CN116781892A - PIR signal high-precision detection method of hunting camera - Google Patents
PIR signal high-precision detection method of hunting camera Download PDFInfo
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Abstract
The invention discloses a PIR signal high-precision detection method of a hunting camera, which relates to the field of PIR signal high-precision detection methods and is provided with the following scheme that S1, preparation work: installing the PIR sensor at a specified position to be detected while ensuring that the PIR sensor is normally connected to a power supply and the ground; s2, testing environment: the moderate light of the test environment is ensured, the influence of direct sunlight and strong light sources is avoided, and the test area is ensured to have no other interferents and moving objects; s3, triggering test: the PIR sensor is triggered by moving or activating other objects in the test area, and a target PIR signal in the detection area is obtained. The invention effectively makes up the defect of a single algorithm by adopting the joint detection among different algorithms and the collaborative detection among different secondary users, reasonably selects various algorithm combinations according to monitoring objects, detection requirements and the like in practical application, and simultaneously improves the efficiency and the effect of high-precision detection of PIR signals of a hunting camera.
Description
Technical Field
The invention relates to a PIR signal high-precision detection method, in particular to a PIR signal high-precision detection method of a hunting camera.
Background
The invention provides a method for accurately detecting PIR signals of a hunting camera, which is characterized in that the hunting camera is also called an infrared induction camera, is a novel product between the camera and a monitoring camera, can form a system by itself, adopts infrared DSP intelligent operation, namely a low false alarm automatic human body (animal) identification technology, automatically shoots high-definition pictures or videos, in addition, the infrared camera can automatically start shooting or shooting video clips according to the setting of the infrared camera when an infrared heat source is induced, the hunting camera can carry out the acquisition identification processing of the pictures through infrared induction, but when PIR signals are weakened, the acquisition of the pictures is unclear, network delay is caused in image capturing and identification, and meanwhile, the quick identification of the hunting camera is influenced.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a PIR signal high-precision detection method of a hunting camera.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a PIR signal high-precision detection method of a hunting camera comprises the following steps:
s1, preparing: installing the PIR sensor at a specified position to be detected while ensuring that the PIR sensor is normally connected to a power supply and the ground;
s2, testing environment: the moderate light of the test environment is ensured, the influence of direct sunlight and strong light sources is avoided, and the test area is ensured to have no other interferents and moving objects;
s3, triggering test: the PIR sensor is triggered by moving or activating other objects in the test area to acquire target PIR signals in the detection area, so that the detection can be performed by using human body movement, animals and hot spot source modes, during detection, the frequency spectrum is segmented by FFT stepping frequency, current and voltage mutation and out-of-limit encountered during each data segment are detected, and for alarm information, the alarm information can be sent to related personnel through a mobile short message terminal to enable the related personnel to know the situation and make corresponding measures in time;
s4, calculating the moving characteristic points: performing operation of moving characteristic points on the target PIR signal so as to obtain a peak value and a period of the target PIR signal;
s5, observing reaction: when the PIR sensor is triggered, whether the response accords with the expectation is observed, and whether the sensor works normally can be judged by observing an indicator lamp and an output signal mode of the sensor;
s6, verifying results: repeated testing is carried out for a plurality of times, so that the accuracy and the stability of the PIR sensor are ensured, and the test result including the trigger time, the response time and the false trigger condition of the sensor can be recorded;
s7, adjusting parameters: if the test result is not as expected, parameters of the PIR sensor, such as the adjustment of the sensing distance, the sensing angle, the delay time, etc., can be adjusted according to the requirements and the actual situation, and the test is performed again, and it should be noted that different types of PIR sensors may have differences in parameter adjustment and test methods, specific test steps should be operated with reference to the specifications or related documents of the sensors, and meanwhile, safety should be noted during the test process, so as to avoid triggering other safety devices or causing personnel injury.
Further, the preparation of S1 adjusts parameters of the PIR sensor, such as sensing distance, sensing angle and delay time, according to specifications and instructions of the PIR sensor.
Further, the S3 trigger test further includes: performing vibration characteristic point operation on the target PIR signal to obtain the maximum signal change rate of the target PIR signal, wherein a time-frequency detection method is generally adopted for trigger test, the time-frequency detection method can respectively analyze signals from a time domain and a frequency domain at the same time, and the method for performing signal detection from a time domain angle is to obtain test statistics through performing correlation operation on a result of sampling a time domain waveform of a real-time signal so as to realize signal detection, and comprises matched filtering detection and an energy detection algorithm;
and performing signal detection from the frequency domain angle, namely performing signal detection by utilizing the characteristics such as amplitude spectrum, power spectrum, phase difference and the like, including cyclostationary characteristic detection and a high-order cumulative spectrum detection algorithm.
