RU2456638C1 - Method of searching for and identifying ionising radiation sources - Google Patents

Abstract

FIELD: physics.

SUBSTANCE: method of searching for and identifying ionising radiation sources using ionisation counters or scintillation detectors moved on the search territory (zone), involving continuous measurement of the mean speed of counting the additive mixture of a signal and a background n(t) as the detecting system moves on the investigated territory; processing the obtained measurement information by breaking the vector n(t) into exposure time sections t_exp, the duration of each of which is equal and is preferably (8-15)·t_s; determining the mean speed of counting the background b, the amplitude of the assumed signal a_m, and the position of the signal t_m on the time scale; further calculating a parameter η using the formula

where t_s is the effective duration of the signal; comparing η with a threshold q₀, which is determined from given false alarm probability; and making a decision on detecting the sought object if η>q₀, wherein the obtained measurement information is processed by successively increasing the effective duration of the signal t_s (1.3±0.1) times from the minimum t_s1=4-t_∂, then t_s2=6·t_∂, t_s3=8·t_∂, … etc to the maximum value t_s≤100 s, (where t_∂ is the discretisation time interval of the vector n(t)); calculating the parameter η_j using (1) for each value t_sj, and for that value from t_sj=t_em for which the parameter assumes maximum value η_m; calculating the estimate value of the unknown distance between the detector and the ionising radiation source using the formula R*=k_k·υ₀·t_em, where υ₀ is the mean velocity of the detecting system, coefficient k_k is equal to 2.0-2.8 depending on the design of the detector.

EFFECT: high accuracy of estimating the location of a detected source.

Description

The inventive method relates to the field of radiation monitoring using ionization counters (proportional or Geiger counters) or scintillation detectors and is intended to search, detect and estimate the coordinates of the location of sources of ionizing radiation (AI) lost or unauthorized and deliberately hidden, for example, in cases of illegal burial radioactive waste, etc. The inventive method can be used in portable portable or mobile radiation monitoring devices (RK), for example, in automobile or helicopter devices of the RK.

Known methods for searching and detecting sources of ionizing radiation used in portable devices for operational radiation monitoring [1-4]. Such devices are, as a rule, portable radiometers-dosimeters, into which an electronic threshold device and corresponding sound and (or) light signaling of exceeding the threshold are introduced. The mentioned devices (systems) use various counting detectors of ionizing radiation (either scintillation counters or ionization counters of gamma and neutron radiation).

So, in one of the relatively new developments - the search dosimeter type ДРС-РМ1401 (Polimaster joint venture, Minsk) [1], built on the basis of a scintillation counter with a photodiode, the simplest method of search and detection based on the use of a constant (hard set) detection threshold, depending on the initially measured background level, and reduced to the following sequence of operations:

- before starting the control of the checked objects, the number of pulses of background radiation N _{b is} measured for a time t _b = 36 s;

- the device is switched to the operational control mode, i.e. measure the number of radiation pulses from the monitored object N _o during the monitoring interval t _n , (in [1] t _n = 2 s); (the value of N _{o is} proportional to the average count rate of the additive mixture of the signal and background n obtained during the time t _n when the control object is in the field of view of the detector - N _o = n · t _n );

- calculate the threshold q = b · t _n + mσ,

where σ = (N _b ) ^1/2 is the standard deviation (SD) of the value of N _b ; m is a number equal to the number of s.c.o. (usually set m≥4); b = N _b / t _b is the average background count rate;

- the obtained N _{o is} compared with a threshold q; if N _o > q, then decide on the detection of the desired source (turn on the light or sound alarm signal); otherwise, they decide on the lack of an AI source. In case of non-detection of AI, no signals are generated and the search continues.

The main disadvantage of the described search and detection method [1] is the stiffness of the set detection thresholds of the AI source, which does not allow to obtain low detection thresholds while providing an acceptable (set) probability of false alarms and, moreover, does not allow working in an unsteady background.

The detection method used in the DRBP-03 type dosimeter-radiometer (VNIIFTRI, Moscow) [2] is based on the use of an even more stringent detection threshold than that used in [1]; in it even the number of standard deviations (rms) m is not regulated and is always equal to m = 4. This leads to unreasonable overestimation of detection thresholds and to an increase in the probability of skipping a weak source of AI, all other things being equal. A similar method for the search and detection of weak sources of AI in the radiometer-dosimeter MKS-06N "Inspector" (production of Green Star LLP, Moscow) [3].

