JP2607854B2 - Signal tracking method - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an unknown narrow direction having a directivity based on a signal intensity distribution in a frequency space obtained by performing a frequency analysis on the output of a receiving beam directed in a plurality of directions. The present invention relates to a signal tracking method for measuring a band signal continuously over time.

[0002]

2. Description of the Related Art A conventional signal tracking method will be described below.
First, a signal is received by a plurality of receiving beams pointing in directions slightly different from each other, and spectrum analysis is performed on the output of the receiving beam at each sample time. As a result, the signal intensity X (f, θ) at each frequency f of the input signal of the received beam in each direction θ that directs the received beam
Is obtained. Next, a local maximum point is detected from the distribution of the signal intensity X (f, θ). Here, the maximum point means that the signal strength is
A point in the same receiving beam that is greater than the signal strength of the adjacent sample frequency and greater than the strength of the receiving beam pointing in the adjacent direction at the same sample frequency. If the point (f ₀ , θ ₀ ) is a local maximum point and its signal strength exceeds a predetermined threshold, it is determined that a narrow band signal of frequency f ₀ has arrived, and its azimuth is substantially. It is determined that θ ₀ . Subsequently, as the measurement value of the arrival direction of a more precise narrowband signal point of the signal intensity X (f, θ) (f 0, θ 0) Request precise measurement orientation theta ₁ from the data in the vicinity. This can be obtained, for example, by performing interpolation processing using several points near the azimuth θ ₀ to obtain the maximum point θ ₁ . In this way, it is detected that the narrow band signal of the frequency f ₀ has arrived from the precise measurement direction θ ₁ . After performing the above processing for each sample time, the frequency and precise measurement direction of the narrowband signal detected at different times are compared. The difference between the frequency f ₀ and the arrival direction θ _{1 of} the narrow band signal detected at a certain sample time and the frequency f ₀ ′ and the arrival direction θ ₁ ′ of the narrow band signal detected within a certain period of time in the past. If it is less than the defined value, both signals are regarded as narrowband signals from the same target and the old and new narrowband signals are integrated. Thereafter, by repeating in the same manner, the ever-changing frequency and arrival direction of the narrowband signal can be continuously obtained in time.

[0003]

However, the conventional signal tracking method has the following problems. The situation where the signal strength is small compared to the noise strength, ie, S /
In a situation where the N-ratio is low, a local maximum based on random noise frequently appears in the distribution of the signal strength together with a true maximum based on the narrowband signal. Therefore, in the conventional signal tracking method, a phenomenon that the maximum point based on the noise is often integrated into the narrow band signal being tracked often occurs, so that an incorrect frequency or arrival direction is indicated or tracking is interrupted. However, the signal intensity cannot be used unless the signal intensity is sufficiently large compared to the noise intensity. FIG.
FIG. 7A is a diagram illustrating a signal intensity distribution in an azimuth space with respect to a frequency f indicating the above-described problem, and FIG.
Signal intensity distribution diagram in _1, FIG. (B) the signal intensity distribution diagram at sample time T _2, FIG. (C) is the signal intensity distribution diagram at sample time T _3, FIG. (D) shows the sample time T ₄ It is a signal intensity distribution diagram. In sample time T _1, arrives narrowband signal from the azimuth theta ₁ is at sample time T2, the narrowband signal has arrived from the direction theta _2. At the sample time T ₃ , the signal intensity is maximum at the azimuth θ _e and the azimuth θ ₃ ,
Moreover the signal strength in the orientation theta _e is, since it is larger than the signal intensity at an azimuth theta _3, signal direction theta _e is estimated to orientation of the narrowband signal. However, the azimuth θ _e is the point at which the signal intensity reaches a local maximum due to random noise, and the signal azimuth θ ₃ is the true maximum at a narrow band signal. Therefore, the signal direction θ _e is erroneously estimated as the direction of the narrowband signal. In sample time T _4, the signal intensity in the orientation theta ₄ is a maximum point, the narrowband signal from the azimuth theta ₄ has arrived. FIG. 3 is a diagram showing a desirable tracking result of the narrowband signal shown in FIG. As shown in this figure,
At sample times T _{1 to} T ₄ , the signal orientations θ _{1 to} θ ₄
Is the correct orientation of the narrowband signal. On the other hand, FIG. 4 is a diagram showing a tracking result by a conventional signal tracking method. As shown in this figure, the direction θ _e of the signal due to random noise at the sample time T ₃ is integrated with the direction of the narrowband signal detected at the sample times T ₁ and T ₂ . In sample time T _4, the narrowband signal of the azimuth theta ₄ is, but is correctly detected, since the orientation theta _e in sample time T ₃ and the direction theta ₄ are spaced apart, the narrow band signal at sample time T ₄ sample the narrowband signal at time T ₁ through T ₃ there is a problem that the tracking is interrupted in the discriminated narrowband signal as not by the signal source.

