CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to Chinese Patent Application No. 201911017732.0, filed on Oct. 24, 2019, the content of which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure belongs to the field of bio-radar technologies, and specifically, to a non-contact method for detecting and distinguishing a human and an animal based on IR-UWB bio-radar signals.

BACKGROUND

Bio-radar is a technology which is capable of penetrating a certain medium to achieve functions such as life body detection, vital sign monitoring, life body imaging and positioning in a non-contact and long-distance manner, by extracting vital signs-related signals from radar echoes. A principle of bio-radar is that radar emits electromagnetic waves to a human body; the electromagnetic waves are reflected back to a radar receiving antenna after being modulated by human physiological activities such as breathing, heartbeat, and body movement; and after the radar receives the radar echo, physiological and biological information about a human target is obtained from the radar echo through a certain signal processing technology. These information includes physiological parameters, waveforms, images, target positions, etc. Because of having the above advantages, the bio-radar technology has shown great advantages and broad application prospects in fields of post-disaster rescue, medical monitoring, anti-terrorism and stability maintenance, and battlefield search and rescue.

At present, the detection research on bio-radar at home and abroad pays more attention to whether human targets is in the detection results, as well as other information such as the number, position, posture and contour of targets. However, actual problems encountered in search and rescue operations after disasters such as earthquakes are complex and diverse. For example, in the Wenchuan earthquake of 2008, a rescue team used a bio-search and rescue radar to detect a life signal under a rubble. After several hours of demolition and excavation by search and rescue officers and soldiers, it was found that a poultry was buried under the rubble. Therefore, in applications such as searching for missing persons in post-disaster rescue, it is not only desired to know whether there is a living body, but also whether the detected living body is a human or an animal. The accurate distinction and distinction of humans and animals is of great practical significance for scientifically formulating rescue plans, saving limited rescue forces and resources, improving an efficiency of search and rescue during a golden rescue period, enhancing search and rescue confidence of rescuers, and more accurately identifying and saving lives of survivors.

When transmitted signals of the UWB bio-radar are irradiated to a stationary object, echo signals of the radar are a stable fixed value. When transmitted signals of the UWB bio-radar are irradiated to life bodies such as humans or animals, raw radar echo signals are cause to fluctuate due to slight fluctuations of a body surface caused by breathing of the living body. In this way, life bodies can be detected through these tiny fluctuations, and differences in characteristics of slight fluctuations (characteristics such as energy amplitude, signal regularity, etc.) between humans and animals in the radar echo can be used to distinguish and discriminate between humans and animals.

SUMMARY

An object of the present disclosure is to provide a non-contact method for detecting and distinguishing a human and an animal based on IR-UWB bio-radar signals, to solve a problem of distinguishing and discriminating between the human and the animal.

In order to achieve above tasks, the present disclosure adopts following technical solutions.

A non-contact method for detecting and distinguishing a human and an animal based on IR-UWB bio-radar signals includes:

step 1: transmitting a radar pulse to a target by a transmitting antenna of an IR-UWB bio-radar, obtaining a radar echo signal E(n,m) through a receiving antenna of the IR-UWB bio-radar, where the radar echo signal is generated from the radar pulse reflected on the target, m is a sampling ordinal number in a fast time direction, n is a sampling ordinal number in a slow time direction, and m and n are positive integers;

step 2: performing signal preprocessing on the radar echo signal E(n,m) obtained in the step 1, to obtain a first energy signal E6(l);

step 3: removing a direct wave from the first energy signal E6(l) obtained in the step 2 to obtain a second energy signal E7(l), obtaining a maximum amplitude value E7maxof the second energy signal E7(l) and a position Imaxcorresponding to the maximum value in the slow time direction;

step 4: calculating a peak-to-background ratio VEtoBof the second energy signal E7(l), and calculating an average correlation coefficient rm, and determining a type of the target through a target detection and distinction rule, where the target detection and distinction rule includes:

a) if VEtoB<σN, a determination result is no target;

b) if σN≤VEtoB<σYand rm<σrm1the determination result is no target;

c) if VEtoB≥σY, the determination result is a human target;

d) if Sthres≤VEtoB<σYand rm>σrm2, the determination result is a human target; and

f) in cases other than a), b), c) and d), the determination result is an animal target,

where σNrepresents a no target threshold; σYrepresents a human target threshold; Sthresrepresents a sensitivity threshold and σN<Sthres<σY; σN, σY, and Sthresare larger than 1; σrm1represent a weak correlation threshold; σrm2represents a strong correlation threshold and σrm1<σrm2; σrm1and σrm2are both larger than 0 and smaller than 1.