The search and detection method used in portable devices MKS-A02 (manufactured by the Aspect Scientific and Production Center, Dubna) [4] is exactly the same as the method described above used in dosimeters-radiometers [1] and has the same disadvantages.

Known methods for detecting sources of AI [5], which use some basic provisions and conclusions from the theory of detection [6-8]. However, in [5] the mentioned provisions are not used consistently and accurately enough and therefore also lead to overestimation of detection thresholds.

In accordance with the theory of detection [6-8], under conditions of complete a priori uncertainty (that is, when there is no information about the detected object either about the presence or absence of an AI source on it, or about the magnitude of the signal from the AI source), the detector can and should only the Neumann-Pearson criterion is used, which is the most powerful criterion. The latter means that the application of the Neumann-Pearson criterion makes it possible to obtain the highest theoretically possible detection probability for a given probability of false alarm.

The second most important point of the theory of detection, which should be used in the engineering implementation of the theory, is the need to calculate the likelihood ratio. For practical purposes, it is more convenient to use the expression equivalent to the likelihood ratio for the sufficient statistics η [6], which in the discussed case of detecting weak AI flows takes the form

,

,

where all the designations coincide with those introduced above (in the section with the description of the analogue - dosimeter DRS-RM 1401 [1]).

In physical terms, η is the relative (or “normalized”) value of the average signal count rate from the detected source a = nb (otherwise: η is the relative value of the signal, presented in the form when the signal dispersion is equal to unity).

The obtained value of η is compared with a threshold q _o , the value of which depends on the probability of false alarm given by the operator P _lt and is determined from the tables of the normal distribution law.

In the search and detection method described in [5], the Neumann-Pearson criterion is applied (according to the authors), however, the detection threshold is calculated not using sufficient statistics (1), but in the form

where σ = (N _b ) ^1/2 is the standard deviation of the value of N _b equal to the number of pulses recorded during the measurement of the background level t _b .

It is obvious that the described method is almost no different from [1], except that the values of t _b and t _n can be set (adjusted) depending on the required accuracy and restrictions on the measurement time. Moreover, the determination of the threshold by (2) leads to a significant overestimation of the minimum detectable signal in comparison with the theory, i.e. compared with (1), since the number of s.c.o. m in (2) is established on the basis of work experience or intuition (usually m> 4), and not on the basis of strictly specified detection parameters and detection theory.

In the well-known “Method for monitoring moving objects for the detection of fissile nuclear materials” [9], ratios are mainly used in accordance with the theory of detection [6-8], and corrections for Poisson statistics are applied to form a detection threshold calculated from the given probability of false alarms , which is especially important at low count rates. However, here the main provisions of the theory of detection are not applied strictly.

There is also a method of detecting weak flows of ionizing radiation [10], which is completely and strictly based on the theory of detection [6-8] and reduces to the following sequence of operations:

- before the start of the monitoring of objects, the threshold q ₀ is determined in accordance with the probability of false alarms set by the operator according to the tables for normal distribution;

- measure the average count rate b, due to background radiation, and the background measurement time t _b can be any large and is determined by the operational environment or entered by the operator;

- the device is switched to the operational control mode, i.e. measuring the number of radiation pulses from the monitored object N _o during the monitoring interval t _n specified by the operator; calculate the average count rate of the additive mixture of signal and background n = N _o / t _n ;

- determine the value of the parameter η by (1);

- the obtained value of the parameter η is compared with the threshold q _o . If η> q _o , then decide on the detection (light or sound alarm signal is turned on); otherwise, they make a decision on non-detection (absence of source), which is recorded in the report on the control procedure.

The methods described above [5, 9, 10] are not suitable for search problems; they can be used precisely and only for the tasks of detecting AI sources at controlled objects. Obviously, when monitoring objects for detecting AI sources, the first key point is that the background is measured in advance, i.e. when the control object is absent in the detector sensitivity zone. The second key point is the following: the operator always knows for sure whether the controlled object is present in the control zone (for this purpose the so-called “presence sensors” are used, or the operator himself sees the control object directly and presses the “start” and “stop” buttons at the right moment in time "). Moreover, the time of the control measurement of radiation from the object corresponds to a value equal to the time the object was in the sensitivity zone of the detectors. And to make a decision about the presence or absence of AI at the object in methods of the type [5, 9, 10], the result of measurements of radiation from the object is directly or indirectly compared with a pre-measured background level.

