Available online at www.sciencedirect.com
ScienceDirect Procedia Engineering 129 (2015) 178 – 183
International Conference on Industrial Engineering
An automated control system by probe signal generator in radar Zhiganov S.N.a, Smirnov M.S.b* a b
23, Radiozavodkoe highway, Murom 602264, Russian Federation 23, Radiozavodkoe highway, Murom 602264, Russian Federation
Abstract The article discusses the creation an automated system by probe signal generator in radar. This system was created by means of National Instruments measurement equipment and LabVIEW software environment. The paper shows a block diagram of test system and describes its components. For this test system a test method, software and typical signals were developed. This system is used for the production monitoring of the probe signal generators. ©2015 2015The TheAuthors. Authors. Published Elsevier © Published by by Elsevier Ltd.Ltd. This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of the organizing committee of the International Conference on Industrial Engineering (ICIE(http://creativecommons.org/licenses/by-nc-nd/4.0/). 2015). Peer-review under responsibility of the organizing committee of the International Conference on Industrial Engineering (ICIE-2015) Keywords: PXIe-1085, PXI-5105, radar, chirp pulses, radar receiver signal compress, heterodyne.
1. Introduction Modern radar – is a complex system comprising mechanical, hydraulic, electrical and other elements. But the main importance is the radio equipment. Radio equipment is used for control of the radar, for generating and receiving radar signals, for transmit, receive, processing and storage of information [1-6]. In the production of radio is very important to monitor its performance. Currently, control is carried out by conventional methods which are based on measuring the values of input and output signals and reception waveforms at the control points. Unfortunately, these methods do not allow finding a failure and operational error, especially when verifying digital signal processing systems [7-9]. One example is the evaluation of the main characteristics of the chirped pulse for probing signals that are generated by radar heterodyne. Radar heterodyne consist of frequency synthesizer for producing carrier, modulator, amplifiers, and balanced circuit. The efficiency of radar depends on quality of heterodyne signal. Therefore, parameter monitoring of chirp pulses is important part of the control system by radar. Due to the large number of measurements, parameter monitoring must be automatized.
* Corresponding author. Tel.: +7-905-140-52-51 E-mail address:
[email protected]
1877-7058 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of the International Conference on Industrial Engineering (ICIE-2015)
doi:10.1016/j.proeng.2015.12.029
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One of the most effective ways to solve this problem is the using modular instruments from National Instruments. The redefined instrumentation approach provided by National Instruments uses open software and modular hardware with key elements (multicore CPUs, user-programmable FPGAs, PCI Express, data converters, and LabVIEW system design software) to address such demanding challenge. Using NI LabVIEW software with PXI modular instrumentation to create a test system that can be used in both characterization and production testing and delivers 11X reduction in capital equipment costs, 15X reduction in footprint, 66X reduction in weight, and 16X reduction in power consumption over the previous automated test equipment used in production. With this approach, we can build test systems based on flexible hardware and scalable software resulting in savings in capital equipment, system development, and maintenance costs while realizing faster test execution. [10]. The aim of the presented paper is the development of an automated system for control of parameters of probing chirp signal. 2. Methods of solving the problem A number of methods to creation of automated systems is referred in works [11-16]. Similar systems are described in works [17-19]. As we can see from that works, the optimum way is use modular measurement equipment with integrated software development environment. Modular measurement equipment from National Instruments is used for generating test signals, digitizing measured signals and measuring parameters. Software development environment LabVIEW is used for processing received information and display of results. 3. Description of the system Block diagram of the measurement system is shown in Fig. 1.
Fig. 1 Block diagram of the measurement system
The system comprises: a heterodyne, a receiver and a measuring unit. To supply the heterodyne and the receiver uses an external power supply ±27 V. Heterodyne generates the following signals: x pilot signal (PS), which is chirped pulse with a duration of 67 ȝs and frequency deviation of 1.2 MHz in most measurement modes; x start trigger (H1); x coherent wave signal with frequency 24 MHz (H2).
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The signals from the heterodyne through attenuators are fed to the regular radar receiver. Signals H1 and H2 are directly fed to the measuring unit. PS signal is supplied to the measuring unit only after transferring it to an intermediate frequency of 30 MHz. In the measuring unit is coherent digitizing of signal PS with the signal H2. Process of digitizing is triggered by signal H1. Measuring unit is used to detect and measure the parameters of chirped pulses generated by radar heterodyne. The main functions of this unit: x visual display of digitized packets chirped pulses; x calculation of the phase values in each pulse in degrees and determining the range of the phase change in the chirp pulses; x calculation of the range of variation of maximum values of the amplitude chirp pulse; x convolution chirped pulses; x estimate of the range of variation of the maximum convolute chirp pulses; x calculation of the range of variation of the maximum levels of side lobes in a pack of convolute chirp pulses. Measuring unit (fig. 2) is based on PXI chassis (PXIe-1085), composed of digitizer (PXI-5105) and controlled by a PXIe-8135.
Fig. 2. A block diagram of a measuring unit
Front panel of measuring unit is shown in Fig. 3. VI created in LabVIEW 2013 allows measurements in sequential mode and batch mode (from 2 to 12 pulses in batch). Before start measurements must be set frequency deviation and the base of signal. In batch mode we must set the number of pulses in the pack and each pulse repetition period. During the measurements, the front panel displays the digitized packs of chirped pulses before and after convolution. At each pulse in the packet is determined the amplitude and phase of the peak. Then we calculate the maximum, minimum, median value among the pulses of one pack.
