automotive adaptive cruise control using fmcw and mfsk technology

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Automotive Adaptive Cruise Control Using FMCW and MFSK Technology

This example uses:

• Radar Toolbox Radar Toolbox
• Simulink Simulink

This example shows how to model an automotive radar in Simulink® that includes adaptive cruise control (ACC), which is an important function of an advanced driver assistance system (ADAS). The example explores scenarios with a single target and multiple targets. It shows how frequency-modulated continuous-wave (FMCW) and multiple frequency-shift keying (MFSK) waveforms can be processed to estimate the range and speed of surrounding vehicles.

Available Example Implementations

This example includes four Simulink models:

FMCW Radar Range Estimation: slexFMCWExample.slx

FMCW Radar Range and Speed Estimation of Multiple Targets: slexFMCWMultiTargetsExample.slx

MFSK Radar Range and Speed Estimation of Multiple Targets: slexMFSKMultiTargetsExample.slx

FMCW Radar Range, Speed, and Angle Estimation of Multiple Targets: slexFMCWMultiTargetsDOAExample.slx

FMCW Radar Range Estimation

The following model shows an end-to-end FMCW radar system. The system setup is similar to the MATLAB® Automotive Adaptive Cruise Control Using FMCW Technology example. The only difference between this model and the aforementioned example is that this model has an FMCW waveform sweep that is symmetric around the carrier frequency.

The figure shows the signal flow in the model. The Simulink blocks that make up the model are divided into two major sections, the Radar section and the Channel and Target section. The shaded block on the left represents the radar system. In this section, the FMCW signal is generated and transmitted. This section also contains the receiver that captures the radar echo and performs a series of operations, such as dechirping and pulse integration, to estimate the target range. The shaded block on the right models the propagation of the signal through space and its reflection from the car. The output of the system, the estimated range in meters, is shown in the display block on the left.

The radar system consists of a co-located transmitter and receiver mounted on a vehicle moving along a straight road. It contains the signal processing components needed to extract the information from the returned target echo.

FMCW - Creates an FMCW signal. The FMCW waveform is a common choice in automotive radar, because it provides a way to estimate the range using a continuous wave (CW) radar. The distance is proportional to the frequency offset between the transmitted signal and the received echo. The signal sweeps a bandwidth of 150 MHz.

Transmitter - Transmits the waveform. The operating frequency of the transmitter is 77 GHz.

Receiver Preamp - Receives the target echo and adds the receiver noise.

Radar Platform - Simulates the radar vehicle trajectory.

Signal Processing - Processes the received signal and estimates the range of the target vehicle.

Within the Radar , the target echo goes through several signal processing steps before the target range can be estimated. The signal processing subsystem consists of two high-level processing stages.

Stage 1: The first stage dechirps the received signal by multiplying it with the transmitted signal. This operation produces a beat frequency between the target echo and the transmitted signal. The target range is proportional to the beat frequency. This operation also reduces the bandwidth required to process the signal. Next, 64 sweeps are buffered to form a datacube. The datacube dimensions are fast-time versus slow-time. This datacube is then passed to a Matrix Sum block, where the slow-time samples are integrated to boost the signal-to-noise ratio. The data is then passed to the Range Response block, which performs an FFT operation to convert the beat frequency to range. Radar signal processing lends itself well to parallelization, so the radar data is then partitioned in range into 5 parts prior to further processing.

Stage 2: The second stage consists of 5 parallel processing chains for the detection and estimation of the target.

Within Stage 2, each Detection and Estimation Chain block consists of 3 processing steps.

Detection Processing: The radar data is first passed to a 1-dimensional cell-averaging (CA) constant false alarm rate (CFAR) detector that operates in the range dimension. This block identifies detections or hits.

Detection Clustering: The detections are then passed to the next step where they are aggregated into clusters using the Density-Based Spatial Clustering of Applications with Noise algorithm in the DBSCAN Clusterer block. The clustering block clusters the detections in range using the detections identified by the CA CFAR block.