Further, the peak value and period calculation in the S4 mobile characteristic point operation is that firstly, the algorithm judges abnormal energy signals through a threshold value (namely, an amplitude threshold), according to the convention of manually judging signals in the radio monitoring industry, the threshold value is 5dB added to the background noise level of the frequency band, only if the amplitude exceeds the level value frequency point, the signals are judged, and when the signals are judged, an important parameter is provided: the peak value threshold is used for defining how much higher than zero point of the threshold or nearby is a signal, and can be judged as effective energy detected.
Further, after the threshold is calculated, the threshold calculation can be seen from the algorithm of energy detection by the S5 observation reaction: threshold setting and calculation are the precondition and key for signal energy detection, and three thresholds are designed for adapting to different electromagnetic environments: level threshold, environmental threshold, and automatic threshold;
the level threshold is the simplest threshold, the threshold is a level designated by a user, the level is used as the threshold to judge signals in the whole scanning process, and the level threshold is applied to the condition that the background noise fluctuates little, such as: monitoring a single-band frequency spectrum;
the environmental threshold is to measure a specific environment for a plurality of times, keep the frequency spectrum of each scanning to the maximum, take the trace curve +5dB (industry practice) obtained by the plurality of times as the threshold, the threshold distinguishing mode can well find or distinguish the newly appeared signal in the environment, but needs to perform the generating work of the environmental threshold firstly, the threshold mode is generally to perform the generating work of the environmental threshold in advance, the threshold curve is stored in the signal monitoring analyzer as the environmental noise level of the area, the threshold is provided for the subsequent monitoring task, and before the energy frequency and bandwidth value detected after the parameters of the signal are acquired are sent into the energy history record, the set of values are the update of the new energy or the existing energy, and a set of energy history record entries are updated or added according to a certain criterion.
Further, the cyclostationary feature detection calculation is performed, the algorithm converts the statistical average into the time average, a decision of the cyclostationary feature detection algorithm is obtained, whether a signal exists or not is judged according to the power spectral density, the algorithm has strong noise interference resistance, is less affected by noise uncertainty, and has higher calculation complexity and needs longer detection time.
Further, the high-order cumulative spectrum detection algorithm is widely applied to signal detection in a non-Gaussian environment, and can be used for detecting signals under the condition that a probability density function of noise amplitude is symmetrical about a longitudinal axis and an odd-order origin moment is zero, gaussian noise is not required, because the amplitude of the signals has specific distribution, the odd-order origin moment is not zero, high-order statistics mainly comprise high-order moment, high-order cumulant and high-order cumulant spectrum, wherein the high-order cumulant is insensitive to a Gaussian process, the influence of noise can be completely restrained theoretically, the high-order moment does not have the property, and therefore the high-order cumulant is used more frequently.
Further, the matched filter detection algorithm is used for passing the signal through a linear filter, sampling and judging the signal at a fixed time, the computational complexity is low, and the output signal-to-noise ratio at a specific time can be maximized, when the signal is expressed as the following formula:
the impulse response of the corresponding matched filter is:。
further, the energy detection algorithm is used for obtaining statistics by directly performing modulo and squaring on a time domain signal sampling value when the signal exists, wherein the received sampling sequence is superposition of noise and the signal, the energy of the energy detection algorithm is stronger than that of the signal when only the environmental noise exists, the detection implementation process is simple, and the statistics can be obtained as shown in the following formula:where r (k) is a sampling sequence of the receiver, N is the length of the sequence, and when the target signal exists, its statistic is larger than that of the noise alone, and if it is larger than the threshold gamma, it is considered thatIf the signal appears, the complexity of the energy detection algorithm is regarded as theta (N) if the signal appears and is smaller than the threshold, the detection performance is stable and is only related to the signal to noise ratio, and the performance is poor under the condition of low signal to noise ratio, so that the algorithm is not suitable for detecting weak signals, the algorithm cannot effectively distinguish main signals or interference signals, and the actual performance also fluctuates due to the uncertainty of noise;
for each data segment, a set threshold value is applied, an energy detection algorithm is used for detecting all energy peaks in the data segment, signal parameters are extracted after signals are found, energy prefiltering is carried out according to set attention conditions, energy history is updated, possible excitation alarms are processed, after all data segments are processed, energy postfiltering is carried out, signals are further screened through comparison with the characteristics of a known signal library, and the process is repeated under the condition that search circulation is carried out;
for signal detection, the energy detection method has low detection performance under low signal-to-noise ratio, the cyclostationary detection method has strong noise immunity, but the calculation complexity is higher, so that the energy detection method and the cyclostationary detection method can be considered to be combined for double-threshold detection.
