CN118091653B - Minimum distortion respiratory signal detection method based on multichannel fusion - Google Patents
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Abstract
The invention provides a minimum distortion respiratory signal detection method based on multichannel fusion, and belongs to the technical field of radar detection. The method uses ultra-wideband pulse radar with multiple channels to detect, radar echo data of all channels are preprocessed and then are subjected to multi-channel fusion to strengthen respiratory signals, the respiratory signal detection method uses the thought of time-space prediction, and the optimal time-space prediction transformation among a group of different channels is determined by minimizing residual noise while signal distortion is close to zero; and generating an optimal noise reduction filter by using the optimal space-time prediction transformation, wherein a noise correlation matrix estimation is performed by using an inanimate signal part, the noise reduction filter estimation is performed by solving an MSE cost function, and finally, the detected human respiratory signal is output. The method solves the difficult problem of respiratory detection caused by the change of the azimuth and the posture of the human body target, and enhances the human body detection capability of the ultra-wideband radar in the applications of earthquake rescue, wall-penetrating detection and the like.
Description
Technical Field
The invention belongs to the technical field of radar detection, and particularly relates to a minimum distortion respiratory signal detection method based on multichannel fusion.
Background
Breath detection based on ultra-wideband (UWB) radar is currently being developed vigorously, and has a wide application prospect in practice. It can be used for remote sensing of the presence of human targets for wall monitoring, post-earthquake search and rescue, etc. From the radar detection principle, the most important way of detecting vital signals of UWB radar is by detecting chest movements caused by respiration of a living body when the human target is stationary.
In these applications, the position and posture of the human target cannot be known in advance, and these two factors cause a problem of the direction of the human body, i.e., the chest of the human target cannot be guaranteed to always face the radar. Because of the small amplitude of chest motion, detecting human breath using UWB radar is a challenging task. Under different human body attitudes, the signal results detected by the radar are different, when a tester faces away from or faces the radar antenna, the radial component of the detected chest motion is the largest, and the respiratory signal energy is the strongest. When the human body cannot face the radar, such as in a lateral state, a lying state and the like, the radial component of the chest motion can be reduced due to the change of the posture, and the difficulty of detecting the respiratory signal is further increased.
Thus, uncertainty in the pose and position of human targets becomes a critical issue affecting UWB radar detection of human breath. Particularly in applications of non-line-of-sight (NLOS) human target detection, such as through-the-wall surveillance or search and rescue of trapped victims after an earthquake, the respiratory motion energy contained in the radar echo becomes weak, affecting the detection of living targets. In previous studies, a learner designed a hidden markov model to infer the orientation of the object under test, performed experiments using multi-channel UWB radar, and solved this problem by selecting the channel with the highest signal quality. However, this study was performed in free space. In the through-the-wall situation, multi-channel UWB radar is mainly used for moving target positioning and tracking for human target detection, and there is no study on improving human breath detection using this type of radar.
Disclosure of Invention
In order to solve the technical problems, the invention provides a minimum distortion respiratory signal detection method based on multi-channel fusion, which uses the idea of time-space prediction to realize noise reduction in two steps, wherein the first step is to determine the optimal time-space prediction transformation between a group of different channels by minimizing residual noise while signal distortion is close to zero; and secondly, generating an optimal noise reduction filter by using optimal space-time prediction transformation, wherein a noise correlation matrix is estimated by using an inanimate signal part, the noise reduction filter is estimated by solving an MSE cost function, and finally, the detected human respiratory waveform is output.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a least distorted breath signal detection method based on multichannel fusion comprises the following steps:
Step 1, acquiring a life signal by using a single-transmitting four-receiving ultra-wideband radar, wherein the life signal is a time domain echo signal;
Step 2, preprocessing the time echo signals, removing fixed background, inhibiting temperature and linear drift caused by a radar system, and filtering high-frequency noise;
step 3, extracting time domain data of priori distance information of the corresponding channels from the time domain echo signals after preprocessing of the channels is completed, and fusing the time domain data to enhance respiratory signals received on a single channel;
And 4, carrying out Fourier transformation and windowing on the respiratory signals received on the enhanced single channel along slow time to obtain time-frequency signals, intercepting the frequency range of the frequency of the target breath, and finally obtaining the required clean respiratory signals.