Parameter Estimation: After detections and clusters are identified, the last step is the Range Estimator block. This step estimates the range of the detected targets in the radar data.

Channel and Target

The Channel and Target part of the model simulates the signal propagation and reflection off the target vehicle.

Channel - Simulates the signal propagation between the radar vehicle and the target vehicle. The channel can be set as either a line-of-sight free space channel or a two-ray channel where the signal arrives at the receiver via both the direct path and the reflected path off the ground. The default choice is a free space channel.

Car - Reflects the incident signal and simulates the target vehicle trajectory. The subsystem, shown below, consist of two parts: a target model to simulate the echo and a platform model to simulate the dynamics of the target vehicle.

In the Car subsystem, the target vehicle is modeled as a point target with a specified radar cross section. The radar cross section is used to measure how much power can be reflected from a target.

In this model's scenario, the radar vehicle starts at the origin, traveling at 100 km/h (27.8 m/s), while the target vehicle starts at 43 meters in front of the radar vehicle, traveling at 96 km/h (26.7 m/s). The positions and velocities of both the radar and the target vehicles are used in the propagation channel to calculate the delay, Doppler, and signal loss.

Exploring the Model

Several dialog parameters of the model are calculated by the helper function helperslexFMCWParam . To open the function from the model, click on Modify Simulation Parameters block. This function is executed once when the model is loaded. It exports to the workspace a structure whose fields are referenced by the dialogs. To modify any parameters, either change the values in the structure at the command prompt or edit the helper function and rerun it to update the parameter structure.

Results and Displays

The spectrogram of the FMCW signal below shows that the signal linearly sweeps a span of 150 MHz approximately every 7 microseconds. This waveform provides a resolution of approximately 1 meter.

The spectrum of the dechirped signal is shown below. The figure indicates that the beat frequency introduced by the target is approximately 100 kHz. Note that after dechirp, the signal has only a single frequency component. The resulting range estimate calculated from this beat frequency, as displayed in the overall model above, is well within the 1 meter range resolution.

However, this result is obtained with the free space propagation channel. In reality, the propagation between vehicles often involves multiple paths between the transmitter and the receiver. Therefore, signals from different paths may add either constructively or destructively at the receiver. The following section sets the propagation to a two-ray channel, which is the simplest multipath channel.

Run the simulation and observe the spectrum of the dechirped signal.

Note that there is no longer a dominant beat frequency, because at this range, the signal from the direct path and the reflected path combine destructively, thereby canceling each other out. This can also be seen from the estimated range, which no longer matches the ground truth.

FMCW Radar Range and Speed Estimation of Multiple Targets

The example model below shows a similar end-to-end FMCW radar system that simulates 2 targets. This example estimates both the range and the speed of the detected targets.

The model is essentially the same as the previous example with 4 primary differences. This model:

contains two targets,

uses range-Doppler joint processing, which occurs in the Range-Doppler Response block,

processes only a subset of the data in range rather than the whole datacube in multiple chains, and

performs detection using a 2-dimensional CA CFAR.

This model uses range-Doppler joint processing in the signal processing subsystem. Joint processing in the range-Doppler domain makes it possible to estimate the Doppler across multiple sweeps and then to use that information to resolve the range-Doppler coupling, resulting in better range estimates.

The signal processing subsystem is shown in detail below.

The stages that make up the signal processing subsystem are similar to the prior example. Each stage performs the following actions.

Stage 1: The first stage again performs dechirping and assembly of a datacube with 64 sweeps. The datacube is then passed to the Range-Doppler Response block to compute the range-Doppler map of the input signal. The datacube is then passed to the Range Subset block, which obtains a subset of the datacube that will undergo further processing.

Stage 2: The second stage is where the detection processing occurs. The detector in this example is the CA CFAR 2-D block that operates in both the range and Doppler dimensions.

Stage 3: Clustering occurs in the DBSCAN Clusterer block using both the range and Doppler dimensions. Clustering results are then displayed by the Plot Clusters block.