When conducting a search, a substantially different formulation of the problem develops: in the general case, when searching, it is always unknown whether a weak AI source is present or not in the detector sensitivity zone. When conducting a search, it is necessary to continuously measure radiation during a long time, reaching 3-10 hours, when moving equipment with detectors, and somehow extract information from the results of such continuous measurements to decide on the presence or absence of an AI source in the search area . In this formulation of the problem, the methods [5, 9, 10] are in principle not suitable.

In addition, it is important to add that all the methods described above [1-5, 9, 10] are not suitable for solving the search problem in conditions where the background is substantially unsteady. As the results of practical studies of the background over long time intervals show [11–13], the background level, measured at averaging intervals of the order of 100–300 s, can vary with speeds of the order of 10 · (s.c.o.) in 20–30 min. and in 2-3 hours (during the search) "float" in absolute value by 50% or more. If for problems of radiation monitoring of objects the problem of adapting to fluctuations in the average background level can be solved quite simply (in the limit, in the most adverse conditions - by measuring the background level before monitoring each new object), then in the search problems the problem of adapting to changes in the background is not easy to solve. Here, for example, such a technique is completely unacceptable: to leave the search zone every 15-20 minutes to clarify the background level. Therefore, they often resort to the only measure possible under the specified conditions — to the method of overstating (roughening) the detection thresholds.

Known methods for searching and detecting radioactive objects used in devices of the type [14] and related to the field of radiation monitoring. These methods can be used to search and detect radioactive objects in the area, as well as the spatial distribution of radioactive sources during nuclear-physical well logging. However, these methods and their corresponding devices [14] are close to the claimed only by name; in fact, they necessarily involve the use of many stationary posts with complex radiation monitoring devices equipped with collimators. An overview description of this group of methods and devices intended for continuous radiation monitoring of the area around the nuclear power plant and for operational monitoring in the event of an accident is given in [13]. Obviously, this group of devices [13, 14] is not related to the discussed problems of searching for AI sources.

Also known is a group of devices designed to detect sources of AI and fissile materials on moving objects. For example, “Yantar-1A” systems [15] for monitoring passing cars (and other similar systems described on the site of the Scientific and Production Center “Aspect” Scientific and Production Center), which use the detection method as described in [9] above. However, all devices of the type [15], as well as devices [13, 14] are stationary; search, as noted above, in principle, requires mobile (or wearable) equipment with AI detectors.

Reference should also be made to the review [16], which describes in detail several different ways of processing data from radiation monitors. In the review [16], virtually all methods of detecting AI sources described above are described (including the method of the authors of this application [10]). However, in [16], only cases of using stationary radiation monitoring devices were considered (the latter are designated by the term “radiation monitors” among specialists), and the tasks of searching for AI by means of mobile (mobile) radiation monitoring devices are not addressed at all.

Closest to the claimed is a method for searching and detecting AI, described in [17].

The method proposed in [17] consists in performing the following sequence of operations.

1. Immediately upon arrival in the search zone of the detecting system, they begin to measure the average count rate of the additive mixture of signal and background n (t) in the exposure time sections (time intervals) t _exp , the duration of each of which is the same and is preferably (8-15) t _e , where t _e is the effective signal duration.

2. At the end of the exposure interval No. 1 of duration t _exp from the values of the vector n ₁ (t) measured in the interval No. 1, the average count rate of the background b, the signal amplitude a _m and the position of the signal maximum on the time scale t _{m are} determined.

3. The value of the parameter η is calculated by the formula

4. The obtained value of the parameter η is compared with the detection threshold q _o determined by the given probability of false alarms. If η> q _o , then decide on the detection (a positive result of the search for the source of AI in the interval No. 1); otherwise, they decide that there is no signal.

5. In parallel with the processing procedure for the implementation of n ₁ (t), further measurements of the values of n (t) are continued.

6. At the end of interval No. 2, the vector n ₂ (t) is subjected to processing exactly the same as described in claims 2-4.

7. Next, sequentially repeat the steps according to claims 1-5 for intervals No. 3, 4, 5, etc. until the search is completed in a given territory.

The effective signal duration is determined from the relation t _e = k _t · r _o / υ _o , where r _o is the shortest distance between the detector and the expected location of the sought-after AI source; υ _o is the average speed of the detecting system; k _k is a coefficient whose value is determined by the design of the detector (for a flat detector, k _k = 2.0; for an omnidirectional detector, k _k = 2.8).

The method [17] has a number of significant advantages compared to those described above [1-5, 9-16], the main of which are:

- the ability to search for AI sources using a movable detecting system under conditions of a substantially unsteady background;

- the ability to achieve the highest probability of detection for a given probability of false alarm, i.e. the ability to achieve low detection thresholds close to theoretically limiting [6-8] for given detection parameters.