[0004]

According to a first aspect of the present invention, in order to solve the above-mentioned problem, a plurality of receiving beams having directivities in different directions are used to monitor a direction in a predetermined continuous range, For each received beam, the sample time and the frequency analysis of the signal received for each sample frequency are performed to calculate the signal strength distribution in the frequency space, and the directionality is calculated based on the signal strength distribution in the frequency space. In the signal tracking method for continuously measuring the frequency and direction of arrival of a narrowband signal arriving at the following time, the following processing is performed. That is, a pattern determination process is performed on each of the received beams to determine whether a linear or curved pattern exists near the sample frequency in the signal intensity distribution in the frequency space using a neural network. Then, from the distribution of the neural network output in the azimuth × frequency space, a local maximum point detection process for detecting a local maximum point is performed, and the local maximum points detected by the local maximum point detection process continuously appear temporally, and The narrow band signal tracking processing for tracking the frequency and the direction of the narrow band signal is performed by detecting the case where the azimuth and the frequency of the local maximum point are close to each other. According to the second invention, the narrow-band signal tracking processing of the first invention is obtained by a tracking filter from the latest sample frequency of the maximum point obtained by the maximum point detection processing and the sample frequency of the past maximum point. The frequency and azimuth of the narrowband signal are tracked by comparing with the predicted value. In a third aspect of the present invention, the narrow-band signal tracking process according to the first aspect comprises a step of: estimating a direction of a latest local maximum point obtained by the local maximum point detection process and a predicted value obtained by a tracking filter from directions of past local maximum points. The frequency and direction of the narrowband signal are tracked by comparison. According to a fourth aspect of the present invention, in the narrowband signal tracking process according to the first aspect, precise measurement information of the frequency of the narrowband signal is obtained by performing the interpolation process in the frequency space. According to a fifth aspect of the present invention, in the narrowband signal tracking process according to the first aspect of the present invention, precise measurement information of the arrival direction of the narrowband signal is obtained by performing an interpolation process between the received beams.

[0005]

According to the first aspect of the present invention, since the signal tracking method is configured as described above, the neural network generates a straight line near each sample frequency in the distribution of signal strength in the frequency space with respect to each received beam. Alternatively, a pattern determination process is performed to determine whether a curve pattern exists. In the case of a narrow band signal, the straight or curved pattern exists in the signal intensity distribution near the frequency of the narrow band signal, and in the case of noise, the pattern does not exist in the signal intensity distribution. Then, when the maximum point of the output of the neural network in the azimuth and frequency space is detected, the maximum point does not include the one due to noise. In the narrow-band signal tracking processing, the detected local maximum points appear continuously in time, and the direction and frequency of those local maximum points are detected, thereby detecting the frequency of the narrow-band signal. , And azimuth. Therefore, the above problem can be solved.

[0006]

FIG. 1 is a functional block diagram of a signal tracking device for implementing a signal tracking method according to the present invention. The signal tracking device, orientation theta _1, a plurality of reception beams B ₁ directed to different orientations slightly narrowband signals S ₁ from an unknown target 1 is moving in the frequency f _1, B _2, ... , _Bn . On the output side of the acoustic array sensor 2, each received beam B _i (i =
1, 2,..., N) are subjected to spectrum analysis, and the signal intensity distribution S3-i in the frequency space is determined at each sample time t.
A frequency analyzer 3-i for each _k is connected.
On the output side of each frequency analyzer 3-i, a peak detector 7 for calculating a precise measurement direction and a precise frequency, and a storage device 4 for storing a signal intensity distribution in a frequency space for a predetermined time in the past.
-I is connected. On the output side of the storage device 4-i,
A portion near the sample frequency is extracted from the signal intensity distribution S4-i on the frequency space from the sample time t _k to t _k + K, and a straight line or a curve formed by a narrow band signal from the unknown target 1 is extracted in the extracted portion. A neural network 5 for determining whether or not a pattern exists is connected. At the output side of the neural network 5, the direction x
A target management device 6 for detecting the maximum point of the output of the neural network in the frequency space, detecting whether the maximum point appears continuously in time, or the like, thereby tracking the frequency and direction of the narrowband signal is connected. Further, a peak detector 7 is connected to the output side. The peak detector 7 outputs the precise measurement frequency f ₁ of the unknown target 1 and the precise measurement direction θ ₁ .

