chaotic frequency modulation slope to make the radar signal better adapt to the motion characteristics of the target and improve detection accuracy. The adjustment amount of the chaotic frequency modulation slope needs to be dynamically calculated based on the Doppler frequency shift. Specifically, this embodiment calculates the amount by which the chaotic frequency modulation slope needs to be adjusted based on the Doppler frequency shift to obtain the chaotic frequency modulation slope adjustment amount. The chaotic frequency modulation slope adjustment amount is proportional to the Doppler frequency shift amount, that is, the chaotic frequency modulation slope adjustment amount is equal to the product of a preset proportional coefficient and the Doppler frequency shift amount. The preset proportional coefficient is determined by the radar's operating frequency, the speed of light, and signal processing parameters. This embodiment adds the calculated chaotic frequency modulation slope adjustment amount to the initial chaotic frequency modulation slope to obtain a chaotic frequency modulation slope correction value. This chaotic frequency modulation slope correction value comprehensively considers the impact of target motion on the signal frequency, so that the frequency modulation slope of the chaotic frequency modulation continuous wave signal can better match the motion state of the target, thereby improving detection accuracy and adaptability.

[0111] Logistic chaos mapping is a commonly used chaos mapping method. It can generate a sequence with chaotic characteristics through iterative calculation. In this embodiment, an initial value is randomly selected between 0 and 1, and the initial value is used as the starting point of the chaotic mapping iteration. It is iterated from the initial value to generate a series of chaotic sequence values. These values ​​are distributed between 0 and 1 and have chaotic characteristics, that is, they are very sensitive to the initial value and control parameters and have no periodicity. In this embodiment, the generated chaotic sequence values ​​are mapped to a frequency range centered on the preset signal center frequency and the upper and lower ranges are determined by the chaotic frequency modulation slope correction value. For example, the preset signal center frequency is , chaotic frequency modulation slope correction value Determines the range of frequency change, for each chaotic sequence value , the corresponding actual frequency ,in, is a time variable, so as the chaotic sequence value The chaotic change of the actual frequency It will also be dynamically modulated according to the chaotic frequency modulation slope correction value to generate a chaotic frequency modulation adjustment signal that adapts to the target's motion state. Through the above steps, the chaotic frequency modulation slope of the chaotic frequency modulation continuous wave signal is dynamically adjusted according to the Doppler frequency shift amount to obtain the chaotic frequency modulation adjustment signal. This process enables the lidar to better adapt to the changes in the target's motion state and improves the accuracy and robustness of obstacle detection.

[0112] In some implementations, the step of calculating the ranging deviation compensation amount based on the chaotic frequency modulation adjustment signal includes:

[0113] Using Hilbert transform to extract the first instantaneous frequency sequence of the chaotic frequency modulation adjustment signal and the second instantaneous frequency sequence of the preset standard chaotic frequency modulation signal;

[0114] Performing time domain alignment on the first instantaneous frequency sequence and the second instantaneous frequency sequence to obtain corresponding first time domain aligned instantaneous frequency sequence and second time domain aligned instantaneous frequency sequence;

[0115] performing a point-by-point difference operation on the first time-domain aligned instantaneous frequency sequence and the second time-domain aligned instantaneous frequency sequence to obtain a frequency difference sequence;

[0116] The ranging deviation at each moment is calculated according to the frequency difference sequence and the laser wavelength to obtain the ranging deviation compensation amount.