However, one cannot fail to note one important drawback characteristic of the described method [17], which consists in the following.

Since the value of the effective signal duration t _e , which depends on the expected distance r _o , is assigned a certain hard value in advance, the probability of missing a useful signal is very high. Indeed, the distance r _o from the detection system to the desired source of AI in most real cases of the search is unknown a priori. Therefore, setting in advance some one hard value of the estimated distance r _o (and the corresponding value of t _e ), we run the risk of skipping (not detecting) the source with a high probability.

This disadvantage of the method [17] can be explained differently using the concept of optimal filtering [18]. The analysis shows that the method [17] (and more specifically, the method of processing the vector n (t) according to items 1-2 described above) can be considered equivalent to the method of using the optimal digital filter, at the output of which the maximum possible is obtained (in comparison with any other devices ) signal to noise ratio. By setting in advance, before starting the search, a certain rigid value * t _e corresponding to the distance * r _o , we thereby “adjust the optimal filter” to only one distance value * r _o . And if in the implementation n (t) there is a signal corresponding to another (for example, three times as large) distance, then such a signal will most likely be skipped.

The present invention solves the problem of reducing the probability of missing a useful signal (and, accordingly, the source of AI) and improving the accuracy of assessing the location of the detected source.

To solve the aforementioned problems in the claimed method of searching, detecting and estimating the location of AI sources, if formulated as briefly as possible, it is proposed to take the prototype method [17] as a basis, supplementing it with regard to processing the vector n (t) by sequential enumeration of t _e values. Those. successively increase t _e (1.3 ± 0.1) times from the minimum t _e1 = 4 · t _∂ , then t _e2 = 6 · t _∂ , t _e3 = 8 · t _∂ , ... t _ej , etc. . to the maximum value t _{e (Max)} <100 s (where t _∂ is the sampling time interval of the vector n (t)). For each t _{ej, the} parameter η _{j is} calculated according to (3), and for that of the values t _ej = t _em for which the parameter η _j takes the maximum value η _m , the estimate value of the sought distance between the detector and the source of the AI is calculated by the formula

,

,

where k _k is 2.0-2.8 depending on the design of the detector.

Note. Here and below, we use the notation adopted and decoded in the description of the prototype method [17].

If the proposed method for searching, detecting and estimating the location of AI sources is described in more detail, then it consists in the following sequence of operations.

1. Immediately upon arrival in the search zone of the detecting system, during the continuous movement of the latter, they begin to measure the average count rate of the additive mixture of signal and background n (t). Moreover, the vector n (t) is divided into sections of the exposure time (time intervals) t _exp , the duration of each of which is the same and is preferably (8-15) · t _e , where t _e represents the effective duration of the signal.

At the first stage of processing, the value of t _e is set equal to a certain minimum value of t _{e (min)} = t _e1 = 4 · t _∂ , where t _∂ is the sampling time interval of the vector n (t). The indicated value of t _{e1 is} limited by the conditions of dicretization of the useful signal on the time scale a (t) (analysis shows that the minimum corresponds to 4 sampling points of the signal a (t)). The sampling time interval t _{∂ itself} , as shown by practical experience in the development of radiation monitoring equipment, can be equal to values from 0.1 to 1.0 s.

Note: To simplify the description here and below, it is assumed that there is only one AI source on the search path.

Further, the sequence of processing the vector n (t) does not differ from that described above in paragraphs 2-7 of the prototype method [17].

2. At the end of the exposure interval No. 1 with duration t _exp1 from the values of the vector n ₁ (t) measured in the interval No. 1, the average count rate of the background b, the signal amplitude a _m and the position of the signal maximum on the time scale t _{m are} determined.

3. The value of the parameter η is calculated by the formula (3).

4. The obtained value of the parameter η is compared with the detection threshold q _o determined by the given probability of false alarms. If η> q _o , then decide on the detection (a positive result of the search for the source of AI in the interval No. 1); otherwise, they decide that there is no signal.

5. In parallel with the processing procedure for the implementation of n ₁ (t), further measurements of the values of n (t) are continued.

6. At the end of interval No. 2, the vector n ₂ (t) is subjected to processing exactly the same as described in claims 2-4.

7. Next, sequentially repeat the steps according to claims 1-5 for intervals No. 3, 4, 5, etc. up to the end of the vector n (t).