Next, a signal tracking method according to the present invention will be described with reference to FIG. The acoustic array sensor 2 receives a narrow band signal from the unknown target 1 by a plurality of receiving beams B ₁ , B ₂ ,..., B _n pointing in slightly different directions.
Received signal S of each received beam B _i (i = 1, 2,..., N)
2-i is output to the frequency analyzer 3-i. Each frequency analyzer 3-i, receiving beam B _i for each sampling time t _k
Of the received signal S2-i is obtained to obtain a signal intensity distribution in the frequency space, and the signal intensity distribution S3-i is output to the storage device 4-i and the peak detector 7. In the storage device 4-i, stores the signal intensity distribution S3-i of receiving beams B _i for the past predetermined time period. In the neural network 5, for the received beam B _i directed in each direction θ _i , the signal in the time × frequency space stored in the storage device 4-i for each sample frequency of a plurality of sample frequencies having slightly different frequencies. A portion limited to the vicinity of the sample frequency is extracted from the intensity distribution S4-i, and it is determined whether or not a straight or curved pattern exists in the extracted portion. This is, for example, a back-propagation-type neural network in which a linear or curved pattern is learned in advance, and 1 is output if the learned pattern is present in the extracted input data, and 0 is output if it is not present. This can be realized by using a network. In the case of a narrow-band signal, the signal strength distribution of a received beam directed in the direction of the narrow-band signal has the property of forming a pattern in which large points of signal strength are arranged linearly in the vicinity of the frequency of the narrow-band signal. . On the other hand, in the case of a noise signal, a random signal intensity distribution is obtained, and the pattern is not formed. Accordingly, since detecting the arrival of the narrow-band signal according to whether a pattern of lines or curves present in the vicinity of the sample frequency in the signal intensity distribution on the frequency space of the receiving beams B _i, influenced by noise A narrow band signal can be detected without using a signal.

[0008] The target management apparatus 6, from the distribution of the neural network output on orientation × frequency space, detects the maximum point (f, theta), polar University point (f, theta) detected in the current sample time t _k Frequency f _{t (k) of} the narrowband signal _obtained ,
And the orientation θt _(k) . The target management device 6, the frequency f _t of the current sample time t _k at the detected narrowband signal _(k), and the azimuth θ _{t (k)} and the frequency of the narrowband signal detected in the past f _{t (k- 1)} and the azimuth θt _(k-1), and if it is determined that both narrowband signals are based on the same target, the new and old narrowband signals are integrated and recorded. As a determination method at this time, for example, it is possible to use a method of integrating when the azimuths of both narrowband signals are close to within a predetermined difference and the frequencies of both narrowband signals are close to within a predetermined difference. it can. Or, an azimuth predicted using a tracking filter from the azimuth of arrival of the narrowband signal detected in the past,
It is also possible to use a method of comparing the arrival direction of the narrowband signal detected this time and integrating them when they are close to each other within a predetermined difference. Then, the target management device 6 outputs the integrated target azimuth θ and frequency f to the peak detector 7 at each sampling time of the narrowband signal.

In the peak detector 7, each frequency analyzer 3-
based on the distribution S3-i of the signal strength of the obtained frequency space from receiving beam B _i directed to the azimuth θ outputted from the i, determining the precise measurement frequency f ₁ of the narrowband signal. As this method, the precise measurement frequency f ₁ of the narrowband signal with a large frequency of the highest signal strength in the vicinity of the frequency f. Alternatively, a method of performing an interpolation process between frequencies near the frequency f can be used. The interpolation process in the latter case can be realized by, for example, parabolic interpolation using three points.
In this method, a parabolic curve that is convex in the direction of the signal strength axis passing through the maximum point and the three points of the signal strength at the sample frequencies adjacent to the maximum point is obtained from the signal strength distribution in the frequency space, and the frequency at which the vertex is located is determined by the precision of the narrowband signal. it is an measuring frequency f _1.
Subsequently, the peak detector 7, based on the signal strength in the frequency f ₁ of the signal intensity distribution S3-i for each receive beam B _i, obtains the Seihaka orientation theta _1. As this method, the signal intensity in the precise measurement frequency f ₁ in the vicinity of the orientation theta is the precise measurement orientation theta ₁ with the orientation directed to the greatest receiving beam. Alternatively, it is possible to use a method of performing an interpolation process between reception beams directed to an azimuth near the azimuth θ.
In the interpolation processing in this case, the parabolic interpolation can be used. The precisely measured frequency f ₁ and the precisely measured azimuth θ ₁ thus obtained are recorded and output as attributes of the narrowband signal being tracked. By repeating the above processing, it is possible to track a narrow band signal whose frequency and arrival direction change every moment.