Stage 4: The fourth and final stage estimates the range and speed of the targets from the range-Doppler map using the Range Estimator and Doppler Estimator blocks, respectively.

As mentioned in the beginning of the example, FMCW radar uses a frequency shift to derive the range of the target. However, the motion of the target can also introduce a frequency shift due to the Doppler effect. Therefore, the beat frequency has both range and speed information coupled. Processing range and Doppler at the same time lets us remove this ambiguity. As long as the sweep is fast enough so that the target remains in the same range gate for several sweeps, the Doppler can be calculated across multiple sweeps and then be used to correct the initial range estimates.

There are now two target vehicles in the scene, labeled as Car and Truck, and each vehicle has an associated propagation channel. The Car starts 50 meters in front of the radar vehicle and travels at a speed of 60 km/h (16.7 m/s). The Truck starts at 150 meters in front of the radar vehicle and travels at a speed of 130 km/h (36.1 m/s).

Several dialog parameters of the model are calculated by the helper function helperslexFMCWMultiTargetsParam . To open the function from the model, click on Modify Simulation Parameters block. This function is executed once when the model is loaded. It exports to the workspace a structure whose fields are referenced by the dialogs. To modify any parameters, either change the values in the structure at the command prompt or edit the helper function and rerun it to update the parameter structure.

The FMCW signal shown below is the same as in the previous model.

The two targets can be visualized in the range-Doppler map below.

The map correctly shows two targets: one at 50 meters and one at 150 meters. Because the radar can only measure the relative speed, the expected speed values for these two vehicles are 11.1 m/s and -8.3 m/s, respectively, where the negative sign indicates that the Truck is moving away from the radar vehicle. The exact speed estimates may be difficult to infer from the range-Doppler map, but the estimated ranges and speeds are shown numerically in the display blocks in the model on the left. As can be seen, the speed estimates match the expected values well.

MFSK Radar Range and Speed Estimation of Multiple Targets

To be able to do joint range and speed estimation using the above approach, the sweep needs to be fairly fast to ensure the vehicle is approximately stationary during the sweep. This often translates to higher hardware cost. MFSK is a new waveform designed specifically for automotive radar so that it can achieve simultaneous range and speed estimation with longer sweeps.

The example below shows how to use MFSK waveform to perform the range and speed estimation. The scene setup is the same as the previous model.

The primary differences between this model and the previous are in the waveform block and the signal processing subsystem. The MFSK waveform essentially consists of two FMCW sweeps with a fixed frequency offset. The sweep in this case happens at discrete steps. From the parameters of the MFSK waveform block, the sweep time can be computed as the product of the step time and the number of steps per sweep. In this example, the sweep time is slightly over 2 ms, which is several orders larger than the 7 microseconds for the FMCW used in the previous model. For more information on the MFSK waveform, see the Simultaneous Range and Speed Estimation Using MFSK Waveform example.

The signal processing subsystem describes how the signal gets processed for the MFSK waveform. The signal is first sampled at the end of each step and then converted to the frequency domain via an FFT. A 1-dimensional CA CFAR detector is used to identify the peaks, which correspond to targets, in the spectrum. Then the frequency at each peak location and the phase difference between the two sweeps are used to estimate the range and speed of the target vehicles.

Several dialog parameters of the model are calculated by the helper function helperslexMFSKMultiTargetsParam . To open the function from the model, click on Modify Simulation Parameters block. This function is executed once when the model is loaded. It exports to the workspace a structure whose fields are referenced by the dialogs. To modify any parameters, either change the values in the structure at the command prompt or edit the helper function and rerun it to update the parameter structure.

The estimated results are shown in the model, matching the results obtained from the previous model.

FMCW Radar Range, Speed, and Angle Estimation of Multiple Targets

One can improve the angular resolution of the radar by using an array of antennas. This example shows how to resolve three target vehicles traveling in separate lanes ahead of a vehicle carrying an antenna array.