The invention has the beneficial effects that:
The invention discloses a minimum distortion respiratory signal detection method based on multichannel fusion, which fuses UWB echo data from different receiving channels, detects respiratory motion response contained in the UWB echo data, and the processing result shows that compared with the original data of each channel, the signal to noise ratio of human body respiration processed by the method is much higher. The radar multichannel information is subjected to data fusion, so that the difficult problem of breath detection caused by the change of the azimuth and the posture of a human body target is solved, and the human body detection capability of the ultra-wideband radar in the applications of earthquake rescue, through-wall detection and the like is enhanced.
Drawings
FIG. 1 is a schematic diagram of a method for detecting a respiratory signal with minimal distortion based on multi-channel fusion;
FIG. 2 is a schematic diagram of a linear arrangement of an antenna array of a multi-antenna ultra-wideband radar system according to an embodiment of the present invention;
FIG. 3 shows ultra wideband radar echo data after preprocessing, wherein (a) is radar echo data of channel one, (b) is radar echo data of channel two, (c) is radar echo data of channel three, and (d) is radar echo data of channel four;
FIG. 4 is a schematic diagram of a respiration time sequence of four receiving channels of an ultra wideband radar according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating the result of four receiving channels of frequency domain data of an ultra wideband radar according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a simulation scenario of the present invention;
Fig. 7 shows a time domain waveform and a frequency spectrum of a signal processed according to the method of the present invention.
Detailed Description
In order to further clarify the technical solution, experimental results and advantages of the present invention, the following describes in detail the implementation procedure of the present invention with reference to specific examples.
Fig. 1 shows a schematic diagram of a method for detecting a respiratory signal with minimum distortion based on multi-channel fusion, which specifically includes inputting data through a first channel, a second channel, a third channel and a fourth channel, preprocessing input data respectively, determining optimal space-time prediction transformation, generating an optimal noise reduction filter, and completing respiratory signal detection after the processing of the steps. The method specifically comprises the following steps:
Step S1: a single-transmitting four-receiving ultra-wideband radar is used for acquiring a life signal, wherein an antenna array consists of a transmitting antenna and four receiving antennas, the antenna array has two arrangement modes, four receiving channels sequentially receive echo signals according to the sequence, and the life signal is a time domain echo signal. The transmitting antennas are positioned at the leftmost side, the receiving antennas 1-4 are positioned at the right side of the transmitting antennas and are arranged at equal intervals, as shown in fig. 2, which is a schematic diagram of a linear arrangement mode of an antenna array of the multi-antenna ultra-wideband radar system according to the embodiment of the invention, wherein the transmitting channels are positioned at the left sides of all receiving channels, and the receiving channel I, the receiving channel II, the receiving channel III and the receiving channel IV are sequentially positioned at the right sides of the receiving antennas;
the matrix of signals received by the radar is represented as time domain echo signals WhereinAndThe number of samples representing the fast and slow time dimensions respectively,，，AndThe total number of sampling points in the fast time dimension and the slow time dimension are natural numbers.
Step S2: preprocessing the time domain echo signals to remove fixed background, inhibit temperature and linear drift caused by a radar system, filter high-frequency noise and the like. The pretreatment mainly comprises the following 2 steps: (1) Compensating linear drift caused by a radar system and temperature in a slow time dimension by adopting a linear trend suppression method; (2) A distance filter is designed to filter out high frequency noise caused by the filtering and unwanted low frequency signals. As shown in fig. 3, the ultra-wideband radar echo data after preprocessing in the embodiment of the present invention is represented by time in seconds on the horizontal axis and the target distance radar distance in meters on the vertical axis, where fig. 3 (a) is radar echo data of channel one, fig. 3 (b) is radar echo data of channel two, fig. 3 (c) is radar echo data of channel three, and fig. 3 (d) is radar echo data of channel four;
further, the time domain signal after the pretreatment is recorded as 。
Step S3: detecting the minimum distortion respiratory signal based on a multichannel fusion method, and according to priori distance information of a human body target, finishing the preprocessing from the time domain echo signalTime domain data of prior distance information of corresponding channel manually selected is defined as；
The content is as follows:
(1)
Wherein the subscript Represents the firstThe number of channels in the channel is the same,，Representing the total number of channels,Represents the firstThe respiratory signal of the individual channels,Represents the firstThe noise component of the individual channels is determined,Representing a clean breathing signal, and the like,Representing the impulse response of the time domain echo signal acquired from the living being to the radar. As shown in fig. 4, a schematic diagram of a respiration time sequence of four receiving channels of the ultra-wideband radar according to the embodiment of the present invention is specifically that time domain data corresponding to a priori distance is extracted from a preprocessed time domain echo signal, a horizontal axis represents time in seconds, a vertical axis represents signal amplitude, no unit is provided, and channel time domain data, channel two time domain data, channel three time domain data and channel four time domain data are sequentially provided from top to bottom. Fig. 5 is a schematic diagram showing a frequency domain data result of four receiving channels of the ultra wideband radar according to the embodiment of the present invention; the horizontal axis represents frequency, the vertical axis represents signal amplitude, and no unit is provided, and the frequency spectrum is channel one, channel two, channel three and channel four in sequence from top to bottom.