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Fig. 3. Original and convolute chirp pulse
In each pulse calculated value of the phase of even and odd discrete.
Mi
§ S · a tan¨¨ i ¸¸ © S i 1 ¹
(1)
Convolution chirped pulses works based on two algorithms: discrete convolution and fast convolution. For discrete convolution we used expression:
Sg i
Ii k
2 2 § N 1 § · § N 1 · 20 lg¨¨ ¨ ¦ S i k sin Ii k ¸ ¨ ¦ S i k cos Ii k ¸ ©k 0 ¹ ©k 0 ¹ ©
5S 2
N · SB § ¨i k ¸ 2 2¹ N ©
N· § ¨i k ¸ 2¹ ©
· ¸ ¸ ¹
(2)
2
N – number of discrete in one chirp pulse; B – base of chirp pulse. Fast convolution algorithm consists from six steps: 1. Forming of the reference chirp signal samples with Hamming (fig. 4).
(3)
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S.N. Zhiganov and M.S. Smirnov / Procedia Engineering 129 (2015) 178 – 183
Fig. 4 Reference chirp signal samples with Hamming
2. 3.
Calculating FFT from reference signal. Calculating FFT from input chirp pulse (fig. 5).
Fig 5. FFT from chirp pulse
4. FFT samples from chirp pulse multiply on complex conjugate FFT samples from reference signal. 5. By using inverse FFT we calculate cross correlation. 6. For calculation of the range of variation of the maximum levels of side lobes we define envelope from cross correlation. In separate windows in front panel of VI shows a graph of relations of maximum and minimum peak convolute chirp pulses in the packet, difference between maximum and minimum of convolute chirp pulses in the packet, and the mean value of the phase, minimum and maximum side lobe level of the convolute chirp pulses. New automated system has successfully replaced the existing system based on desktop devices and expanded the possibilities measuring system. This allowed significantly speeding up the process of testing and improving the
S.N. Zhiganov and M.S. Smirnov / Procedia Engineering 129 (2015) 178 – 183
quality of measurements. 4. Summary New automated system is successfully replacing traditional test system based on desktop devices and expand its capabilities. New automated system accelerates all measurements and improves measurement quality. Acknowledgements This work was supported by grants RFBR #14-07-00293 and RFBR #14-02-97510 r_center_a. References [1] V.A. Vasin, I.B. Vlasov, Y.M. Egorov, Information technologies in radio systems, Moscow State Technical University, Moscow, 2004. [2] S.Z. Kuzmin, Digital radar. Introduction, KVITS, Kiev 2000. [3] Y.D. Shirman, Radioelectronic systems: Fundamentals and theory, Radiotec, Moscow, 2007. [4] G. Richard, Curry Radar System Performance Modeling, Artech house, inc., 2005. [5] B.A. Bakulev, Radar systems, Radiotec, Moscow, 2004. [6] Y.P. Grishin, V.P. Ipatov, Y.M. Kazarinov, Radio system, Vishaya shkola, Moscow, 2004. [7] V.I. Nefedov, A.S. Sigov, V.K. Bityukov, V.I. Khakhin, Metrology and radiomeasurement, Vishaya shkola, Moscow, 2006. [8] G.G. Raneev, Information-measurement technology, Vishaya shkola, Moscow, 2002. [9] V.I. Nefedov, A.S. Sigov, Metrology and electric and radio measurements in telecommunication systems, Vishaya shkola, Moscow, 2005. [10] Information on http://www.ni.com/pxi/ [11] S.N. Danilin, M.V. Makarov, S.A. Shchanikov, S.V. Panteleev, Algorithm of choice the parameters of artificial neural network in the evaluation of the amplitude of the harmonic signal with respect to the destabilizing influences, Methods and devices of transmission and processing of information. 16 (2014) 70–73. [12] S.N. Danilin, M.V. Makarov, Shchanikov, Algorithm of designing neural networks with minimum capacity. 1 (2013) 245–251. [13] S.N. Danilin, Study of fault tolerance device transform coordinates with neural network architecture, Methods and devices of transmission and processing of signals in radio and radars. 5 (2009) 31–33. [14] S.N. Danilin, M.V. Makarov, The method for determining the minimum capacity of artificial neural networks, Radio- and telecommunication systems. 2 (2013) 71–75. [15] S.N. Danilin, Research of work quality index choice influence on result of devices faulttolerance estimation with neiro architecture, Radioand telecommunication systems. 4 (2011) 15–19. [16] S.N. Zhiganov, Crimean Conference “Microwave & Telecommunication Technology”. (2014) 334–335. DOI: 10.1109/CRMICO.2014.6959419 [17] S.N. Zhiganov, M.S. Smirnov, D.N. Romanov, Stand for assessing the quality decimated signal to a digital receiver NI – NIDays. (2014) 46–47. [18] S.N. Zhiganov, M.S. Smirnov, D.N. Romanov, Signal measurement system of intermediate frequency radar receiver, NIDays. (2014) 48–50. [19] Correlation and frequency properties of nonequidistant pulse sequence obtained by means of Frank codes CriMiCo. (2014) 334–335. [20] K.K. Khramov, S.N. Zhiganov, Investigation of the characteristics of digital filters in the software-hardware device for evaluation of modulation parameters of chirp waveform, Radio- and Telecommunication Systems. 3 (2011) 30–34. [21] N. Levanon, Eli Mozeson Radar Signals, Wiley-Interscience, 2004.
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