JP2007520145A - Method and apparatus using a randomized Hough transform for detecting radio systems with periodic radiation patterns - Google Patents

Abstract

The present invention includes a method and apparatus for identifying transmission opportunities in a radio network. The method and apparatus monitors the first, second and third time periods to detect the first, second and third busy slots. The processor recognizes the sequence of the first, second and third busy slots as a function of time, performs a randomized Hough Transform on the sequence, and generates a histogram based on the randomized Hough transform Then, the peak of this histogram is identified, it is determined whether this peak corresponds to a known radar, and a transmission opportunity is identified.

Description

  This application claims the benefit of US Provisional Application Serial No. 60 / 541,207, filed February 2, 2004, the contents of which are incorporated herein by reference.

  A radio network that radiates energy in the vicinity (and thus uses shared radio resources) and other devices such as radar (eg, primary source) or other radio networks (eg, secondary sources) When facing, it is desirable to characterize the radio resource usage patterns of these other devices. Such usage pattern characterization leads to identification of opportunities for the radio network to transmit and / or receive signals. In general, the wireless network is allowed to operate only when radar is not detected. If a radio network detects a working radar signal, the radio network must free up its frequency bandwidth.

  The Hough Transform has been used to detect such other devices. As an example, the Hough transform is used to detect radar pulses that are subjected to any type of radio signal that forms a periodic pattern. The repetition of the width indicates what type of radar is being used. The Hough transform is studied in the literature of image processing for detecting patterns such as lines, circles, and ellipses in a binary image. The effectiveness of the Hough transform has been proved in pattern detection in data using a large number of overlapping patterns and random noise. In the presence of outliers, the Hough transform is more robust against noise than the commonly used least squares estimation.

  1A and 1B depict Hough transforms for detecting straight lines in image space (FIG. 1A) and parameter space (FIG. 1B). The image space is represented by (x, y), while the parameter space is represented by (gradient, intersection) or (m, c). For each point (eg, p and q) in the image space of FIG. 1A, a line is generated in the parameter space of FIG. 1B as shown. The parameter space can be recognized as a two-dimensional histogram. The peak (r) in the parameter space corresponds to a line in the image space. The Hough transform is powerful because in image space, a set of collinear points is sufficient to guide a peak in parameter space.

  However, the parameter space has the disadvantage that it requires a large amount of computer memory in the detection device for detecting straight lines (ie when a collection of one-dimensional measurements by two points results in a histogram of the gradient in the parameter space). .

  The strategy provided by the present invention relaxes the memory requirements of known methods and devices. Such a strategy uses a specific Hough transform known as the Randomized Hough Transform (RHT) to detect the parameters of the spiral that wraps around the cylinder. RHT, as applied to straight line detection, randomly selects a point pair and calculates and accumulates parameters (eg, slope). When sufficient reliability of the peak is reached, the process stops, so both memory and processing time are reduced.

  In one aspect, a method for identifying an opportunity in a radio network includes several steps. One step is to monitor (listen) the first time period. Another step is to detect the first busy slot. Some additional steps include monitoring a second time period, detecting a second busy slot, monitoring a third time period, and detecting a third busy slot. Another step is recognizing the sequence of first, second and third busy slots as a function of time. Other steps include performing a randomized Hough transform on the sequence, generating a histogram based on the randomized Hough transform, identifying a peak in the histogram, and corresponding the peak to a known radar Determining whether and when to transmit.

  In one embodiment, the method includes monitoring at least a fourth time period and detecting at least a fourth busy slot.

  In another embodiment, the determining step determines whether the peak corresponds to a known radar in a limited bandwidth.

  In another aspect, an apparatus for identifying timing in a radio network includes a source, means for performing calculation and performing randomized Hough transform, means for generating a histogram based on the randomized Hough transform, A processor including means for identifying a peak and means for identifying a transmission timing; a memory; and at least one monitoring device.

  In one embodiment, the apparatus includes medium access control, a physical layer, and at least one transmitter.

  In another embodiment, the monitoring device is an IEEE 802.11 slot mechanism.

  The present invention provides numerous advantages that will be apparent from the following description, drawings and claims.

