Performance Verification of Symbol-Based OFDM Radar Processing

Performance Verification of Symbol-Based OFDM Radar Processing Christian Sturm, Thomas Zwick Werner Wiesbeck

Martin Braun Insitut für Nachrichtentechnik Universität Karlsruhe (TH) Karlsruhe, Germany [email protected]

Institut für Hochfrequenztechnik und Elektronik Universität Karlsruhe (TH) Karlsruhe, Germany [email protected] Abstract—In this paper a recently proposed novel processing approach for OFDM radar is verified with measurements. This processing algorithm directly calculates a radar image from the transmitted and received modulation symbols that compose the OFDM signal without the need for performing correlation operations. The implementation of a suitable system setup is described. The measurement results confirm that with this approach a very high dynamic range can be obtained. Moreover, the SNR in the measured radar images is investigated. The results prove that the proposed algorithm provides high processing gain.

I.

INTRODUCTION

Radar systems that employ OFDM signals are a promising concept, since they allow the accomplishment of radar measurements and the transmission of digital information simultaneously with only one transmit signal. A typical application area for OFDM Radars are intelligent transportation systems, which require the ability for intervehicle-communications as well as the need for reliable environment sensing. Moreover, OFDM signals are an interesting alternative for many civil and military radar applications [1].

approach has only been verified by computer simulations. In this paper the setup of a suitable system setup and first measurement results will be reported, that confirm the operability of the developed system concept and the outstanding performance of the novel processing approach. The paper is organized as follows. First, a brief introduction in the novel processing approach will be given. Then, the parameterization of the OFDM signal and two different system setups for operation at 6 GHz and 24 GHz will be described. In the following section measurement results that have been obtained with both configurations will be presented and discussed. Moreover, the SNR of measured radar images will be evaluated in order to verify the processing gain. Finally, a conclusion will be given. II.

SYMBOL-BASED OFDM RADAR PROCESSING

The OFDM transmit signal is composed of a parallel stream of orthogonal subcarriers, each modulated with transmit data. One OFDM symbol can be expressed as N "1

#

x(t) = I(n) exp( j2$f n t), 0 % t % T . (1) Typically, OFDM radar signals are processed with a n= 0 correlation of the transmitted and received baseband signal as for any pulse radar system [2]. The drawback of this with N denoting the number of orthogonal subcarriers, fn being procedure lies in the fact that the transmit data influences the the individual subcarrier frequencies, T being the elementary quality of the radar measurements. Randomly occurring OFDM symbol duration, and {I(n)} representing an arbitrary correlations in the transmit data produce unpredictable ! information series consisting of complex modulation symbols sidelobes in the radar image. In order to overcome this obtained through a discrete phase modulation technique, e.g. problem, the authors have already proposed a novel phase shift keying (PSK). In order to avoid interference processing approach for OFDM radar that overcomes this between the single subcarriers, the subcarriers have to be drawback [3]. This is achieved by calculating the radar image orthogonal, which is fulfilled in case of Δf = 1/T, where Δf directly from the transmitted and received modulation represents the frequency difference between two adjacent symbols composing the OFDM signal. During the processing subcarriers. the transmit data is eliminated, which guarantees a reliable The basic idea behind the symbol-based processing performance of the radar system that is completely approach consists in comparing the transmitted information independent from the transmit data. Up to now this processing {I(n)} and the received soft-state information {Ir(n)} obtained We would like to thank the Rohde&Schwarz company for supporting us with measurement equipment.

in the receiver at the output of the OFDM de-multiplexer before the channel equalization and the decoding is performed. At this point the distortion from the channel is fully contained in the complex modulation symbols {Ir(n)}. Since all information symbols in one OFDM symbol are transmitted through the channel at different carrier frequencies separated by Δf, the received information symbols can be used in order to perform channel sensing at discrete frequencies like in stepped frequency radar. The samples of the frequency domain channel transfer function can easily be obtained by simply calculating an element-wise division

Idiv (n) =

Ir (n) . I(n)

(2)

The corresponding channel impulse response, which represents the radar range profile, is obtained as the inverse (discrete) Fourier transform of {Idiv(n)}

!

and processed in MatLab, the signal generator is operated in an arbitrary waveform generator mode, the receiver in a pure sampling mode. TABLE I. OFDM Radar System Parameters Symbol

Quantity

Value

fc

Carrier frequency

6 GHz / 24 GHz

Nc

Number of subcarriers

1024

Δf

Subcarrier spacing

90.909 kHz

T

Elementary OFDM symbol duration

11 µs

Tp

Cyclic prefix length

1.375 µs

Tsym

Transmit OFDM symbol duration

12.375 µs

B

Total signal bandwidth

93.1 MHz

Δr

Radar range resolution

1.61 m

dmax

Unambiguous range

1650 m

h(k) = IDFT({Idiv (n)}) =

!