[0117] Specifically, the Hilbert transform is a method of converting a time domain signal into an analytical signal, which can extract the instantaneous frequency information of the signal. In this embodiment, the chaotic FM adjustment signal and the preset standard chaotic FM signal are subjected to the Hilbert transform respectively to obtain their respective corresponding analytical signals. The analytical signals contain the amplitude and phase information of the original signal. Then, in this embodiment, the analytical signal of the chaotic FM adjustment signal is phase-demodulated to obtain a first instantaneous frequency sequence, and the analytical signal of the preset standard chaotic FM signal is phase-demodulated to obtain a second instantaneous frequency sequence, thereby obtaining the first instantaneous frequency sequence of the chaotic FM adjustment signal and the second instantaneous frequency sequence of the preset standard chaotic FM signal. The first instantaneous frequency sequence and the second instantaneous frequency sequence are then aligned in time domain. The time domain alignment is to ensure that the two instantaneous frequency sequences correspond to each other on the time axis. To eliminate the error caused by time asynchrony, specifically, this embodiment adjusts the time axes of the first instantaneous frequency sequence and the second instantaneous frequency sequence through interpolation or resampling technology, so that the first instantaneous frequency sequence and the second instantaneous frequency sequence are aligned at the time point, ensuring that the two sequences have corresponding frequency values ​​at the same time point, and obtaining a first time-domain aligned instantaneous frequency sequence and a second time-domain aligned instantaneous frequency sequence. For example, if the first instantaneous frequency sequence and the second instantaneous frequency sequence have different time ranges, it is necessary to fill the shorter instantaneous frequency sequence with zeros or truncate the longer instantaneous frequency sequence to make their time lengths consistent; if the sampling rates of the first instantaneous frequency sequence and the second instantaneous frequency sequence are different, it is necessary to resample one of the instantaneous frequency sequences to make it the same as the sampling rate of the other instantaneous frequency sequence.

[0118] This embodiment takes the first time-domain aligned instantaneous frequency sequence and the second time-domain aligned instantaneous frequency sequence as input, performs differential calculation on the frequency values ​​at each corresponding time point of the first time-domain aligned instantaneous frequency sequence and the second time-domain aligned instantaneous frequency sequence, obtains a frequency difference, and combines all the frequency difference values ​​into a frequency difference sequence, which reflects the frequency deviation between the chaotic frequency modulation adjustment signal and the preset standard signal. Finally, this embodiment calculates the ranging deviation based on the frequency difference sequence and the known laser wavelength. In the frequency modulated continuous wave laser radar, the ranging deviation is proportional to the frequency difference, and the ranging deviation is proportional to the laser wavelength. Inversely proportional, for each frequency difference, this embodiment calculates the ratio of the product of the frequency difference and the speed of light to the product of twice the laser wavelength and the frequency modulation slope to obtain a ranging deviation. A ranging deviation compensation is then obtained based on the product of the ranging deviation and a preset distance calibration coefficient. All ranging deviation compensations are arranged in chronological order to form a ranging deviation compensation sequence. This ranging deviation compensation sequence reflects the distance measurement error caused by the frequency difference between the chaotic frequency modulation adjustment signal and the preset standard chaotic frequency modulation signal at different time points. This compensation can be used to correct the ranging error caused by the frequency deviation and improve the accuracy of obstacle detection.

[0119] S5. Compensate the target object's difference frequency signal frequency value according to the ranging deviation compensation amount and the environmental noise data to calculate the obstacle distance and speed detection information.

[0120] In some embodiments, the step of compensating the target object's difference frequency signal frequency value according to the ranging deviation compensation amount and the environmental noise data to calculate the obstacle distance and speed detection information includes:

[0121] Collecting environmental noise data in real time, and performing preliminary compensation on the original frequency value of the difference frequency electrical signal according to the ranging deviation compensation amount to obtain a compensated frequency value of the difference frequency electrical signal;

[0122] Performing power spectral density estimation on the ambient noise data to obtain ambient noise power spectral density, and constructing an adaptive filter using a minimum mean square error algorithm based on the ambient noise power spectral density;

[0123] Using the adaptive filter to perform noise correction on the compensation frequency value of the difference frequency electrical signal to obtain a correction frequency value of the difference frequency electrical signal;

[0124] Performing a fractional Fourier transform on the correction frequency value of the difference frequency electrical signal to obtain a frequency offset characteristic of the difference frequency electrical signal in a fractional Fourier transform domain;

[0125] Calculating an initial distance estimate based on the frequency offset characteristics, frequency modulation bandwidth, and frequency modulation period of the difference frequency electrical signal, and calculating a target speed based on a deviation between a corrected frequency value of the difference frequency electrical signal and an original frequency value of the difference frequency electrical signal;

[0126] Obstacle distance and speed detection information is obtained based on the initial distance estimation value, the target speed, and the frequency modulated continuous wave laser radar geometric position.