The invention effectively makes up the defect of a single algorithm by adopting the joint detection among different algorithms and the collaborative detection among different secondary users, reasonably selects various algorithm combinations according to monitoring objects, detection requirements and the like in practical application, and simultaneously improves the efficiency and the effect of high-precision detection of PIR signals of a hunting camera.
Drawings
Fig. 1 is a block diagram of the PIR signal high-precision detection method of the hunting camera according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise. Furthermore, the terms "mounted," "connected," "coupled," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1: a PIR signal high-precision detection method of a hunting camera comprises the following steps:
s1, preparing: installing the PIR sensor at a specified position to be detected while ensuring that the PIR sensor is normally connected to a power supply and the ground;
s2, testing environment: the moderate light of the test environment is ensured, the influence of direct sunlight and strong light sources is avoided, and the test area is ensured to have no other interferents and moving objects;
s3, triggering test: the PIR sensor is triggered by moving or activating other objects in the test area to acquire target PIR signals in the detection area, so that the detection can be performed by using human body movement, animals and hot spot source modes, during detection, the frequency spectrum is segmented by FFT stepping frequency, current and voltage mutation and out-of-limit encountered during each data segment are detected, and for alarm information, the alarm information can be sent to related personnel through a mobile short message terminal to enable the related personnel to know the situation and make corresponding measures in time;
s4, calculating the moving characteristic points: performing operation of moving characteristic points on the target PIR signal, so as to obtain a peak value and a period of the target PIR signal;
s5, observing reaction: when the PIR sensor is triggered, whether the response accords with the expectation is observed, and whether the sensor works normally can be judged by observing an indicator lamp and an output signal mode of the sensor;
s6, verifying results: repeated testing is carried out for a plurality of times, so that the accuracy and the stability of the PIR sensor are ensured, and the test result including the trigger time, the response time and the false trigger condition of the sensor can be recorded;
s7, adjusting parameters: if the test result is not as expected, parameters of the PIR sensor, such as the sensing distance, sensing angle and delay time, can be adjusted according to the requirements and actual conditions, and the test is performed again, and it should be noted that different types of PIR sensors may have differences in parameter adjustment and test methods, specific test steps should be operated with reference to the specifications or related documents of the sensors, and meanwhile, safety needs to be paid attention to during the test process, so as to avoid triggering other safety devices or causing personnel injury.
In the invention, the preparation work of S1 adjusts parameters of the PIR sensor, such as sensing distance, sensing angle and delay time, according to the specification and the use description of the PIR sensor.
In the invention, the S3 trigger test further comprises: performing vibration characteristic point operation on a target PIR signal to obtain the maximum signal change rate of the target PIR signal, wherein a time-frequency detection method is generally adopted for trigger test, the time-frequency detection method can respectively analyze the signal from a time domain and a frequency domain at the same time, and the method for performing signal detection from a time domain angle is to obtain test statistics through performing correlation operation on a result of sampling a time domain waveform of a real-time signal so as to realize signal detection, and comprises matched filtering detection and an energy detection algorithm;
and performing signal detection from the frequency domain angle, namely performing signal detection by utilizing the characteristics such as amplitude spectrum, power spectrum, phase difference and the like, including cyclostationary characteristic detection and a high-order cumulative spectrum detection algorithm.
In the invention, the peak value and period calculation in S4 mobile characteristic point operation, the algorithm firstly judges abnormal energy signals through a threshold value (namely, an amplitude threshold), according to the convention of manually judging signals in the radio monitoring industry, the threshold value is 5dB added to the background noise level of the frequency band, the signals are judged only if the amplitude exceeds the level value frequency point, and when the signals are judged, an important parameter is provided: the peak value threshold is used for defining how much higher than zero point of the threshold or nearby is a signal, and can be judged as effective energy detected.