(3) % 2$ ( 1 N "1 Idiv (n) exp' j nk * , k = 0,…,N "1 # & N ) N n= 0

With this approach the processing is completely independent from the transmitted information, since it relates every received modulation symbol to a transmitted one. No sidelobes except those caused by the Fourier transform will appear in the radar image. This fact promises a constant and reliable system performance and a very high dynamic range of the radar measurements. III.

SYSTEM PARAMETERIZATION AND SETUP

For applications in intelligent transportation systems in the 24 GHz ISM band a suitable OFDM system parameterization has been derived in [4]. These system parameters have been optimized in order to obtain optimum performance taking into account multi-path propagation and the Doppler effect and will be applied for the measurements. An overview on these system parameters is provided in Table 1. The limitation of the unambiguous range is a result of the sampling of the spectrum in the symbol-based processing. The system setup that has been developed can be operated in two different configurations. The system consists of three main hardware components, which are a Rohde&Schwarz (R&S) SMJ100A vector signal generator, a R&S FSQ26 signal analyzer, and optionally a R&S SMR40 microwave signal generator. The SMJ100A is limited to a maximum carrier frequency of 6 GHz but offers much higher output power than the SMR40. Therefore it has been decided to set up two different configurations, one with the SMJ100A only in order to achieve high output power at 6 GHz and another one with both SMJ100A and SMR40 in order to generate a signal at the intended carrier frequency of 24 GHz but with lower transmit power. All instruments are connected through an Ethernet link and controlled from a computer via the MatLab Instrument Control Toolbox. All signals are generated

The first configuration of the system setup is shown in Fig. 1. The transmit signal is generated in MatLab, transferred to the signal generator and radiated. The signal analyzer is synchronized in phase through a 10 MHz reference signal and in time through a trigger signal. The signal analyzer samples the I and Q components of the received signal and transfers them back to the computer. The signal generator provides a maximum carrier frequency of 6 GHz and a maximum peak power of 20 dBm. Since with the chosen parameters the OFDM signal shows a relatively stable peak-to-average power ratio (PAPR) of approx. 10 dB, a maximum mean transmit power of 10 dBm is available. The employed horn antennas at the transmitter and at the receiver have a gain of 18.5 dBi each.

Fig. 1. First configuration of the system setup with maximum carrier frequency of 6 GHz

In order to carry out measurements with a carrier frequency of 24 GHz an additional mixer is required. For that purpose a slightly different second setup is used, in which the output signal of the SMJ100A signal generator is fed to the external modulation signal input of the SMR40 signal

generator. Since the SMR40 does not support I/Q-signals, its output signal shows two sidebands. With an intermediate frequency of 200 MHz at the input of the SMR40 and a local oscillator frequency of 23.85 GHz, the center frequencies of the two sidebands occur at 23.65 GHz and 24.05 GHz, respectively. The receiver is tuned to the upper sideband, which spans from 24.0 GHz to 24.1 GHz and hence exactly occupies the 24 GHz ISM band. The lower sideband is only a side effect of the immature setup and will not occur when using an I/Q mixing stage in future systems. The external modulation input of the SMR40 does allow for output power control. When driving the SMR40 with an average input signal power of 0 dBm, a total output power of -9 dBm has been measured with a thermal sensor, hence the desired sideband has a transmit power of only -12 dBm. Also in this setup modified horn antennas are employed, which have a gain of 22 dBi each. IV.

MEASUREMENT RESULTS

The conducted measurements have two main intentions. First, the general operability of the novel processing approach and the achievable dynamic range are to be verified. This can be achieved at best with the first configuration of the system setup, since this configuration provides higher transmit power. Second, also the performance of the processing regarding the processing gain and the SNR of the radar images has to be analyzed, in order to prove its competitiveness to the classical processing approach in this regard. This verification is performed with the second setup. In the following the investigated scenarios and the obtained results will be presented in detail. A. Verification of the Dynamic Range In order to verify the dynamic range a scenario with objects distributed over a wide range is required. Therefore it has been decided to carry out the measurements from the rooftop of our institute building, which has several other high buildings in its vicinity that will act as clearly identifiable targets. A photo of the measurement setup and the background in the viewing direction of the radar platform is shown in Fig.