[0127] Specifically, this embodiment corrects the original frequency value of the difference frequency electrical signal according to the ranging deviation compensation amount to obtain the compensated frequency value of the difference frequency electrical signal. The compensated frequency value of the difference frequency electrical signal is equal to the sum of the original frequency value and the ranging deviation compensation amount. The compensated frequency value of the difference frequency electrical signal takes into account the error caused by the frequency deviation. Then, this embodiment collects environmental noise data in real time. The environmental noise data reflects the external environmental noise characteristics of the radar system. This embodiment performs power spectrum density estimation on the environmental noise data collected in real time to obtain the environmental noise power spectrum density in the frequency domain. The power spectrum density estimation can reflect the distribution characteristics of the environmental noise in the frequency domain, so that the adaptive filter can dynamically adjust the filtering parameters according to the environmental noise characteristics to minimize the mean square error. Specifically For example, this embodiment designs the parameters of the adaptive filter using the minimum mean square error algorithm based on the power spectral density of the environmental noise, so that the adaptive filter can dynamically adapt to the changes in the environmental noise, thereby constructing an adaptive filter. The adaptive filter can dynamically adjust the filtering parameters according to the characteristics of the environmental noise, so as to effectively suppress the influence of the environmental noise on the difference frequency electrical signal and improve the signal-to-noise ratio of the signal. This embodiment uses the compensated frequency value of the difference frequency electrical signal as the input of the adaptive filter, and uses the adaptive filter to filter the compensated frequency value of the difference frequency electrical signal to suppress the influence of the environmental noise and obtain the corrected frequency value of the difference frequency electrical signal. The corrected frequency value of the difference frequency electrical signal removes the interference of the environmental noise and provides high-quality signal data for the subsequent fractional-order Fourier transform.

[0128] This embodiment performs a fractional Fourier transform on the correction frequency value of the difference frequency electrical signal to obtain a representation of the signal corresponding to the correction frequency value of the difference frequency electrical signal in the fractional Fourier transform domain. In the fractional Fourier transform domain, this embodiment identifies the frequency offset characteristics of the difference frequency electrical signal. These characteristics reflect the frequency changes caused by the target motion, thereby obtaining the frequency offset characteristics of the difference frequency electrical signal in the fractional Fourier transform domain. There is a direct relationship between the frequency offset characteristics of the difference frequency electrical signal and the target distance and speed. This embodiment can calculate the initial value of the target through the frequency modulation bandwidth and frequency modulation period. Distance, the speed of the target can be calculated by frequency deviation. Specifically, according to the ranging formula of the laser radar, the initial distance is related to the frequency offset characteristics of the difference frequency electrical signal, the frequency modulation bandwidth and the frequency modulation period. The frequency modulation bandwidth refers to the frequency change range of the laser signal within one frequency modulation period. The frequency modulation period is the time it takes for the frequency to complete one change. In this embodiment, the initial distance is equal to the ratio of the product of the speed of light and the frequency modulation period to twice the frequency modulation bandwidth multiplied by the frequency offset characteristics of the difference frequency electrical signal. For example, the speed of light is c, the frequency modulation period is T, the frequency modulation bandwidth is B, and the frequency offset characteristics are , then the initial distance estimate The calculation formula is:

[0129]

[0130] Thus, this embodiment can obtain an initial distance estimate, and then calculate the difference between the corrected frequency value of the difference frequency electrical signal and the original frequency value to obtain the frequency change. At the same time, according to the principle of the Doppler effect, the target speed is proportional to the frequency change. The target speed proportional coefficient is determined by factors such as the laser wavelength and the frequency modulation period. This embodiment can set the target speed proportional coefficient to the ratio of the speed of light to twice the carrier frequency. Therefore, this embodiment obtains the target speed by calculating the product between the frequency change and the target speed proportional coefficient. The two parameters, the initial distance estimate and the target speed, jointly describe the motion state of the target. This embodiment uses the initial distance estimate as the rough distance information of the obstacle and the target speed as the motion speed information of the obstacle. Finally, this embodiment considers the geometric position of the frequency modulated continuous wave laser radar. , the initial distance estimate and target speed are corrected to eliminate the system error caused by the radar position, and the final obstacle distance and speed detection information is obtained. The obstacle distance and speed detection information includes the distance and speed of the obstacle. For example, in this embodiment, geometric parameters such as the coordinates of a certain position where the laser radar is installed on the vehicle and the pitch angle parameters of the laser radar installation are known. The initial distance estimate is projected and corrected in combination with these geometric parameters. In this embodiment, the ratio between the initial distance estimate and the cosine value of the pitch angle of the laser radar installation is calculated to obtain the obstacle distance value after spatial geometry correction. At the same time, in combination with the target speed, the obstacle distance and speed detection information is obtained. The obstacle distance and speed detection information includes information such as the precise distance between the obstacle and the laser radar, the movement speed of the obstacle, and the position in space.

