CN114280368B - Burst signal detection method and system in complex environment - Google Patents
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Abstract
The invention discloses a burst signal detection method in a complex environment, which comprises the following steps: step 1: obtaining cached spectrum data, and carrying out FIFO queuing cache on the spectrum data; step 2: when the size of the FIFO queue reaches a set value, differential calculation is carried out, and the oldest frame data at the bottom is discarded to obtain a differential result; step 3: performing conventional threshold detection on the differential result in the step 2 to obtain a detected signal; step 4: performing FIFO (first in first out) queuing and caching on the detected signals obtained in the step 3, and counting signals in the queues to obtain reliable detection results; step 5: filtering the detection result in the step 4 to obtain a signal detection result; step 6: performing burst signal judgment on the signal detection result obtained in the step 5 to obtain a burst signal detection result; the invention has small calculated amount and simple realization, and can accurately find and detect burst signals which appear from nothing to nothing in various complex environments.
Description
Technical Field
The invention relates to the technical field of signal detection, in particular to a burst signal detection method and system in a complex environment.
Background
In non-cooperative communication, the primary task of signal analysis is to acquire three-dimensional characteristics of target signal frequency, time and power from a complex electromagnetic environment, and the acquisition of the three-dimensional characteristics of the signal mainly depends on a signal detection technology. The existing signal detection methods are mainly divided into two types, namely, whether signals conforming to frequency and power characteristics exist in a frequency range or not is observed through Fourier transformation. If yes, then judging whether the time characteristic is met or not through accumulated acquisition, thereby acquiring the target signal and measuring the frequency, time and power three-dimensional characteristic parameters of the target signal. The method is simple to implement, small in calculation amount and good in signal effect. And secondly, three-dimensional characteristics such as frequency, time and power of the signals are directly analyzed by adopting wavelet transformation, short-time Fourier transformation and other technologies. The method has complex realization and large calculation amount, and can capture burst signals and frequency hopping signals.
Disclosure of Invention
The invention provides a burst signal detection method and a burst signal detection system in a complex environment, which have small calculated amount and simple realization, aiming at the problems existing in the prior art.
The technical scheme adopted by the invention is as follows: a burst signal detection method under a complex environment comprises the following steps:
step 1: obtaining cached spectrum data, and carrying out FIFO queuing cache on the spectrum data;
step 2: when the size of the FIFO queue reaches a set value, taking the latest frame and the oldest frame in the queue for differential calculation, and discarding the data of the oldest frame at the bottom to obtain a differential result;
step 3: performing conventional threshold detection with the threshold of 0 on the differential result in the step 2 to obtain a detected signal;
step 4: performing FIFO (first in first out) queuing and caching on the detected signals obtained in the step 3, and counting signals in the queues to obtain reliable detection results;
step 5: filtering the detection result in the step 4 to obtain a signal detection result;
step 6: and (5) carrying out burst signal judgment on the signal detection result obtained in the step (5) to obtain a burst signal detection result.
Further, the method for FIFO queue buffering in step 1 is as follows: reading frame data of a frame, pressing the frame data into the uppermost part of a queue, and repeating the process; when the size of the queue reaches a preset value, the oldest frame of data is deleted from the tail of the queue.
Further, the differential calculation method in the step 2 is as follows: when the FIFO queue is larger than a preset value, the top and bottom frames of data in the FIFO queue are taken, and the point-to-point power value of the bottom data is subtracted from the point-to-point power value of the top data to obtain a difference result.
Further, the conventional threshold detection method in the step 3 is as follows: setting a threshold value to be 0, judging the power value of the frequency spectrum data point by point according to the differential result, and searching a continuous interval with not less than three points, power greater than 0 and the maximum power value within the threshold value; each interval is used as a detection result, frequency and time parameters are extracted, and power parameters of signals are obtained from an original frequency spectrum according to frequency information; the time parameter and the power parameter are detected.
Further, the method for obtaining the reliable detection result in the step 4 is as follows: when the size of the FIFO queue reaches a preset value, counting the detection times n of each signal, and if n is more than or equal to b, considering the signal as a reliable detection result; b is a threshold.
Further, the burst signal determination method in step 6 in step 5 is as follows: when the signal disappears, whether the duration of the signal is smaller than a set maximum duration threshold value of the burst signal is judged, and if the duration of the signal is smaller than the set maximum duration threshold value of the burst signal, the burst signal is judged.