The present invention exploits the idea of using time-space prediction to enhance the respiratory signal received on a single channel in two steps using the received data of multiple channels. The first step determines the optimal spatio-temporal prediction transforms between a set of sensors and the second step uses these transforms to form an optimal noise reduction filter.
Solving by using the signal model given by the above formula (1)Respiratory signal of one of the channels, when the respiratory signal of that channelQuilt is covered withCo-predicting the time domain echo signals of the channels, and recording the predicted respiratory signal result as：
(2)
Wherein,Is the optimal set of noise reduction filters,For each channelThere is a firstOptimal noise reduction filter set for individual channelsFirst, theOptimal noise reduction filter set length for each channel，Represents the firstOptimal noise reduction filter set for individual channelsCoefficients of each optimal noise reduction filter in the channel, time domain data vectors corresponding to prior distance information of the channel，Representing a matrix transpose, corresponding respiratory signal estimation errorsIs defined as:
(3)
Wherein, The left side of the representative equation is defined as the right side of the equation,Representing the signal distortion caused by the linear transformation,Representing residual noise, noise reduction is ideally a set of filters that find optimal noise reductionIs to minimize residual noise while keeping distortion close to 0By minimizing the mean square error and defining the solving formula as：
(4)
Wherein,Representative mathematics it is desirable that the first and second heat exchangers,Representing the noise correlation matrix and,Noise intermediate variableRepresented asFirst, theNoise component vector for individual channels；
At this time, the noise reduction problem is converted into the following formula,Is the result of the solution:
(5)
the constraint of the above formula (5) is signal distortion caused by linear transformation Wherein agrmin denotes a minimum value operation;
let a known space-time prediction matrix be WhereinRepresentative ofIn 1 st, 2 nd, … thSpace-time prediction part of each channel, and for each channelHas the firstRespiratory signal vector for each channel:
(6)
Wherein, the first Respiratory signal vector for individual channelsVectors of respiratory signals of the channels sought (i.e. respiratory signals of the channels being predicted)Re-describing signal distortion caused by linear transformationThe method comprises the following steps:
(7)
Wherein, Represents a length ofThe noise reduction problem in an ideal case at this time is re-described as:
(8)
min represents the minimum value, and the constraint condition of the formula (8) is The Lagrangian multiplier is used to solve the above constraints:， As a function of the lagrangian, Representing the lagrangian, namely:
(9)
obtaining the solution of formula (9), namely the optimal noise reduction filter ，Representing an inversion operation. As can be seen from the results, the calculation of the optimal noise reduction filter requires a known noise correlation matrixSum full channel space-time prediction matrix. Noise correlation matrixThe end of the data (whether or not the target is present, the detection capability range is exceeded) is selected for estimation. The spatio-temporal prediction matrix is estimated using a root mean square error cost function, and a solution formula is defined as：
(10)
Obtaining the optimal space-time prediction matrix of the current channel；
Wherein,，The cross correlation matrix and the autocorrelation matrix, which represent vital signals respectively, are based on the formula (1):， In the following AndThe cross-correlation matrix and the cross-correlation matrix of the noise component representing the time domain data corresponding to the a priori distance information are manually selected respectively,AndThe autocorrelation matrix of the time domain data and the autocorrelation matrix of the noise component which respectively represent the manual selection of the corresponding prior distance information can obtain the optimal space-time prediction matrix of the current channel:
(11)
thereby obtaining the full-channel optimal space-time prediction matrix The method is thatNoise reduction is performed on the basis of the establishment, and in practice, distortion may exist, but the distortion is usually replaced by equation (9) at a smaller level, and the optimal transformation is written as:
(12)
The final output is predicted respiratory signal I.e. the required clean breathing signal:
(13)
Step S4: and carrying out Fourier transformation and windowing on the processed signals along slow time to obtain time-frequency signals, intercepting a frequency range of the frequency of target respiration, and finally obtaining the required clean respiration signals.