As described above, this embodiment has the following advantages. (I) In the case of a narrow-band signal, it has the property of forming a pattern in which points with high signal strength are arranged in a line near the frequency of the narrow-band signal. Therefore, we used a neural network to discover the existence of this pattern and track it to track narrowband signals.
Signal tracking can be performed without being affected by noise, and the accuracy of signal tracking can be improved. (Ii) Narrowband signal tracking can be performed in a situation where the S / N ratio is lower than that of the conventional method. The present invention is not limited to the above-described embodiment, and various modifications are possible. For example, there are the following modifications. (1) In the above embodiment, the input signal is an acoustic signal. However, any input signal may be used as long as it has directivity such as electromagnetic waves and can obtain a frequency and a signal strength. (2) The signal tracking device of FIG. 1 may be realized by an individual circuit or a program. (3) In the above embodiment, a back propagation type is taken as an example of a neural network. However, another neural network such as a Boltzmann machine may be used as long as it can determine the presence of a pattern.

[0011]

As described in detail above, the first to fifth embodiments
According to the invention, for each received beam, a pattern determination process for determining whether there is a linear or curved pattern near the sample frequency in the signal intensity distribution in the frequency space using a neural network. Since the maximum point is detected from the distribution of the output of the neural network in the azimuth × frequency space, tracking of the narrow band signal can be performed without being affected by noise. In addition, it is possible to track a narrow band signal under a condition where the S / N ratio is low.

[Brief description of the drawings]

FIG. 1 is a functional block diagram of a signal tracking device for implementing a signal tracking method according to an embodiment of the present invention.

FIG. 2 is a diagram illustrating a signal intensity distribution in an azimuth space at each sample time T _{1 to} T ₄ with respect to a frequency f.

FIG. 3 is a diagram illustrating a desirable tracking result of the narrowband signal of FIG. 2;

FIG. 4 is a diagram showing a tracking result of the conventional method of the narrow band signal of FIG. 2;

[Explanation of symbols]

Reference Signs List 1 unknown target 2 acoustic array sensor 3-i (i = 1, 2,..., N) frequency analyzer 4-i (i = 1, 2,..., N) storage device 5 neural network 6 target management device 7 peak Detector

 ────────────────────────────────────────────────── ─── Continuing on the front page (72) Inventor Masato Yamashita 1-7-12 Toranomon, Minato-ku, Tokyo Oki Electric Industry Co., Ltd. (72) Inventor Takashi Mizota 1-7-12 Toranomon, Minato-ku, Tokyo Oki Electric Industry Co., Ltd. (72) Inventor Shunji Ozaki 1-7-12 Toranomon, Minato-ku, Tokyo Oki Electric Industry Co., Ltd. (56) References JP-A-6-43238 (JP, A) JP Hei 8-15402 (JP, A)

Claims (5)

(57) [Claims] An azimuth in a predetermined continuous range is monitored using a plurality of receiving beams having directivities in different azimuths. A receiving time is determined for each of the receiving beams at a sample time and a sample frequency. The frequency analysis of the waved signal is performed to calculate the signal strength distribution in the frequency space.Based on the signal strength distribution in the frequency space, the frequency and direction of arrival of the narrowband signal arriving with directionality are temporally continued. A signal tracking method for measuring by using a neural network, for each of the received beams, determining whether a linear or curved pattern exists near the sample frequency in the signal intensity distribution in the frequency space. A determination process; a maximum point detection process for detecting a maximum point from a distribution in an azimuth and a frequency space of the output of the neural network; By detecting a case where the maximum points detected by the point detection process appear continuously in time and the directions and frequencies of those maximum points are close to each other, the frequency and the direction of the narrowband signal are detected. And a narrowband signal tracking process for tracking the signal. 2. The narrow-band signal tracking processing includes comparing a latest sample frequency of a maximum point obtained by the maximum point detection processing with a predicted value obtained by a tracking filter from a sample frequency of a past maximum point. 2. The signal tracking method according to claim 1, wherein the tracking of the frequency and the direction of the narrow band signal is performed by using the tracking method. 3. The narrow-band signal tracking processing is performed by comparing the azimuth of the latest maximum point obtained by the maximum point detection processing with a predicted value obtained by a tracking filter from the azimuth of a past maximum point. 2. The signal tracking method according to claim 1, wherein the frequency and the direction of the band signal are tracked. 4. The narrow-band signal tracking process obtains precise measurement information of a frequency of a narrow-band signal by performing an interpolation process on a signal distribution in the frequency space.
The described signal tracking method. 5. The signal tracking according to claim 1, wherein the narrow-band signal tracking process obtains precise measurement information of an arrival direction of the narrow-band signal by performing an interpolation process between the reception beams. Method.