In this scenario, the radar is traveling in the center lane of a highway at 100 km/h (27.8 m/s). The first target vehicle is traveling 20 meters ahead in the same lane as the radar at 85 km/h (23.6 m/s). The second target vehicle is traveling at 125 km/h (34.7 m/s) in the right lane and is 40 meters ahead. The third target vehicle is traveling at 110 km/h (30.6 m/s) in the left lane and is 80 meters ahead. The antenna array of the radar vehicle is a 4-element uniform linear array (ULA).

The origin of the scenario coordinate system is at the radar vehicle. The ground truth range, speed, and angle of the target vehicles with respect to the radar are

The signal processing subsystem now includes direction of arrival estimation in addition to the range and Doppler processing.

The processing is very similar to the previously discussed FMCW Multiple Target model. However, in this model, there are 5 stages instead of 4.

Stage 1: Similar to the previously discussed FMCW Multiple Target model, this stage performs dechirping, datacube formation, and range-Doppler processing. The datacube is then passed to the Range Subset block, thereby obtaining the subset of the datacube that will undergo further processing.

Stage 2: The second stage is the Phase Shift Beamformer block where beamforming occurs based on the specified look angles that are defined in the parameter helper function helperslexFMCWMultiTargetsDOAParam .

Stage 3: The third stage is where the detection processing occurs. The detector in this example is again the CA CFAR 2-D block that operates in both the range and Doppler dimensions.

Stage 4: Clustering occurs in the DBSCAN Clusterer block using the range, Doppler, and angle dimensions. Clustering results are then displayed by the Plot Clusters block.

Stage 5: The fourth and final stage estimates the range and speed of the targets from the range-Doppler map using the Range Estimator and Doppler Estimator blocks, respectively. In addition, direction of arrival (DOA) estimation is performed using a custom block that features an implementation of the Phased Array System Toolbox™ Root MUSIC Estimator.

Several dialog parameters of the model are calculated by the helper function helperslexFMCWMultiTargetsDOAParam . To open the function from the model, click on Modify Simulation Parameters block. This function is executed once when the model is loaded. It exports to the workspace a structure whose fields are referenced by the dialogs. To modify any parameters, either change the values in the structure at the command prompt or edit the helper function and rerun it to update the parameter structure.

The estimated results are shown in the model and match the expected values well.

The first model shows how to use an FMCW radar to estimate the range of a target vehicle. The information derived from the echo, such as the distance to the target vehicle, are necessary inputs to a complete automotive ACC system.

The example also discusses how to perform combined range-Doppler processing to derive both range and speed information of target vehicles. However, it is worth noting that when the sweep time is long, the system capability for estimating the speed is degraded, and it is possible that the joint processing can no longer provide accurate compensation for range-Doppler coupling. More discussion on this topic can be found in the MATLAB Automotive Adaptive Cruise Control Using FMCW Technology example.

The following model shows how to perform the same range and speed estimation using an MFSK waveform. This waveform can achieve the joint range and speed estimation with longer sweeps, thus reducing the hardware requirements.

The last model is an FMCW radar featuring an antenna array that performs range, speed, and angle estimation.

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Using FMCW in Autonomous Cars to Accurately Estimate the Distance of the Preceding Vehicle

• Published: 12 January 2023
• Volume 23 , pages 1755–1762, ( 2022 )

Cite this article

• Wei-Tai Hsu 1 &
• Shih-Lin Lin 2  

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Failure to maintain a safe driving distance between moving vehicles is one of the major causes of traffic accidents. Research on maintaining a safe distance with autonomous vehicles is especially important. This paper uses the Hilbert-Huang transform (HHT) method and error estimation to analyze the frequency modulated continuous wave (FCMW) signal of Doppler radar for autonomous vehicle applications. The FMCW signal is decomposed into intrinsic mode functions (IMF) using the empirical mode decomposition (EMD) method. The Doppler radar signal is then reproduced through the Hilbert spectrum obtained using the instantaneous amplitude and instantaneous frequency. The characteristics of the motion of the object are obtained by analyzing the reconstructed Doppler radar signal. The simulation and verification results confirm that this method can accurately estimate the distance between vehicles within the range of 20 ∼ 120 meters at speeds of 50 ∼ 230 km/h. Error estimation is also obtained based on the distance to the car in front and the vehicle’s speed. This study contributes by the application of the proposed Hilbert-Huang transform (HHT) method for the analysis of the frequency modulated continuous wave (FCMW) signal of Doppler radars. The method of this study has been applied to multi-target detection. In this simulation, there are 5 targets, each with a different distance from the car and the speed of the car. The simulation results show that the proposed method can improve the accuracy of the sensor in terms of estimating the distance, reliability and stability of the vehicle, and can increase the safety of the autonomous vehicles.