The time domain signals after the processing can be converted into the frequency domain through the slow time FFT and then windowed, the designed frequency band is limited in a narrow window according to the actual respiratory signal frequency of the human body, and meanwhile, for comparison, the FFT conversion and the windowing are also carried out on the preprocessed multiple channel signals.
Referring to fig. 6 and 7, fig. 6 is a schematic diagram of a simulation scenario of the present invention; comprises a wall body, a transmitting and receiving model and a breathing model for simulating human breathing. Fig. 7 shows a time domain waveform and a frequency spectrum of a signal processed according to the method of the present invention. The method comprises the steps of including processed time domain data, wherein a horizontal axis represents time, a unit is seconds, a vertical axis represents signal amplitude, and no unit exists; the frequency spectrum of the processed result has the units of hertz on the horizontal axis and signal amplitude on the vertical axis, and no unit.
It can be seen that the vital signals are not prominent in the four different channel results and are difficult to visually detect, and in the spectrum of the processed results, the vital signals are clearly visible, and the noise is basically inhibited.
In summary, the invention uses the idea of using time-space prediction to enhance the respiratory signal received on a single channel by using the received data of a plurality of channels in two steps, solves the difficult problem of respiratory detection caused by the change of the azimuth and the posture of the human target, and enhances the human detection capability of the ultra-wideband radar in the applications of earthquake rescue, wall-penetrating detection and the like.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the invention thereto, but to limit the invention thereto, and any modifications, equivalents, improvements and equivalents thereof may be made without departing from the spirit and principles of the invention.
Claims (1)
1. The least distorted respiratory signal detection method based on multichannel fusion is characterized by comprising the following steps of:
Step 1, acquiring a life signal by using a single-transmitting four-receiving ultra-wideband radar, wherein the life signal is a time domain echo signal;
Step 2, preprocessing the time echo signals, removing fixed background, inhibiting temperature and linear drift caused by a radar system, and filtering high-frequency noise;
step 3, extracting time domain data of priori distance information of the corresponding channels from the time domain echo signals after preprocessing of the channels is completed, and fusing the time domain data to enhance respiratory signals received on a single channel;
Step 4, carrying out Fourier transformation and windowing on the respiratory signals received on the enhanced single channel along slow time to obtain time-frequency signals, intercepting a frequency range of the frequency of target respiration, and finally obtaining the required clean respiratory signals;
The step 2 comprises the following steps:
Step 2.1, compensating linear drift caused by temperature and a radar system in a slow time dimension by adopting a linear trend suppression method;
step 2.2, designing a distance filter, filtering high-frequency noise and unnecessary low-frequency signals caused by the filtering sampling, and recording the time domain echo signals after the preprocessing is completed as ，/>And/>Sample points representing fast and slow time dimensions, respectively,/> ，/> ，/>And/>The total number of sampling points in the fast time dimension and the slow time dimension are natural numbers;
The step 3 comprises the following steps;
Step 3.1, according to the prior distance information of the target, the time domain echo signal after preprocessing is completed Time domain data of prior distance information of the corresponding channel manually selected is defined as/>：
,
Wherein the subscriptRepresents the/>Individual channels,/>Representing the total number of channels,/>Represents the/>The respiratory signal of the individual channels,Represents the/>Noise component of individual channels,/>Representing a clean respiratory signal,/>Representing an impulse response of a time domain echo signal acquired from a living body to a radar;
Solving for the signal model given above Respiratory signal of one predicted channel in the channels, and respiratory signal/>, of the predicted channelIs/>The time domain echo signals of the channels are jointly predicted, and the breathing signal result of the predicted channel is recorded as/>：