  FIG. 2 is a block diagram representing various steps of a method for identifying a transmission or / or reception opportunity or opportunity in a radio network. In step 201, the radio network device enters a monitoring or receiving (listening) state for a period of time in order to detect the first busy slot. Any type of monitoring device known in the art can accomplish this. In step 202, the radio network device inquires about whether a first busy slot is detected. If not, the radio network device monitors (listens) for an additional time period for the first busy slot and returns to step 201. If the radio network device detects the first busy slot, the second time period is monitored in step 203. The radio network device queries in step 204 whether a second busy slot is detected. If not, an additional time period is monitored for the second busy slot. If the radio network device detects the second busy slot, the third time period is monitored in step 205. Only when the radio network device detects the third busy slot confirmed in step 206, the process of generating the modified Hough transform is started. Also, in steps 205.1 and 206.1, the radio network device can monitor and query for the fourth busy slot or several busy slots beyond steps 201-206.

  If the radio network device detects three busy slots in steps 201-206, in step 207, it must recognize the sequence of first, second and third busy slots as a function of time. For example, the number of idle slots separating the second busy slot from the first busy slot is the same as the number of idle slots between busy slots 3 and 2, or the difference between them exceeds a small number. There should be no (ie less than a few). If the detected sequence of busy slots cannot be recognized as a function of time, the iterations of steps 201-206 are started. If the radio network device recognizes the sequence of first, second and third busy slots as a function of time, the software, algorithm and / or processor may perform steps 208.1 to 208.2 as described below. Perform randomized Hough transform.

  The present invention applies a Hough transform to detect a straight line corresponding to a pulse train using a one-dimensional to three-dimensional transform (such as a raster scan). In addition, a noise floor calculation is introduced. According to the method, first in step 208.1, the converting step converts the one-dimensional signal into a three-dimensional spiral signal. Next, in step 208.1, the transformation step applies RHT to the three-dimensional helical signal.

The method can include generating a helix that can be represented by the following parameter equation:
X (t) = sin (ωt)
Y (t) = cos (ωt) (1)
Z (t) = t
This helix is cylindrical (as opposed to the more common ellipse) and has a unit radius.

２つのポイントＰ_０及びＰ_１が螺旋の１巻きの内部にあると、ωは、１となる。螺旋の１巻きにより与えられる線分の長さは、

である。 Based on the parameter ω, a new helix can be generated that wraps around the cylinder more slowly as ω decreases. In FIG. 3, the points on the blue spiral form a spiral themselves, and the value of ω is smaller than 1. For two points P ₀ (x ₀ , y ₀ , z ₀ ) and P ₁ (x ₁ , y ₁ , z ₁ ) on the spiral, the parameter ω can be expressed by the following equation:

If the two points P ₀ and P ₁ are inside one turn of the spiral, ω is 1. The length of the line segment given by one turn of the spiral is

It is.

In step 209 of FIG. 2, the algorithm or other known histogram generating mechanism generates a histogram based on the randomized Hough transform performed in steps 609.1 and 609.2. Examples of a histogram generated in step 610, represents the position of the radar pulse train of a single pulse that repeats at a rate of 50 pulses at a time period by a vector L _p (arrival time). The ω histogram of FIG. 4 is obtained for L _p1 = [9, 59, 109, 159, 209, 259, 309, 359].

  In step 210, the radio network identifies the histogram peaks generated in step 209. The peak 30 in FIG. 4 represents the pulse repetition frequency in the signal sequence.

In the example depicted in FIG. 4, ω = 0.116. On the other hand, FIG. 5 is multiplied by pulses repeating at a rate of 40 and 50 pulses over a period of time, and L _p2 = [9, 20, 59, 60, 100, 109, 140, 159, 180, 209, 220. , 259, 260, 300, 309, 340, 359], there are two pulse trains. Peaks 40 and 41 represent pulse repetition frequencies in the signal sequence corresponding to rates of 40 and 50 in the sequence. Note that ω = 1 corresponds to a point on the spiral in one turn. This can be ignored as an artifact of the model.

  If a peak is identified in step 210, the radio network determines in step 211 whether the peak corresponds to a known radar. This can be done, for example, by comparing the identified peak with peak data stored in memory. If the peak does not correspond to a known radar, the radio network repeats steps 207-211. If the peak corresponds to a known radar, the radio network knows the period and usage associated with the radar and can identify the transmission and / or reception timing in step 212. The radio network performs transmission and / or reception in step 213 based on the time identification in step 212.

The method can also be associated with alternative aspects of detected periodic interference, eg based on autocorrelation of measurement results with respect to time. FIG. 7 shows the autocorrelation function of two vectors L _p1 and L _p2 . This method, similar to the Hough transform, detects the period. Another alternative result can also be used to assist each other in selecting a decision threshold. The combination of both methods can improve the detection probability.