The detection system of the burst signal detection method in the complex environment comprises a buffer module, a differential processing module, a signal detection module, a signal extraction module, a filter module and a burst judgment module;
and a buffer module: the method comprises the steps of obtaining cached spectrum data, and carrying out FIFO queuing cache on the spectrum data;
and the differential processing module is used for: the method comprises the steps of reading cache data, and performing differential processing on the cache data to obtain a differential result;
and the signal detection module is used for: the method comprises the steps of reading a differential result, and performing conventional threshold detection on the differential result to obtain a detection result;
and a signal extraction module: the method comprises the steps of reading a detection result, and performing FIFO (first in first out) queuing and caching on the detection result to obtain a reliable detection result;
and a filtering module: the method is used for reading reliable detection results, and filtering signal parameters to obtain signal detection results;
and a burst judging module: and the method is used for reading the signal detection result and carrying out signal burst judgment to obtain a burst signal detection result.
The beneficial effects of the invention are as follows:
(1) The invention has small calculated amount, and the calculated amount is mainly differential calculation and threshold detection of spectrum data;
(2) The invention has simple realization and can accurately capture the burst new signal in various complex environments;
(3) The invention can filter out spurious signals in complicated environment.
Drawings
Fig. 1 is a diagram of a burst signal detection framework according to the present invention.
Fig. 2 is a flow chart of a burst signal detection method according to the present invention.
FIG. 3 is a schematic diagram of a dynamic differential method according to an embodiment of the invention.
Detailed Description
The invention will be further described with reference to the drawings and specific examples.
The FIFO queue buffer is a buffer data structure with fixed size and first-in first-out.
As shown in fig. 1 and 2, a burst signal detection method in a complex environment includes the following steps:
step 1: obtaining cached spectrum data, and carrying out FIFO queuing cache on the spectrum data;
the method for FIFO queuing and caching is as follows: reading frame data of a frame, pressing the frame data into the uppermost part of a queue, and repeating the process; when the size of the queue reaches a preset value, the oldest frame of data is deleted from the tail of the queue. The process is to realize dynamic difference of spectrum data, and provides preconditions for the following steps.
Step 2: when the size of the FIFO queue reaches a set value, taking the latest frame and the oldest frame in the queue for differential calculation, and discarding the data of the oldest frame at the bottom to obtain a differential result;
the differential calculation method is as follows: when the FIFO queue is larger than a preset value, the top and bottom two frames of data (the latest frame is the top frame data and the oldest frame is the bottom frame data) in the FIFO queue are taken, and the point power value of the bottom data is subtracted from the point power value of the top data to obtain a differential result. The process is to obtain spectrum data with a threshold of 0 and flat background noise so as to perform conventional threshold detection.
Step 3: performing conventional threshold detection with the threshold of 0 on the differential result in the step 2 to obtain a detected signal;
the conventional threshold detection method is as follows: setting a threshold value to be 0, judging the power value of the frequency spectrum data point by point according to the differential result, and searching continuous intervals with not less than three points, power greater than 0 and maximum power value within the threshold value (in the embodiment, 3dB or more is selected as the threshold value); each interval is used as a detection result, frequency and time parameters are extracted, and power parameters of signals are obtained from an original frequency spectrum according to frequency information; the time parameter and the power parameter are detected.
Step 4: performing FIFO (first in first out) queuing and caching on the detected signals obtained in the step 3, and counting signals in the queues to obtain reliable detection results;
the method for obtaining reliable detection results is as follows: when the size of the FIFO queue reaches a preset value, counting the detection times n of each signal, and if n is more than or equal to b, considering the signal as a reliable detection result; b is a threshold. This step is to filter out unreliable false detection signals.
Step 5: filtering the detection result in the step 4 to obtain a signal detection result; the specific method is to screen out the signal detection results meeting the conditions according to the set signal frequency, bandwidth, power and time range. This step is to reveal the signal detection results within the custom range of the user.
Step 6: and (5) carrying out burst signal judgment on the signal detection result obtained in the step (5) to obtain a burst signal detection result.