[0131] An embodiment of the present invention provides an obstacle detection method based on a frequency modulated continuous wave laser radar. The method transmits a chaotic frequency modulated continuous wave signal with non-periodic characteristics to a detection area to obtain a reflected echo signal of a target object; decouples and calculates the difference frequency electrical signal to obtain a target time-frequency distribution matrix; wherein the difference frequency electrical signal is obtained by coherent optical mixing of the reflected echo signal and the chaotic frequency modulated continuous wave signal; according to the target time-frequency distribution matrix, the Doppler frequency shift of the target object at the next moment is predicted; based on a chaotic frequency modulation adjustment signal, a ranging deviation compensation amount is calculated; wherein the chaotic frequency modulation adjustment signal is obtained by dynamically adjusting the chaotic frequency modulation slope of the chaotic frequency modulated continuous wave signal according to the Doppler frequency shift amount; and the frequency value of the target object difference frequency electrical signal is compensated according to the ranging deviation compensation amount and environmental noise data to calculate obstacle distance and speed detection information. Compared with existing technologies, this method achieves precise compensation for ranging deviation by real-time prediction of Doppler frequency shift and dynamic adjustment of chaotic frequency-modulated continuous wave signal parameters, significantly improving the accuracy of obstacle distance and speed detection in complex scenarios, and meeting the needs of high-dynamic scenarios such as autonomous driving.

[0132] It should be noted that the size of the serial numbers of the above-mentioned processes does not mean the order of execution. 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 embodiment of this application.

[0133] In one embodiment, Figure 2 As shown, an embodiment of the present invention provides an obstacle detection system based on a frequency modulated continuous wave laser radar, the system comprising:

[0134] The signal acquisition module 101 is used to transmit a chaotic frequency-modulated continuous wave signal with non-periodic characteristics to the detection area to obtain the reflected echo signal of the target object;

[0135] The difference frequency analysis module 102 is used to perform decoupling calculation on the difference frequency electrical signal to obtain a target time-frequency distribution matrix; wherein the difference frequency electrical signal is obtained by coherent optical mixing of the reflected echo signal and the chaotic frequency modulated continuous wave signal;

[0136] The frequency shift estimation module 103 is used to predict the Doppler frequency shift of the target object at the next moment according to the target time-frequency distribution matrix;

[0137] The deviation compensation module 104 is configured to calculate a ranging deviation compensation amount based on a chaotic frequency modulation adjustment signal, wherein the chaotic frequency modulation adjustment signal is obtained by dynamically adjusting a chaotic frequency modulation slope of the chaotic frequency modulation continuous wave signal according to the Doppler frequency shift amount;

[0138] The target detection module 105 is used to compensate the target object's difference frequency signal frequency value according to the ranging deviation compensation amount and the environmental noise data to calculate the obstacle distance and speed detection information.

[0139] For the specific definition of an obstacle detection system based on a frequency modulated continuous wave laser radar, please refer to the above-mentioned definition of an obstacle detection method based on a frequency modulated continuous wave laser radar, which will not be repeated here. A person of ordinary skill in the art will appreciate that the various modules and steps described in conjunction with the embodiments disclosed in this application can be implemented in hardware, software, or a combination of both. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0140] An embodiment of the present invention provides an obstacle detection system based on a frequency modulated continuous wave laser radar. The signal acquisition module of the system transmits a chaotic frequency modulated continuous wave signal with non-periodic characteristics to a detection area to obtain a reflected echo signal of a target object; a difference frequency analysis module performs decoupling calculation on the difference frequency electrical signal to obtain a target time-frequency distribution matrix; wherein the difference frequency electrical signal is obtained by extracting the reflected echo signal and the chaotic frequency modulated continuous wave signal by coherent optical mixing; a frequency shift estimation module predicts the Doppler frequency shift of the target object at the next moment based on the target time-frequency distribution matrix; a deviation compensation module calculates a ranging deviation compensation amount based on a chaotic frequency modulation adjustment signal; wherein the chaotic frequency modulation adjustment signal is obtained by dynamically adjusting the chaotic frequency modulation slope of the chaotic frequency modulated continuous wave signal according to the Doppler frequency shift amount; and a target detection module compensates the frequency value of the target object difference frequency electrical signal based on the ranging deviation compensation amount and environmental noise data to calculate obstacle distance and speed detection information. Compared with existing technologies, this system achieves precise compensation for ranging deviations by real-time prediction of Doppler frequency shift and dynamic adjustment of chaotic frequency-modulated continuous wave signal parameters, significantly improving the accuracy of obstacle distance and speed detection in complex scenarios, and meeting the needs of high-dynamic scenarios such as autonomous driving.