,
Wherein,Representing an optimal noise reduction filter set,/>For each channel/>There is a/>Optimal noise reduction Filter set for Individual channels/>First/>Optimal noise reduction Filter set for Individual channels/>Length is/>，Represents the/>Optimal noise reduction Filter set for Individual channels/>Coefficients of each optimal noise reduction filter in the channel, and time domain data vector/>, corresponding to priori distance information of the channelSuperscript/>Representing a matrix transpose, corresponding, respiratory signal estimation error/>Is defined as:
,
Wherein, The left side of the representative equation is defined as the right side of the equation,/>Representing signal distortion caused by linear transformation,/>Represents residual noise, th/>Noise component vector for individual channels；
The noise reduction problem under ideal conditions is equivalent to finding the optimal noise reduction filter setIs translated into minimizing residual noise/>, while keeping signal distortion close to 0；
By means of minimizing mean square error, the solving formula is defined as：; Wherein/>Representing mathematical expectation, noise correlation matrix/>Represented asNoise intermediate variable/>Expressed as/>；
The noise reduction problem under ideal conditions is converted into mathematical description:
,
The above constraint is signal distortion caused by linear transformation Wherein agrmin denotes a minimum value operation,/>Refers to an optimal noise reduction filter;
let a known space-time prediction matrix be Wherein/>Representing a spatio-temporal prediction matrix/>In 1 st, 2 nd, … th/>Space-time prediction part of individual channels, for each channel/>With the/>Respiratory signal vector for individual channelsWherein/>Respiratory signal vector of individual channels/>Respiratory signal vector/>, of predicted channelRe-describing signal distortion caused by linear transformationThe method comprises the following steps:
,
Wherein, Represents a length of/>The noise reduction problem in an ideal case at this time is re-described as:
,
min represents the minimum value, and the constraint condition is that The Lagrangian multiplier is used to solve the above equation:，/> As Lagrangian function,/> Representing lagrangian operators, there are:
,
Obtaining the above solution, namely the optimal noise reduction filter ，/>Representing inversion operation;
step 3.2, solving an optimal space-time prediction matrix by performing a root mean square error cost function method, wherein the noise correlation matrix is performed by using the inanimate signal part Estimating;
The optimal space-time prediction matrix is estimated by using a root mean square error cost function, and a solving formula is defined as ：
,
Obtaining the optimal space-time prediction matrix of the current channelWherein/>，Respectively representing a cross correlation matrix and an autocorrelation matrix of the vital signals: /(I)，In/>And/>Cross-correlation matrix and cross-correlation matrix of noise component respectively representing manually selected time domain data corresponding to a priori distance information,/>And/>The autocorrelation matrix of the time domain data and the autocorrelation matrix of the noise component, which respectively represent manual selection of the corresponding prior distance information, result in: optimal space-time prediction matrix of current channelThereby obtaining the optimal space-time prediction matrix/>, of the whole channel；
When obtaining the optimal space-time prediction matrix of the whole channelAfter that, an optimal noise reduction filter/>, is obtained：
,
Final output of respiratory signal results for predicted channelsI.e. the required clean breathing signal:
。
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| CN106546979A (en) * | 2016-10-08 | 2017-03-29 | 中国人民解放军第四军医大学 | It is a kind of that detection method is strengthened based on the ultra-wide spectrum radar type human body respiration that multi-antenna data merges |
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| CN109031287B (en) * | 2018-09-21 | 2021-09-07 | 西安交通大学 | Human body respiration signal detection method based on fast-RCNN through-wall ultra-wideband radar |
| CN116466316A (en) * | 2023-04-06 | 2023-07-21 | 辽宁工程技术大学 | Ultra-wideband radar vital sign signal denoising algorithm based on PE and TVF-EMD |
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| CN106546979A (en) * | 2016-10-08 | 2017-03-29 | 中国人民解放军第四军医大学 | It is a kind of that detection method is strengthened based on the ultra-wide spectrum radar type human body respiration that multi-antenna data merges |
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