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Acknowledgements

The authors would like to thank the ministry of science and technology, Taiwan, for financially supporting this research grant No.MOST109-2222-E-018-001-MY2.

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Wei-Tai Hsu

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Hsu, WT., Lin, SL. Using FMCW in Autonomous Cars to Accurately Estimate the Distance of the Preceding Vehicle. Int.J Automot. Technol. 23 , 1755–1762 (2022). https://doi.org/10.1007/s12239-022-0153-4

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Received : 05 January 2021

Revised : 07 November 2021

Accepted : 01 July 2022

Published : 12 January 2023

Issue Date : December 2022

DOI : https://doi.org/10.1007/s12239-022-0153-4

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Modeling RF Front End in Radar System Simulation

This example uses:

• Phased Array System Toolbox Phased Array System Toolbox
• RF Blockset RF Blockset
• Simulink Simulink

In a radar system, the RF front end often plays an important role in defining the system performance. For example, because the RF front end is the first section in the receiver chain, the design of its low noise amplifier is critical to achieving the desired signal to noise ratio (SNR). This example shows how to incorporate RF front end behavior into an existing radar system design.

This example requires RF Blockset™.

Available Example Implementations

This example includes two Simulink® models:

Monostatic Radar with One Target: slexMonostaticRadarRFExample.slx

FMCW Radar Range and Speed Estimation: slexFMCWRFExample.slx

Introduction

Several examples, such as Simulating Test Signals for a Radar Receiver in Simulink and Automotive Adaptive Cruise Control Using FMCW and MFSK Technology (Radar Toolbox) have shown that one can build end-to-end radar systems in Simulink using Phased Array System Toolbox™. In many cases, once the system model is built, the next step could be adding more fidelity in different components. A popular candidate for such a component is the RF front end. One advantage of modeling the system in Simulink is the capability of performing multidomain simulations.

The following sections show two examples of incorporating RF Blockset modeling capability in radar systems built with Phased Array System Toolbox.

Monostatic Radar with One Target

The first model is adapted from example Simulating Test Signals for a Radar Receiver in Simulink which simulates a monostatic pulse radar with one target. From the diagram itself, the model below looks identical to the model shown in that example.

When the model is executed, the resulting plot is also the same.

However, a deeper look in the transmitter subsystem shows that now the transmitter is modeled by power amplifiers from RF Blockset.

Similar changes are also implemented in the receiver side.

With these changes, the model is capable of simulating RF behaviors. For example, the simulation result shown above assumes a perfect power amplifier. In real applications, the amplifier will suffer many nonlinearities. If one sets the IP3 of the transmitter to 70 dB and runs the simulation again, the peak corresponding to the target is no longer as dominant. This gives the engineer some knowledge regarding the system's performance under different situations.

FMCW Radar Range and Speed Estimation

The second example is adapted from Automotive Adaptive Cruise Control Using FMCW and MFSK Technology (Radar Toolbox) . However, this model uses a triangle sweep waveform instead so the system can estimate range and speed simultaneously. At the top level, the model is similar to what gets built from Phased Array System Toolbox. Once executed, the model shows the estimated range and speed values that matches the distance and relative speed of the target car.

However, similar to the first example, the transmitter and receiver subsystems are now built with RF Blockset blocks.

The following figure shows the transmitter subsystem.