[0141] The above-described embodiments merely represent several preferred implementations of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art could make several improvements and substitutions without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be based on the scope of protection of the claims.

Claims

1. An obstacle detection method based on frequency modulated continuous wave laser radar, characterized in that: The following steps are involved: Transmitting a chaotic frequency-modulated continuous wave signal with non-periodic characteristics to the detection area to obtain the reflected echo signal of the target object; Performing decoupling calculation on the difference frequency electrical signal to obtain a target time-frequency distribution matrix; wherein the difference frequency electrical signal is obtained by extracting the reflected echo signal and the chaotic frequency modulated continuous wave signal by coherent optical mixing; Predicting the Doppler frequency shift of the target object at the next moment according to the target time-frequency distribution matrix; Based on the chaotic frequency modulation adjustment signal, a ranging deviation compensation amount is calculated; wherein the chaotic frequency modulation adjustment signal is obtained by dynamically adjusting the chaotic frequency modulation slope of the chaotic frequency modulation continuous wave signal by the Doppler frequency shift amount; Compensating the target object's difference frequency signal frequency value according to the ranging deviation compensation amount and the environmental noise data to calculate the obstacle distance and speed detection information; The step of obtaining the reflected echo signal of the target object comprises: Generate an initial chaotic sequence with non-periodic characteristics, and dynamically adjust the instantaneous frequency offset of the initial chaotic sequence through a phase modulator to generate a chaotic frequency-modulated continuous wave signal; The chaotic frequency-modulated continuous wave signal is modulated by adopting a hybrid modulation method combining orthogonal frequency division multiplexing and high-order orthogonal amplitude modulation to obtain a modulated chaotic frequency-modulated continuous wave signal; The modulated chaotic frequency-modulated continuous wave signal is loaded onto a multi-beam transducer array for space-time coding, and a chaotic encrypted positioning pilot sequence is embedded into the chaotic frequency-modulated continuous wave signal loaded onto the multi-beam transducer array to obtain a chaotic frequency-modulated continuous wave transmission signal; The chaotic frequency modulated continuous wave transmission signal is transmitted directionally to the detection area through the multi-beam transducer array, and the reflected echo signal of the target object reflected from the detection area is received; The step of predicting the Doppler frequency shift of the target object at the next moment: A peak search algorithm is used to search for energy concentration points in the target time-frequency distribution matrix and extract characteristic parameters of the energy concentration points; the characteristic parameters of the energy concentration points include the time center position, the frequency center value, the energy intensity and the time-frequency distribution variance; A state vector of a Kalman filter is defined, and a state equation is established based on the state vector. The frequency center position and the time center position of the characteristic parameters of the energy accumulation point are used as observation variables to establish an observation equation; the state vector includes the target distance, radial velocity, and radial acceleration to be estimated; Integrating the state equation and the observation equation to form a target motion state space model; Based on the characteristic parameters of the energy accumulation point, the target motion state is initially estimated using the target motion state space model to obtain an initial state estimation value at the previous moment; According to the target state estimation value at the previous moment and the state equation, the target state estimation value at the current moment is predicted by using a Kalman filter; The radial velocity information is calculated according to the target state estimation value at the current moment, and the radial velocity information is converted into a frequency shift amount using the Doppler effect principle to obtain the Doppler frequency shift amount at the next moment.