Further, in the step 4, σN=1.65, σY=8, σrm1=0.45, σrm2=0.92, Sthres={2, 3, 3.8}.

Further, the signal preprocessing in the step 2 includes following sub-steps:

step 2.1: performing a distance accumulation on the radar echo signal E(n,m);

step 2.2: multiplying a signal after the distance accumulation in the step 2.1 by an exponential gain curve G(l) of a formula I, to perform attenuation compensation,

G⁡(l)=exp⁡(l⁢g⁡(Vh)P×l)Formula⁢⁢I

where Vhrepresents a ratio of the maximum value of the radar echo data to an amplitude of a target reflection echo, P represents a target position in units of m, l represents a fast time ordinal number after the distance accumulation, l=1, 2, . . . , L, and L is a positive integer;

step 2.3: removing a static clutter from the signal after the attenuation compensation in the step 2.2;

step 2.4: performing a linear trend subtraction from the signal after the static clutter is removed in the step 2.3;

step 2.5: performing, in the slow time dimension, low-pass filtering on the signal after the linear trend subtraction in the step 2.4;

step 2.6: accumulating, along a slow time axis, the signal after the low-pass filtering in the step 2.5, to obtain the first energy signal E6(l).

Further, the average correlation coefficient rmat a position of the maximum amplitude is calculated and obtained by a formula II:

{ri=∑q=1Q⁢(E(max+(i-4))⁡(q)-E(max+(i-4))⁡(q)_)(Emax⁡(q)-Emax⁡(q)_)∑q=1Q⁢(E(max+(i-4))⁡(q)-E(max+(i-4))⁡(q)_)2∑q=1Q⁢(Emax⁡(q)-Emax⁡(q)_)2,⁢i≤3ri=∑q=1Q⁢(E(max+(i-3))⁡(q)-E(max+(i-3))⁡(q)_)(Emax⁡(q)-Emax⁡(q)_)∑q=1Q⁢(E(max+(i-3))⁡(q)-E(max+(i-3))⁡(q)_)2∑q=1Q⁢(Emax⁡(q)-Emax⁡(q)_)2,⁢i≥4rm=16⁢∑i=16⁢riFormula⁢⁢II

where i represents an ordinal number of correlation coefficient and i=1, 2, 3, 4, 5, 6, E5(l,q) represents a signal of a middle channel after the low-pass filtering in the slow time dimension obtained in the step 2.5, Emax(q) represents a signal at a position of the maximum of E5(l,q), E(max+(i−4))(q) represents signals at the first three positions adjacent to the position of the maximum of E5(l,q), E(max+(i−3))(q) represents signals at the last three positions adjacent to the position of the maximum value of E5(l,q), Q represents the total number of sampling points of the signal of E5(l,q) in the slow time direction and Q is a positive integer, and q represents the q-th signal sampling point in the slow time direction and q is a positive integer.

Compared with the related art, the present disclosure has following technical characteristics.

1. In the disclosure, according to signal characteristics of human and animal breathing, a set of target detection and distinction procedures that combine two characteristic parameters of energy-to-noise ratio and the correlation coefficient average, and multiple thresholds, is mainly adopted to realize detection and distinction between a human and an animal, and to give a distance to the target.

2. In the present disclosure, the problem of energy attenuation with increasing the distance in the transmission process of radar signals is solved, by pre-determining the target position and by performing attenuation compensation on the signal in the distance upwards according to the calculated exponential gain curve G(l). In this way, a miss-detection rate of a target in the distance is effectively reduced. In addition, compared with the segmental linear gain compensation method, the gain compensation adopted by the present disclosure is more accurate.

3. Physical significances, formulas, and calculation methods of the two characteristic parameters of the energy-to-noise ratio and the correlation coefficient average is provided according to the present disclosure, to give an optimal distinction threshold of each parameter on this basis. Under this optimal threshold condition, the detection and distinction method proposed by the present disclosure can distinguish a human from animals such as dogs, cats, and poultry with a high recognition accuracy rate.