The following figure shows the receiver subsystem.

In a continuous wave radar system, part of the transmitted waveform is used as a reference to dechirp the received target echo. From the diagrams above, one can see that the transmitted waveform is sent to the receiver via a coupler and the dechirp is performed via an I/Q demodulator. Therefore, by adjusting parameters in those RF components, higher simulation fidelity can be achieved.

This example shows two radar models that are originally built with Phased Array System Toolbox and later incorporated RF models from RF Blockset. The simulation fidelity is greatly improved by combining the two products together.

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Tech Tutorial: Driver Assistance Systems, an introduction to Adaptive Cruise Control: Part 2

Part 1 discussed “surround sensing” and frequency modulated continuous wave (FMCW) radar as the basis of an Adaptive Cruise Control (ACC).

How ACC works—system hardware A Gunn oscillator (Gunn VCO) is often used to generate the very high frequency transmit signal. If combined transmit and receive antennas are used, the transmit signal is multiplexed with the receive signal by means of a circulator (below). The receive signal is combined with the current transmit signal. The differential signal thereby arising is designated the intermediate frequency. The intermediate frequency is a much lower frequency than the transmit and the receive signals. Sample values of the intermediate frequency are therefore highly suitable for further processing with digital processors.

While the ACC radar sensor works in the high frequency range (radio frequency, RF), the signal processing for calculating the distance and the relative speed takes place in the low frequency (LF) range. The figure below shows the system block diagram for an ACC system. The RF part (left) consists of the Gunn control, the Gunn oscillator, a mixer, and a preamplifier. The LF part comprises an analog-digital converter, the device for signal processing and system control, as well as the power supply and the network interface with the car.

The microcontroller ( TMS470R1VF76B from Texas Instruments ) has two CPUs, an ARM7 RISC (microcontroller, MCU) and a C54x 16-bit fixed-point digital signal processor (DSP). It is therefore ideally suited to applications that must perform both control tasks and high-performance digital signal calculations. Communication between the two CPUs, the numerous peripheral interfaces, and the memory can also be accelerated using direct memory access, DMA . The TMS470R1VF76B meets automotive requirements and is well suited for an ACC system. The figure below shows the block diagram of the microcontroller with a typical division of the ACC application tasks.

ACC system software In addition to the usual diagnostic tasks, the following system tasks belong to the ACC system. The sequence of these system tasks is annotated in the ACC system block diagram below this list.

1. Read in the control presets entered via the HMI (speed, time interval) and the current driving-specific parameters detected by sensors (steering angle, wheel speed, yaw rate, etc.) 2. a) Set the frequency ramps to be transmitted (start frequency, end frequency, ramp time) b) Set the A/D converter (conversion rate, number of samples) 3. Set the transmit frequency and start the Gunn VCO 4. Generate the transmit signal 5. a) Simultaneous transmission of the transmit signal via all antennae and mixing of the intermediate frequency b) Control loop for Gunn control 6. Filtering and amplification of the intermediate frequency 7. Sampling of the intermediate frequency 8. DMA transmission of the samples to the DSP 9. Digital Signal Processing (part 1 of the frequency modulated continuous wave (FMCW) radar tasks) 10. Exchange of the data calculated in the DSP 11. Digital Signal Processing (part 2 of the FMCW radar tasks) 12. Communication via the car network (CAN bus) with the electronic control units (ECUs) to adjust the speed or distance

The ACC system currently developed by Robert Bosch is based on frequency modulation to generate three linear frequency ramps with the different ramp times seen here.

The transmit signal is transmitted via four antennas (A, B, C, and D) simultaneously. The corresponding antenna diagram is seen below.

For each antenna a receive signal is obtained that is combined with the current transmit signal to form an intermediate frequency. This results in twelve intermediate frequencies (A1, A2, A3, B1, , D3) which are analyzed for the later location of potential objects.