2. The obstacle detection method based on FMCW laser radar according to claim 1, characterized in that: The acquisition process of the difference frequency electrical signal is specifically as follows: performing time domain alignment on the reflected echo signal according to timestamp information of the reflected echo signal and the chaotic frequency modulation continuous wave signal to obtain a target reflected echo time domain aligned signal; Using a mixer to perform coherent optical mixing on the target reflected echo time-domain aligned signal and the chaotic frequency-modulated continuous wave signal to obtain an original mixed signal; The high-frequency component in the original mixed signal is filtered out by a low-pass filter to extract the difference frequency electrical signal.

3. The obstacle detection method based on FMCW laser radar according to claim 1, characterized in that: The target time-frequency distribution matrix is ​​extracted through a deep neural network, which includes a first channel branch and a second channel branch using a dual-channel parallel branch architecture. The first channel branch is a branch structure composed of a cascade of a long short-term memory network and a three-dimensional convolutional neural network, and the second channel branch is a branch structure based on a fractional Fourier transform.

4. The obstacle detection method based on FMCW laser radar according to claim 3, characterized in that: The step of performing decoupling calculation on the difference frequency electrical signal to obtain a target time-frequency distribution matrix includes: According to the amplitude range of the difference frequency electrical signal, a maximum-minimum value normalization algorithm is used to normalize the difference frequency electrical signal to obtain a standardized difference frequency electrical signal; Capturing the temporal dependency in the standardized difference frequency electrical signal through a long short-term memory network and extracting the time domain features of the difference frequency electrical signal; Using a three-dimensional convolutional neural network to perform a space-frequency domain convolution operation on the time domain features of the difference frequency electrical signal to obtain a space-time frequency joint feature vector; Inputting the joint time-space-frequency feature vector into a fully connected layer for nonlinear transformation to obtain a channel state parameter vector; Based on the channel state parameter vector, an optimal fractional Fourier transform order is calculated using a gradient descent method, and a fractional Fourier transform is performed on the standardized difference frequency electrical signal according to the optimal fractional Fourier transform order to obtain a preliminary time-frequency distribution matrix; A morphological filtering algorithm is used to perform spatial domain filtering on the preliminary time-frequency distribution matrix to obtain a target time-frequency distribution matrix.

5. The obstacle detection method based on FMCW laser radar according to claim 1, characterized in that: The acquisition process of the chaotic frequency modulation adjustment signal is specifically as follows: The chaotic frequency modulation continuous wave signal is processed by a spectrum analysis method to extract the initial chaotic frequency modulation slope; Calculating a chaotic frequency modulation slope adjustment amount according to the Doppler frequency shift amount, and obtaining a chaotic frequency modulation slope correction value according to the chaotic frequency modulation slope adjustment amount and the initial chaotic frequency modulation slope; According to the chaotic frequency modulation slope correction value and the preset signal center frequency, a chaotic frequency modulation adjustment signal is generated by utilizing a logistic chaotic map.

6. The obstacle detection method based on FMCW laser radar according to claim 1, characterized in that: The step of calculating the ranging deviation compensation amount based on the chaotic frequency modulation adjustment signal includes: Using Hilbert transform to extract the first instantaneous frequency sequence of the chaotic frequency modulation adjustment signal and the second instantaneous frequency sequence of the preset standard chaotic frequency modulation signal; Performing time domain alignment on the first instantaneous frequency sequence and the second instantaneous frequency sequence to obtain corresponding first time domain aligned instantaneous frequency sequence and second time domain aligned instantaneous frequency sequence; performing a point-by-point difference operation on the first time-domain aligned instantaneous frequency sequence and the second time-domain aligned instantaneous frequency sequence to obtain a frequency difference sequence; The ranging deviation at each moment is calculated according to the frequency difference sequence and the laser wavelength to obtain the ranging deviation compensation amount.