In the invention, after the threshold value is calculated by the S5 observation reaction, the threshold value calculation can be seen from an algorithm of energy detection: threshold setting and calculation are the precondition and key for signal energy detection, and three thresholds are designed for adapting to different electromagnetic environments: level threshold, environmental threshold, and automatic threshold;
the level threshold is the simplest threshold, the threshold is a level designated by a user, the level is used as the threshold to determine a signal in the whole scanning process, and the level threshold is applied to the situation that the background noise fluctuates little, such as: monitoring a single-band frequency spectrum;
the environmental threshold is to measure a specific environment for a plurality of times, keep the frequency spectrum of each scanning to the maximum, take the trace curve +5dB (industry practice) obtained by the plurality of times as the threshold, the threshold distinguishing mode can well find or distinguish the newly appeared signal in the environment, but needs to perform the generating work of the environmental threshold first, the threshold mode is generally to perform the generating work of the environmental threshold in advance, store the threshold curve in the signal monitoring analyzer as the environmental noise level of the area, provide the threshold for the subsequent monitoring task, and judge whether the set of values are new energy or update of the existing energy according to a certain criterion before the energy frequency and bandwidth value detected after the parameters of the signal are acquired are sent into the energy history record, update or add a set of energy history record entries.
In the invention, the cyclostationary characteristic detection calculation is carried out, the algorithm converts the statistical average into the time average to obtain the judgment type of the cyclostationary characteristic detection algorithm, and then whether signals exist or not is judged according to the power spectral density.
In the invention, the high-order cumulative spectrum detection algorithm is widely applied to signal detection in a non-Gaussian environment, and can be used for detecting signals under the condition that a probability density function of noise amplitude is symmetrical about a longitudinal axis and an odd-order origin moment is zero, gaussian noise is not required, because the amplitude of the signals has specific distribution, the odd-order origin moment is generally not zero, and the high-order statistics mainly comprise high-order moment, high-order cumulant and high-order cumulant spectrums, wherein the high-order cumulant is insensitive to a Gaussian process, can completely inhibit the influence of noise in theory, but does not have the property, so that the use frequency of the high-order cumulant is higher.
In the invention, the matched filtering detection algorithm is used for carrying out signal sampling and judgment on a signal at a fixed moment through a linear filter, the calculation complexity is very low, the output signal-to-noise ratio at a specific moment can be maximized, and when the signal is expressed as the following formula:
the impulse response of the corresponding matched filter is:。
in the invention, the energy detection algorithm is used for obtaining statistics by directly carrying out modulo and square on the sampling value of the time domain signal when the signal exists, the received sampling sequence is superposition of noise and the signal, the energy is stronger than that of the signal when only the environmental noise exists, the detection implementation process is simple, and the statistics can be obtained as shown in the following formula:in the formula, r (k) is a sampling sequence of a receiver, N is the length of the sequence, when a target signal exists, the statistics of the target signal is larger than that of the target signal when the target signal exists, a threshold can be set according to the sampling sequence, if the target signal is larger than the threshold gamma, the signal is considered to appear, if the target signal is smaller than the threshold, no signal is considered, the complexity of an energy detection algorithm is θ (N), the detection performance is stable and is only related to the signal-to-noise ratio, the performance is poor under the condition of low signal-to-noise ratio, and therefore the target signal is not suitable for the detection of weak signals, the main signal or an interference signal cannot be effectively distinguished, and the actual performance of the target signal can also fluctuate due to the uncertainty of the noise;
for each data segment, a set threshold value is applied, an energy detection algorithm is used for detecting all energy peaks in the data segment, signal parameters are extracted after signals are found, energy prefiltering is carried out according to set attention conditions, energy history is updated, possible excitation alarms are processed, after all data segments are processed, energy postfiltering is carried out, signals are further screened through comparison with the characteristics of a known signal library, and the process is repeated under the condition that search circulation is carried out;
for signal detection, the energy detection method has low detection performance under low signal-to-noise ratio, the cyclostationary detection method has strong noise immunity, but the calculation complexity is higher, so that the energy detection method and the cyclostationary detection method can be considered to be combined for double-threshold detection.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (9)
1. The PIR signal high-precision detection method of the hunting camera is characterized by comprising the following steps of:
s1, preparing: installing the PIR sensor at a specified position to be detected while ensuring that the PIR sensor is normally connected to a power supply and the ground;
s2, testing environment: the moderate light of the test environment is ensured, the influence of direct sunlight and strong light sources is avoided, and the test area is ensured to have no other interferents and moving objects;
s3, triggering test: triggering a PIR sensor by moving or activating other objects in a test area to acquire a target PIR signal in a detection area;
s4, calculating the moving characteristic points: performing operation of moving characteristic points on the target PIR signal so as to obtain a peak value and a period of the target PIR signal;
s5, observing reaction: when the PIR sensor is triggered, observing whether its response meets expectations;
s6, verifying results: repeated testing is carried out for a plurality of times, so that the accuracy and stability of the PIR sensor are ensured;
s7, adjusting parameters: if the test result is not as expected, parameters of the PIR sensor, such as the sensing distance, the sensing angle, the delay time and the like, can be adjusted according to the requirements and the actual conditions, and the test can be carried out again.