The figure below shows an example of a spectrum of an intermediate frequency. To filter the noise contained in the spectrum, prior to actual signal processing an adaptive threshold is overlaid on the intermediate frequency. Frequencies below the threshold are thus excluded as objects. Potential objects in the example are marked with a red x. The peak close to zero frequency is also excluded, since it originates from reflections of the lens. The remaining frequencies are used for further calculations.

The twelve frequency spectra are calculated from the samples of the twelve filtered intermediate frequencies by means of fast Fourier transform ( FFT ). The frequencies contained in the spectrums each represent an object detected and correspond to the peaks remaining after filtering in the frequency spectrum of the intermediate frequency. Each frequency in the frequency spectrum can be assigned to a linear line in the speed/distance diagram using the FMCW radar equation,

This correlation is demonstrated once again here.

However, virtual objects—known as ghosts—may also occur. The probable sequential locations can be predicted on the basis of the natural continuity of the movement, including previous calculations. Consequently, to check the plausibility of the frequency matching performed and to exclude virtual objects, reference is also made to the knowledge acquired of previously detected objects. To this end, the parameters of the detected objects are stored for the next calculation round.

In general, the transmit signal is reflected at several points on the objects (i.e. rear window, trunk lid, wheels, etc.). This is particularly the case with strongly structured objects such as trucks. As a result, several points of intersection (multiple reflections) arise close to each other in the speed/distance diagram, as seen here.

If several receive antennas are used there is the possibility of determining—in addition to the distance and the relative speed—the angle between the object and the longitudinal axis of the host vehicle. This allows clear determination of the position of the object relative to the host vehicle. The figure below shows the detection cone for an ACC system with four receive antennas with superimposed beams.

The superposition of all the receive antennas' results in more than one point of intersection for each object in the speed/distance diagram, similarly to multiple reflections from a single object. The figure below shows detail from a speed/distance diagram where there are two receive antennas. To minimize computational work and memory space required (i.e. for location prediction), it is necessary to bundle all the points detected into a common object.

• The price/performance ratio has become more and more attractive
• Computational performance has increasingly improved, and

The TMS470R1VF76B from Texas Instruments, a microcontroller with two CPUs, makes a very high level of computational performance available in a single module. As a result, the system components for signal processing are reduced so that the entire system has small dimensions overall. The complete system can thus be accommodated on two small PCBs; one for the RF part (radar sensor, Gunn VCO, and preamplifier) and one for the LF part (power supply, digital signal processing, and vehicle-network interface). Today's Long-Range Radar 2 (LRR2) Adaptive Cruise Control System from Robert Bosch (below) has dimensions of 73 x 70 x 60 mm (2.9 x 2.8 x 2.4 in), allowing it to be integrated almost anywhere on the front of the vehicle.

Future ACC systems will optimize the price/performance ratio further with the simultaneous introduction of new functions (such as Stop-and-Go, blind spot detection, etc.) and other types of sensors—making their use in mid-price range or even small vehicles increasingly likely.

The authors would like to thank the staff of departments AE-DA/ELR2 and AE-DA/ELR3 at Robert Bosch for their cooperation on the LRR2 project. Particular thanks goes to Dipl. Ing. (FH) Andreas Höger and to Dr. Götz Kühnle, Dipl.-Ing. (FH) Hermann Mayer, and Dr. Herbert Olbrich, whose technical writings were of assistance in the preparation of this article.