2. For all large objects the approximate distance is given. The closest metal object is a large satellite dish that is lying on the roof in a distance of 10 m. The most distant large building is located approximately 380 m away. The measurement system is operated at a carrier frequency of 6 GHz with the maximum output power of 10 dBm. The transmit signal is modulated with random data and QPSK subcarrier modulation. The normalized radar image obtained with the novel symbol-based processing approach, Hamming windowing and coherent integration over 256 subsequent OFDM symbols is shown in Fig. 3. It can be seen that multiple reflections from all objects appear in the radar image. No sidelobes due to random correlation effects occur. The dynamic range between the strongest and the weakest reflection is 49.5 dB. The relative average noise level in the radar image compared to the strongest peak has been evaluated for distances larger than 400 m (where no reflections are present) and amounts -70.3 dB. Hence, the dynamic range is superior compared to the classical correlation based OFDM radar processing approach, where previous investigations of the authors have shown a limitation of the dynamic range to approximately 30 dB with identical system parameters [3].

Fig. 3. Calculated radar range profile with symbol-based processing

B. Verification of the Radar Image SNR In order to completely characterize the system performance also the SNR of the radar image after the processing has to be analyzed. Correlation based processing is known to provide high processing gain. The gain provided by a correlation processor is equal to the number of correlated samples, which in the case of processing over Nsym subsequent OFDM symbols consisting of Nc subcarriers each results in a total processing gain of

G = N c " N sym .

Fig. 2. Radar measurement for the verification of the dynamic range

(4)

However, having a closer look at the symbol based processing, it becomes obvious that also here the same gain described in (4) must occur, since the inverse Fourier ! transform in (3) provides an SNR gain of Nc and the coherent integration over subsequent OFDM symbols gives an

additional gain of Nsym. Hence, the SNR of the radar image calculated by symbol based processing is described by

SNR =

Pr " N c " N sym Pn

calculated from the areas outside the main peak and are reported in Table 2. Moreover, Table 2 shows the SNR values that are theoretically expected from (6) taking also into account the cable losses.

(5)

TABLE II.

MEASURED AND THEORETICAL SNR VALUES Distance to the reflector in m

with Pr being the received signal power and Pn denoting the noise power at the radar processor input. Taking additionally into account the radar equation this results in

!

4

10

20

Measured SNR in dB

49.8

41.4

30.8

Expected SNR in dB

60.7

44.5

32.8

2

SNR =

Pt " N c " N sym " Gt " Gr " # " $ Pn " (4 % ) 3 r 4

(6)

with Pt being the transmitted signal power, Gt and Gr being the transmit and receive antenna gain, λ being the wavelength, and σ denoting the radar cross section of the reflector.

!

In order to verify that this relation applies for practical OFDM radar measurements with symbol-based processing, additional measurements have been carried out with the second system setup in the 24 GHz ISM band, in which radar images of one single trihedral reflector with an RCS of σ = 16.3 dBm2 have been taken for three different distances of r = 4, 10, 20 m. The radar range profiles calculated with symbol-based processing each normalized to its maximum are shown in Fig. 4.

With increasing distance the measured SNR values approach the expected ones. It can be assumed that for the distance of 4 m the unexpected loss was caused by the fact that the transmit and the receive antenna had a distance of 30 cm and hence the receiving antenna was outside the main reflection direction. For higher distances of the reflector there is only a minor discrepancy between the measured and the expected SNR, which can be explained with imperfect component behavior and the uncertainty in the receiver noise figure specifications. Nevertheless, it is evident that the symbol based processing provides the gain claimed in (5) and hence offers the same SNR performance like correlation processing. V.

CONCLUSIONS

In this paper the first verification measurements for a novel OFDM radar processing approach that operates on the modulation symbols instead on the baseband signals have been presented. This algorithm offers a constant radar performance independent of the information content of the OFDM signal. A suitable OFDM radar configuration and system setup has been developed and implemented. With this system it has been verified that the proposed processing algorithm is fully applicable for practical radar measurements and offers a very high dynamic range that clearly outperforms correlation based OFDM radar processing. Moreover, an investigation of the radar image SNR has proven that the novel processing approach provides the same processing gain as correlation processing. Overall, it has been verified that the symbol-based processing offers a considerable improvement of OFDM radar performance.

Fig. 4. Radar measurement for the verification of the SNR

REFERENCES The relatively high noise level in the radar images is a result of both the low transmit power of only -12 dBm and the extremely high noise figure of the receiver. The receiver noise power level specified by the manufacturer is -143 dBm/Hz at 22 GHz, which results in a total noise power of -64 dBm. Also in Fig. 4 it can be observed that the reflector does not exactly appear at its actual distance in the radar image. This is caused by the limited steepness of the trigger signal, which results in a distance inaccuracy of typically around 1 m. Nevertheless, it can be clearly seen that in the normalized images the noise level drastically increases with the distance of the reflector. The average noise levels for the different distances have been

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