  FIG. 6 depicts a device for detecting and characterizing a device's radio resource usage pattern. The device 607 can include at least one antenna or other listening (monitoring or receiving) device 605, a transmitter 606, a processor 601, and a memory 608. Source 604 can be a receiving system, a computer, a notebook computer, a PDA, a cell phone, or other receiving device or system. The source 604 is, for example, a wireless wide area network, a wireless metropolitan area network, a wireless local area network, a terrestrial broadcasting system (radio, TV), a satellite network, a cell phone, or a wireless telephone network, a wired network, an internal communication bus, an internal connection In addition, information may be provided by one or more network connections through these parts or combinations and other types of networks.

  As an example, if the device 607 operates according to 802.11 WLAN, the source 604 may include a medium access control (MAC) layer 602 and a physical layer (PHY) 603. The processor 601 will direct the listening device 605 to monitor the first time period. When the processor 601 detects the first busy slot, it directs the listening device 605 to monitor the second time period. When processor 602 detects the second busy slot, it directs listening device 605 to monitor the third time period. Upon detecting the third busy slot, the processor 601 recognizes the sequence of the first, second and third busy slots as a function of time by comparing the busy slot with the sequence stored in the memory 608. . The connection between the processor 601 and the memory 608 may be, for example, a bus, a communication network, one or more internal connections of a circuit, a circuit card or other device, parts and combinations thereof, and other communication media. it can.

  The processor 601 now performs a randomized Hough transform on the sequence as described above using software, algorithms or other computing means. The processor 601 then generates a histogram based on the randomized Hough transform and identifies the peak of the histogram. Then, the processor 601 determines whether or not the peak corresponds to a known radar by comparing the peak with the peak data stored in the memory 608. The processor 601 identifies the transmission timing based on the known behavior of the radar recognized here. The source 604 determines when to transmit using the transmitter 606 using the MAC 602 and the PHY 603.

  The processor 601 can be an algorithm, a general-purpose or special-purpose computing system, or a hardware configuration such as a laptop computer, a desktop computer, a server, a handheld computer, a dedicated logic circuit, or an integrated circuit. You can also The processor 601 can also be a programmable array logic (PAL), an application specific integrated circuit (ASIC), etc., that includes software instructions that provide known outputs in response to known inputs. It can be programmed hardware. The elements shown here can also be implemented as individual hardware elements operable to perform the operations indicated using encoded logic operations, or by executing hardware executable code.

  The previous expressions and examples are for illustrative purposes and are not intended to limit the scope of the appended claims.

The figure which shows the Hough transform used in order to detect the straight line in image space. The figure which shows the Hough transform used in order to detect the straight line in parameter space. FIG. 6 shows a method for detecting and characterizing a radio resource usage pattern of a device. A graph of the helix (ω = 1) given by RHT Equation 1. It shows a histogram of ω for L _p1. It shows a histogram of ω for L _p2. FIG. 4 shows an apparatus for detecting and characterizing a radio resource usage pattern of an apparatus. Graph of autocorrelation of measurement results with respect to time.

Claims (6)

A method for identifying opportunities in a radio network,
Monitoring the first time period;
Detecting the first busy slot;
Monitoring the second time period;
Detecting a second busy slot;
Monitoring a third time period;
Detecting a third busy slot;
Recognizing a sequence of first, second and third busy slots as a function of time;
Performing a randomized Hough transform on the sequence;
Generating a histogram based on the randomized Hough transform;
Identifying peaks in the histogram;
Determining whether the peak corresponds to a known radar, and identifying a transmission opportunity;
Including methods.   The method of claim 1, further comprising monitoring at least a fourth time period and detecting at least a fourth busy slot.   The method of claim 1, wherein the determining step determines whether the peak corresponds to a known radar in a limited bandwidth. A device for identifying opportunities in a radio network,
a) source,
b) a processor for performing calculation and performing randomized Hough transform; means for generating a histogram based on the randomized Hough transform; means for identifying a peak in the histogram; and means for identifying a transmission opportunity ,
c) a memory; and d) a device having at least one listening device.   The apparatus of claim 4, further comprising medium access control, a physical layer, and at least one transmitter.   The apparatus of claim 4, wherein the listening device is an IEEE 802.11 slot mechanism.

Images