The burst signal judging method comprises the following steps: when the signal disappears, whether the duration of the signal is smaller than a set maximum duration threshold value of the burst signal is judged, and if the duration of the signal is smaller than the set maximum duration threshold value of the burst signal, the burst signal is judged. When in use, the caller sets the maximum duration threshold value of the burst signal through the parameter configuration interface. When the signal disappears, determining whether the duration of the signal is less than a set maximum duration threshold value of the burst signal, if so, determining that the signal is burst
The detection system of the burst signal detection method in the complex environment comprises a buffer module, a differential processing module, a signal detection module, a signal extraction module, a filter module and a burst judgment module;
and a buffer module: the method comprises the steps of obtaining cached spectrum data, and carrying out FIFO queuing cache on the spectrum data;
and the differential processing module is used for: the method comprises the steps of reading cache data, and performing differential processing on the cache data to obtain a differential result;
and the signal detection module is used for: the method comprises the steps of reading a differential result, and performing conventional threshold detection on the differential result to obtain a detection result;
and a signal extraction module: the method comprises the steps of reading a detection result, and performing FIFO (first in first out) queuing and caching on the detection result to obtain a reliable detection result;
and a filtering module: the method is used for reading reliable detection results, and filtering signal parameters to obtain signal detection results;
and a burst judging module: and the method is used for reading the signal detection result and carrying out signal burst judgment to obtain a burst signal detection result.
The calculated amount of the invention is mainly differential calculation and threshold detection of spectrum data, the part can be realized on a general radio digital receiver platform, and carrier detection and result accumulation reporting can be realized on a DSP or a general PC.
One implementation of the signal detection method will be briefly described using a general-purpose radio digital receiver and a general-purpose PC.
And (3) carrying out continuous spectrum scanning on the target frequency band by using a universal radio digital receiver, collecting power spectrum density data, and carrying out spectrum splicing to form a whole frame. And then, the digital receiver uploads the spliced power spectrum density data to the upper computer software through a LAN (local area network) port or a USB (universal serial bus) port.
And the upper computer software background calls a signal detection algorithm dynamic interface and loads data to be detected.
The algorithm library performs dynamic interval difference on the loaded frequency spectrum data: setting the size of the FIFO queue as D, using the FIFO queue to segment and buffer the latest D frame data, and subtracting the nth-D frame from the nth frame to obtain differential data, thus continuously and dynamically differentiating as shown in figure 3.
With the threshold as 0, detecting the threshold signal of the differential data
And carrying out FIFO queue buffering on the detected signals of each frame of frequency spectrum, and filling empty signals if the detected signals are not detected. And if the signal FIFO queue is full, counting the number of effective signals in the queue, and judging whether the number of effective signals in the queue meets a preset value. For example, the queue size is 10, and the number of valid signals is required to be not less than 8.
And performing operations such as data caching, dynamic difference, signal detection, result statistics reporting and the like on the power spectrum data, and finally displaying the reporting result to an interface or guiding the lateral positioning equipment to further process.
The invention uses the frequency spectrum data after Fourier transformation as the source data to detect the signals, and the differential data obtained after the data is differential has flat and approximate 0 bottom noise. The method can directly take the value of 0 as the threshold to perform conventional threshold detection, so that the calculated amount is small and the speed is high. After the spectrum data is differentiated, the background noise unevenness and the common signals caused by various complex environments can be filtered, and only the signals which are suddenly appeared from nothing to nothing can be aimed at. Thus enabling the capture of bursty new signals in a variety of complex environments. The detected signals pass through the cache statistics of the FIFO queue, and the signals are judged to be real signals only when the signals appear for a certain number of times according to the set parameters, so that spurious signals in complex environments can be filtered.
Claims (3)
1. The burst signal detection method in the complex environment is characterized by comprising the following steps:
step 1: obtaining cached spectrum data, and carrying out FIFO queuing cache on the spectrum data;
step 2: when the size of the FIFO queue reaches a set value, taking the latest frame and the oldest frame in the queue for differential calculation, and discarding the data of the oldest frame at the bottom to obtain a differential result; the differential calculation method is as follows: when the FIFO queue is larger than a preset value, taking top and bottom two frames of data in the FIFO queue, and subtracting the point-to-point power value of the bottom data from the point-to-point power value of the top data to obtain a difference result;
step 3: performing conventional threshold detection with the threshold of 0 on the differential result in the step 2 to obtain a detected signal; the conventional threshold detection method is as follows: setting a threshold value to be 0, judging the power value of the frequency spectrum data point by point according to the differential result, and searching a continuous interval with not less than three points, power greater than 0 and the maximum power value within the threshold value; each interval is used as a detection result, frequency and time parameters are extracted, and power parameters of signals are obtained from an original frequency spectrum according to frequency information; the time parameter and the power parameter are detected;
step 4: performing FIFO (first in first out) queuing and caching on the detected signals obtained in the step 3, and counting signals in the queues to obtain reliable detection results; the method for obtaining reliable detection results is as follows: when the size of the FIFO queue reaches a preset value, counting the detection times n of each signal, and if n is more than or equal to b, considering the signal as a reliable detection result; b is a threshold;
step 5: filtering the detection result in the step 4 to obtain a signal detection result;
step 6: performing burst signal judgment on the signal detection result obtained in the step 5 to obtain a burst signal detection result; the burst signal judging method comprises the following steps: when the signal disappears, whether the duration of the signal is smaller than a set maximum duration threshold value of the burst signal is judged, and if the duration of the signal is smaller than the set maximum duration threshold value of the burst signal, the burst signal is judged.