• Paper Driver Assistance Systems for Safety and Comfort Werner Uhler, Hans-Joerg Mathony, Peter M. Knoll Robert Bosch GmbH, Driver Assistance Systems, Leonberg, Germany
• 11 Aachener Kolloquium Fahrzeug- und Motorentechnik 2002 Low-Cost Long-Range-Radar for Future Driver Assistance Systems Dr. Götz K&umul;hnle, Dipl.-Ing. (FH) Hermann Mayer, Dr. Herbert Olbrich Dipl.-Ing. Hans-Christian Swoboda, Robert Bosch GmbH, Stuttgart, Germany
• AutoTechnology, 4/2003 Low-Cost Long-Range Radar for Future Driver Assistance Systems Dr. Götz Kühnle, Dipl.-Ing. (FH) Hermann Mayer, Dr. Herbert Olbrich, Dr. Wolf Steffens Dipl.-Ing. Hans-Christian Swoboda, Robert Bosch GmbH
• Mitsubishi Electric, Automobile-Human Technology Edition, VOL. 94/JUN. 2001 Automobile-Human Technology Edition Millimeter-Wave Radar Technology for Automotive Application Shinichi Honma, Naohisa Uehara
• Paper Waveform Design Principles for Automotive Radar Systems Hermann Rohling, Marc-Michael Meinecke Technical University of Hamburg-Harburg, Germany Department of Telecommunications
• Datasheet TMS470R1VF76B 16/32-BIT RISC FLASH MICROCONTROLLER SPNS076C ” MARCH 2002 ” REVISED JULY 2004 Texas Instruments Incorporated

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Modeling RF Front End in Radar System Simulation

This example uses:

• Phased Array System Toolbox Phased Array System Toolbox
• RF Blockset RF Blockset
• Simulink Simulink

In a radar system, the RF front end often plays an important role in defining the system performance. For example, because the RF front end is the first section in the receiver chain, the design of its low noise amplifier is critical to achieving the desired signal to noise ratio (SNR). This example shows how to incorporate RF front end behavior into an existing radar system design.

This example requires RF Blockset™.

Available Example Implementations

This example includes two Simulink® models:

Monostatic Radar with One Target: slexMonostaticRadarRFExample.slx

FMCW Radar Range and Speed Estimation: slexFMCWRFExample.slx

Introduction

Several examples, such as Simulating Test Signals for a Radar Receiver in Simulink and Automotive Adaptive Cruise Control Using FMCW and MFSK Technology (Radar Toolbox) have shown that one can build end-to-end radar systems in Simulink using Phased Array System Toolbox™. In many cases, once the system model is built, the next step could be adding more fidelity in different components. A popular candidate for such a component is the RF front end. One advantage of modeling the system in Simulink is the capability of performing multidomain simulations.

The following sections show two examples of incorporating RF Blockset modeling capability in radar systems built with Phased Array System Toolbox.

Monostatic Radar with One Target

The first model is adapted from example Simulating Test Signals for a Radar Receiver in Simulink which simulates a monostatic pulse radar with one target. From the diagram itself, the model below looks identical to the model shown in that example.

When the model is executed, the resulting plot is also the same.

However, a deeper look in the transmitter subsystem shows that now the transmitter is modeled by power amplifiers from RF Blockset.

Similar changes are also implemented in the receiver side.

With these changes, the model is capable of simulating RF behaviors. For example, the simulation result shown above assumes a perfect power amplifier. In real applications, the amplifier will suffer many nonlinearities. If one sets the IP3 of the transmitter to 70 dB and runs the simulation again, the peak corresponding to the target is no longer as dominant. This gives the engineer some knowledge regarding the system's performance under different situations.

FMCW Radar Range and Speed Estimation

The second example is adapted from Automotive Adaptive Cruise Control Using FMCW and MFSK Technology (Radar Toolbox) . However, this model uses a triangle sweep waveform instead so the system can estimate range and speed simultaneously. At the top level, the model is similar to what gets built from Phased Array System Toolbox. Once executed, the model shows the estimated range and speed values that matches the distance and relative speed of the target car.

However, similar to the first example, the transmitter and receiver subsystems are now built with RF Blockset blocks.

The following figure shows the transmitter subsystem.

The following figure shows the receiver subsystem.

In a continuous wave radar system, part of the transmitted waveform is used as a reference to dechirp the received target echo. From the diagrams above, one can see that the transmitted waveform is sent to the receiver via a coupler and the dechirp is performed via an I/Q demodulator. Therefore, by adjusting parameters in those RF components, higher simulation fidelity can be achieved.

This example shows two radar models that are originally built with Phased Array System Toolbox and later incorporated RF models from RF Blockset. The simulation fidelity is greatly improved by combining the two products together.

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