8. On the 2nd, 3rd, etc. in the processing steps, the values of t _{e are} successively increased by (1.3 ± 0.1) times, and for each new value t _e2 = 6 · t _∂ , t _e3 = 8 · t _∂ , ... t _ej , etc. repeat the processing of the vector n (t) in accordance with paragraphs.2-7. This procedure is repeated until t _ej reaches a maximum value of t _{e (Max)} ≤100 s.

9. As a result of performing all the processing steps, if there are facts of signal detection, a series of values of the parameter η _j corresponding to the values of the effective duration t _ej are obtained.

10. For that of the values t _ej = t _em for which the parameter η _j takes the maximum value η _m , the estimated value of the sought distance between the detector and the source of the AI is calculated by formula (4), and the coefficient k _{k is} chosen to be 2.0 for detector flat design and k _k = 2.8 for an omnidirectional detector.

When processing the vector n (t) in the proposed method, as in the prototype [17], it is advisable to provide such a mode in which each subsequent (i + 1) -th interval of duration t _{exp is} superimposed on the previous i-th interval by 2t _e . Such overlap is necessary for cases when the signal is located on the edge of the interval t _exp .

From the description of the proposed method, it can be seen that since the "enumeration" of all really possible values of t _e , and accordingly, the distances r _o , is carried out, the probability of missing a useful signal is significantly reduced compared to the prototype [17]. (Of course, only the problems of detecting weak signals for which the signal-to-background ratio ≤0.1 are discussed here).

On the issue of improving the accuracy of determining the distance to the detected AI source in comparison with the prototype [17], the following should be noted.

In the prototype [17], when moving along the path of the detecting system, it was possible to determine only the position of the line perpendicular to the portion of the path on which the detected source of AI can be located. The distance from the path to the AI source according to [17] was determined with a very large error exceeding ± 80% (and sometimes, with large signals, reaching 200-300%).

When using the proposed method, as analysis and model verification experiments show, the indicated error in determining the distance between the detector and the AI source (error in the R * value) will not exceed 40%.

Thus, the obtained value of the distance R * and the coordinates of the detection system location point corresponding to the moment of detection allow us to unambiguously indicate the location of the AI source, since the AI source is on a line perpendicular to the path along which the detection system moves. It is also obvious that the coordinates of the “detection point” on the track are determined using a modern satellite navigation system (GPS or GLONASS), the operation of which is synchronized with the operation of the detecting system using application software.

The proposed method has additional advantages in comparison with analogues and prototype.

1. In the described search and detection method, the assumption is used that only one point source of AI can be located on the length of the segment of the controlled search zone. This assumption is accepted to simplify the description of the method. However, in reality, there can be several point sources of AI on the control path. Given that in most practical cases the control path is quite long, and the sources are very spaced, the proposed method can be applied to the mentioned case of searching for several AI sources without significant additional complications. For each of the detected sources, the proposed method allows you to determine your distance R * from the path along which the detection system moves.

2. The proposed method is also suitable for working as a checkpoint, especially when a large number of vehicles and very long wagons are examined.

INFORMATION SOURCES

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3. The radiometer-dosimeter MKS-06N INSPECTOR. Passport. - M .: Green Star LLP, 1996.

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Claims (1)

A method for searching and detecting ionizing radiation sources using ionization counters or scintillation detectors moving around the territory (zone) of the search, which consists in continuously measuring the average count rate of the additive mixture of signal and background n (t) during the motion of the detection system in the surveyed area, processing the obtained measurement information by dividing the vector n (t) plots for exposure time t _exp, the duration of each of which is identical and is preferably (8-15) · t _e, op edelenii values of the average background count rate b, the intended signal amplitude and signal a _m t _m position on the timeline, then the calculation of the parameter η of formula

where t _e is the effective duration of the signal, comparing η with the threshold q ₀ , determined by the given probability of false alarms, and deciding whether to find the desired object if η> q ₀ , characterized in that the processing of the obtained measurement information is carried out by sequentially increasing effective signal duration t _e (1.3 ± 0.1) times of the minimum t _e1 = 4 · t _∂ , then t _e2 = 6 · t _∂ , t _e3 = 8 · t _∂ , ... etc. to the maximum value of t _e ≤100 s (where t _∂ is the sampling time interval of the vector n (t)), the calculation of the parameter η _j according to (1) for each value of t _ej and for that of the values of t _ej = t _em for which the parameter η _j takes the maximum value η _m , calculating the value of the estimate of the desired distance between the detector and the source of AI according to the formula
R * = k _k · υ ₀ · t _em ,
where υ ₀ is the average speed of the detecting system, and the coefficient k _k is 2.0-2.8 depending on the design of the detector.