2. The method for detecting burst signals in a complex environment according to claim 1, wherein the method for FIFO queuing and buffering in step 1 is as follows: reading frame data of a frame, pressing the frame data into the uppermost part of a queue, and repeating the process; when the size of the queue reaches a preset value, the oldest frame of data is deleted from the tail of the queue.
3. The detection system adopting the burst signal detection method under any one of the complex environments as claimed in claims 1-2, which is characterized by comprising a buffer module, a differential processing module, a signal detection module, a signal extraction module, a filter module and a burst judgment module;
and a buffer module: the method comprises the steps of obtaining cached spectrum data, and carrying out FIFO queuing cache on the spectrum data;
and the differential processing module is used for: the method comprises the steps of reading cache data, and performing differential processing on the cache data to obtain a differential result;
and the signal detection module is used for: the method comprises the steps of reading a differential result, and performing conventional threshold detection on the differential result to obtain a detection result;
and a signal extraction module: the method comprises the steps of reading a detection result, and performing FIFO (first in first out) queuing and caching on the detection result to obtain a reliable detection result;
and a filtering module: the method is used for reading reliable detection results, and filtering signal parameters to obtain signal detection results;
and a burst judging module: and the method is used for reading the signal detection result and carrying out signal burst judgment to obtain a burst signal detection result.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5524282A (en) * | 1990-03-28 | 1996-06-04 | Matsushita Electric Industrial Co., Ltd. | Apparatus and method for detecting place of burst signals in time division multiplex communication system |
| CN102332933A (en) * | 2011-09-28 | 2012-01-25 | 上海交通大学 | Short-term burst spread spectrum signal transmitting and receiving method |
| CN107733516A (en) * | 2017-09-20 | 2018-02-23 | 西安空间无线电技术研究所 | A kind of burst communication signals detection method |
| CN108155959A (en) * | 2017-12-15 | 2018-06-12 | 成都爱科特科技发展有限公司 | UAV TT ＆ C's signal detecting method under a kind of complex network environment |
| CN112737983A (en) * | 2020-12-24 | 2021-04-30 | 成都戎星科技有限公司 | Rapid burst signal detection method based on maximum difference spectrum |
| CN113259029A (en) * | 2021-05-04 | 2021-08-13 | 中国人民解放军32802部队 | Real-time automatic detection and identification method suitable for unmanned aerial vehicle signals |
| CN113810162A (en) * | 2021-09-17 | 2021-12-17 | 四川安迪科技实业有限公司 | Presence detection method for random burst signal containing preamble |
-
2021
- 2021-12-28 CN CN202111627891.XA patent/CN114280368B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5524282A (en) * | 1990-03-28 | 1996-06-04 | Matsushita Electric Industrial Co., Ltd. | Apparatus and method for detecting place of burst signals in time division multiplex communication system |
| CN102332933A (en) * | 2011-09-28 | 2012-01-25 | 上海交通大学 | Short-term burst spread spectrum signal transmitting and receiving method |
| CN107733516A (en) * | 2017-09-20 | 2018-02-23 | 西安空间无线电技术研究所 | A kind of burst communication signals detection method |
| CN108155959A (en) * | 2017-12-15 | 2018-06-12 | 成都爱科特科技发展有限公司 | UAV TT ＆ C's signal detecting method under a kind of complex network environment |
| CN112737983A (en) * | 2020-12-24 | 2021-04-30 | 成都戎星科技有限公司 | Rapid burst signal detection method based on maximum difference spectrum |
| CN113259029A (en) * | 2021-05-04 | 2021-08-13 | 中国人民解放军32802部队 | Real-time automatic detection and identification method suitable for unmanned aerial vehicle signals |
| CN113810162A (en) * | 2021-09-17 | 2021-12-17 | 四川安迪科技实业有限公司 | Presence detection method for random burst signal containing preamble |
Non-Patent Citations (1)
| Title |
|---|
| 通信信号盲检测技术研究;隋丹;《中国优秀硕士学位论文全文数据库 信息科技辑》;I136-14 * |
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