2. The method for detecting PIR signal of hunting camera according to claim 1, wherein the preparation of S1 adjusts parameters of the PIR sensor, such as sensing distance, sensing angle and delay time, according to specifications and instructions of the PIR sensor.
3. The PIR signal high-precision detection method of a hunting camera according to claim 1, wherein the S3 trigger test further comprises: performing vibration characteristic point operation on the target PIR signal to obtain the maximum signal change rate of the target PIR signal, wherein a time-frequency detection method is generally adopted for trigger test, the time-frequency detection method can respectively analyze signals from a time domain and a frequency domain at the same time, and the method for performing signal detection from a time domain angle is to obtain test statistics through performing correlation operation on a result of sampling a time domain waveform of a real-time signal so as to realize signal detection, and comprises matched filtering detection and an energy detection algorithm;
and performing signal detection from the frequency domain angle, namely performing signal detection by utilizing the characteristics such as amplitude spectrum, power spectrum, phase difference and the like, including cyclostationary characteristic detection and a high-order cumulative spectrum detection algorithm.
4. The PIR signal high-precision detection method of hunting camera according to claim 1, wherein the peak value and period in the S4 moving feature point operation are calculated, the algorithm firstly judges abnormal energy signals through a threshold value (namely, an amplitude threshold), according to the convention of manually judging signals in the radio monitoring industry, the threshold value is 5dB higher than the background noise level of the frequency band, only if the amplitude exceeds the frequency point of the level value, the signal is judged, and when the signal judgment is carried out, an important parameter is provided: the peak value threshold is used for defining how much higher than zero point of the threshold or nearby is a signal, and can be judged as effective energy detected.
5. The PIR signal high-precision detection method of hunting camera according to claim 1, wherein after the threshold is calculated, the S5 observation response is passed through, and before the detected energy frequency and bandwidth values after the parameters of the signal are obtained are sent to the energy history, it is necessary to determine whether the set of values are new energy or update of existing energy according to a certain criterion, update or add a set of energy history entries.
6. A PIR signal high-precision detection method of a hunting camera according to claim 3, wherein the cyclostationary feature detection calculation is performed by converting a statistical average into a time average to obtain a decision formula of the cyclostationary feature detection algorithm, and determining whether a signal exists according to a power spectral density.
7. A PIR signal high-precision detection method of a hunting camera according to claim 3, wherein the high-order cumulative spectrum detection algorithm is widely applied to signal detection in a non-gaussian environment, and can be used to detect signals without requiring gaussian noise when a probability density function of noise amplitude is symmetric about a vertical axis and an odd-order origin moment is zero.
8. A PIR signal high-precision detection method of hunting camera according to claim 3, wherein the matched filtering detection algorithm is used for sampling and deciding signals at a fixed time by passing the signals through a linear filter, and the calculation complexity is low, and the output signal-to-noise ratio at a specific time can be maximized.
9. A PIR signal high accuracy detection method for a hunting camera according to claim 3, wherein the energy detection algorithm is adapted to receive a sequence of samples as a superposition of noise and signal when the signal is present, which is more energy than when only ambient noise is present.
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| CN116489340A (en) * | 2023-04-11 | 2023-07-25 | 深圳市龙之源科技股份有限公司 | Outdoor camera aging testing device and control method thereof |
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| US20170131149A1 (en) * | 2015-11-05 | 2017-05-11 | Google Inc. | Passive Infrared Sensor Self Test With Known Heat Source |
| CN114636476A (en) * | 2022-02-23 | 2022-06-17 | 漳州立达信光电子科技有限公司 | PIR-based mobile detection method, system and medium |
| CN116389717A (en) * | 2023-04-11 | 2023-07-04 | 深圳市龙之源科技股份有限公司 | Outdoor camera detection device and control method thereof |
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