7. The obstacle detection method based on FMCW laser radar according to claim 1, characterized in that: The step of compensating the target object's difference frequency signal frequency value according to the ranging deviation compensation amount and the environmental noise data to calculate the obstacle distance and speed detection information includes: Collecting environmental noise data in real time, and performing preliminary compensation on the original frequency value of the difference frequency electrical signal according to the ranging deviation compensation amount to obtain a compensated frequency value of the difference frequency electrical signal; Performing power spectral density estimation on the ambient noise data to obtain ambient noise power spectral density, and constructing an adaptive filter using a minimum mean square error algorithm based on the ambient noise power spectral density; Using the adaptive filter to perform noise correction on the compensation frequency value of the difference frequency electrical signal to obtain a correction frequency value of the difference frequency electrical signal; Performing a fractional Fourier transform on the correction frequency value of the difference frequency electrical signal to obtain a frequency offset characteristic of the difference frequency electrical signal in a fractional Fourier transform domain; Calculating an initial distance estimate based on the frequency offset characteristics, frequency modulation bandwidth, and frequency modulation period of the difference frequency electrical signal, and calculating a target speed based on a deviation between a corrected frequency value of the difference frequency electrical signal and an original frequency value of the difference frequency electrical signal; Obstacle distance and speed detection information is obtained based on the initial distance estimation value, the target speed, and the frequency modulated continuous wave laser radar geometric position.

8. An obstacle detection system based on frequency modulated continuous wave laser radar, characterized in that: The system comprises: The signal acquisition module is used to transmit a chaotic frequency-modulated continuous wave signal with non-periodic characteristics to the detection area to obtain the reflected echo signal of the target object; a difference frequency analysis module, configured to perform decoupling calculations on the difference frequency electrical signal to obtain a target time-frequency distribution matrix; wherein the difference frequency electrical signal is extracted by performing coherent optical mixing of the reflected echo signal and the chaotic frequency modulated continuous wave signal; A frequency shift estimation module is used to predict the Doppler frequency shift of the target object at the next moment according to the target time-frequency distribution matrix; A deviation compensation module is used to calculate a ranging deviation compensation amount based on a chaotic frequency modulation adjustment signal; wherein the chaotic frequency modulation adjustment signal is obtained by dynamically adjusting the chaotic frequency modulation slope of the chaotic frequency modulation continuous wave signal by the Doppler frequency shift amount; A target detection module is used to compensate the target object's difference frequency signal frequency value according to the ranging deviation compensation amount and the environmental noise data to calculate the obstacle distance and speed detection information; The step of obtaining the reflected echo signal of the target object comprises: Generate an initial chaotic sequence with non-periodic characteristics, and dynamically adjust the instantaneous frequency offset of the initial chaotic sequence through a phase modulator to generate a chaotic frequency-modulated continuous wave signal; The chaotic frequency-modulated continuous wave signal is modulated by adopting a hybrid modulation method combining orthogonal frequency division multiplexing and high-order orthogonal amplitude modulation to obtain a modulated chaotic frequency-modulated continuous wave signal; The modulated chaotic frequency-modulated continuous wave signal is loaded onto a multi-beam transducer array for space-time coding, and a chaotic encrypted positioning pilot sequence is embedded into the chaotic frequency-modulated continuous wave signal loaded onto the multi-beam transducer array to obtain a chaotic frequency-modulated continuous wave transmission signal; The chaotic frequency modulated continuous wave transmission signal is transmitted directionally to the detection area through the multi-beam transducer array, and the reflected echo signal of the target object reflected from the detection area is received; The step of predicting the Doppler frequency shift of the target object at the next moment: A peak search algorithm is used to search for energy concentration points in the target time-frequency distribution matrix and extract characteristic parameters of the energy concentration points; the characteristic parameters of the energy concentration points include the time center position, the frequency center value, the energy intensity and the time-frequency distribution variance; A state vector of a Kalman filter is defined, and a state equation is established based on the state vector. The frequency center position and the time center position of the characteristic parameters of the energy accumulation point are used as observation variables to establish an observation equation; the state vector includes the target distance, radial velocity, and radial acceleration to be estimated; Integrating the state equation and the observation equation to form a target motion state space model; Based on the characteristic parameters of the energy accumulation point, the target motion state is initially estimated using the target motion state space model to obtain an initial state estimation value at the previous moment; According to the target state estimation value at the previous moment and the state equation, the target state estimation value at the current moment is predicted by using a Kalman filter; The radial velocity information is calculated according to the target state estimation value at the current moment, and the radial velocity information is converted into a frequency shift amount using the Doppler effect principle to obtain the Doppler frequency shift amount at the next moment.

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