Abstract:This paper focuses on estimating the angle and size of different-sized targets using a radar system that estimates the angles of trucks, vehicles, bicycles, and pedestrians. The problem is that the presence of a sidelobe from the larger target degrades the accuracy of estimating the angle and size of the smaller target in multiple target detection. In this paper, a novel scheme, called antenna element space interference cancelling (AIC), is proposed for reducing the influence of the sidelobe. The performance o… Show more
“…APPS processing is based on the antenna element space interference canceling radar 26 . AIC radar processing enables the provision of a replica received signal vector for the maximum peak angle of a spatial spectrum, which is generated by beamforming-based angle estimation.…”
Section: Methodsmentioning
confidence: 99%
“…A replica signal is generated for the maximum peak angle based on AIC radar processing, and then this replica is subtracted from the received signal vector. A coefficient for an incident plane wave for the pseudo peak, , can be estimated by 26 where is an estimated angle for the pseudo peak and represents the conjugate transpose of a complex vector. Thus, the signal vector after subtracting the replica, , is given by The beamforming algorithm is subsequently applied to the resulting signal vector, and then the spatial spectrum obtained for the field of view angles ranging from to is proportional to the power given by Figure 4 Computationally obtained spatial spectra in the presence of two angularly close targets.…”
Section: Methodsmentioning
confidence: 99%
“…To this end, this paper proposes a higher angular resolution technique by introducing an antenna element space pseudo peak suppressing method, which has extremely low signal processing cost and just use 3T X –4R X MIMO radar with a single radar IC 25 . The proposed method is based on an antenna element space interference canceling (AIC) technique that was proposed in 26 for automotive millimeter wave FMCW radars. The original proposed AIC technique has been previously introduced into multiple target radar imaging for suppressing the influence of side-lobes from large targets.…”
Frequency-modulated continuous wave radar techniques typically have inadequate angular resolutions due to the limited aperture sizes of antenna arrays in spite of employing multiple-input multiple-output (MIMO) techniques. Therefore, despite the existence of multiple objects, angularly close objects with similar distances and relative velocities are recognized as one single object. Autonomous driving requires the accurate recognition of road conditions. This requirement is one of the critical issues to be solved to distinguish significantly close objects. This paper proposes a technique referred to as an antenna element space pseudo-peak suppressing (APPS) method to resolve angularly close targets. The proposed APPS method aims to identify closely spaced objects on roads. These angularly close targets cause a single peak in a spatial spectrum obtained by a beamformer-based angle estimation. The APPS considers this single peak as pseudo. The APPS radar cancels this pseudo peak from the spatial spectrum. Then, the obtained residual received signal is analyzed. With these procedures, the APPS identifies the number of targets. The APPS also estimates the target angles. The proposed APPS is experimentally validated using a typical single-chip MIMO-based radar evaluation board with three transmit (TX) and four receive (RX) antennas. The experimental results confirm that the proposed APPS successfully resolves angularly close pseudo targets with an angle difference of approximately $$0.5^\circ $$
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“…APPS processing is based on the antenna element space interference canceling radar 26 . AIC radar processing enables the provision of a replica received signal vector for the maximum peak angle of a spatial spectrum, which is generated by beamforming-based angle estimation.…”
Section: Methodsmentioning
confidence: 99%
“…A replica signal is generated for the maximum peak angle based on AIC radar processing, and then this replica is subtracted from the received signal vector. A coefficient for an incident plane wave for the pseudo peak, , can be estimated by 26 where is an estimated angle for the pseudo peak and represents the conjugate transpose of a complex vector. Thus, the signal vector after subtracting the replica, , is given by The beamforming algorithm is subsequently applied to the resulting signal vector, and then the spatial spectrum obtained for the field of view angles ranging from to is proportional to the power given by Figure 4 Computationally obtained spatial spectra in the presence of two angularly close targets.…”
Section: Methodsmentioning
confidence: 99%
“…To this end, this paper proposes a higher angular resolution technique by introducing an antenna element space pseudo peak suppressing method, which has extremely low signal processing cost and just use 3T X –4R X MIMO radar with a single radar IC 25 . The proposed method is based on an antenna element space interference canceling (AIC) technique that was proposed in 26 for automotive millimeter wave FMCW radars. The original proposed AIC technique has been previously introduced into multiple target radar imaging for suppressing the influence of side-lobes from large targets.…”
Frequency-modulated continuous wave radar techniques typically have inadequate angular resolutions due to the limited aperture sizes of antenna arrays in spite of employing multiple-input multiple-output (MIMO) techniques. Therefore, despite the existence of multiple objects, angularly close objects with similar distances and relative velocities are recognized as one single object. Autonomous driving requires the accurate recognition of road conditions. This requirement is one of the critical issues to be solved to distinguish significantly close objects. This paper proposes a technique referred to as an antenna element space pseudo-peak suppressing (APPS) method to resolve angularly close targets. The proposed APPS method aims to identify closely spaced objects on roads. These angularly close targets cause a single peak in a spatial spectrum obtained by a beamformer-based angle estimation. The APPS considers this single peak as pseudo. The APPS radar cancels this pseudo peak from the spatial spectrum. Then, the obtained residual received signal is analyzed. With these procedures, the APPS identifies the number of targets. The APPS also estimates the target angles. The proposed APPS is experimentally validated using a typical single-chip MIMO-based radar evaluation board with three transmit (TX) and four receive (RX) antennas. The experimental results confirm that the proposed APPS successfully resolves angularly close pseudo targets with an angle difference of approximately $$0.5^\circ $$
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“…Chirp Sequence (CS) radar, in particular, is considered a promising mainstay in onboard radar systems due to its ability to simultaneously detect distances and relative velocities of multiple targets [4]. To accurately separate and identify pedestrians, a distance resolution of approximately 0.2 meters is necessary, which translates to a required bandwidth of around 3 GHz that inversely proportional to the distance resolution [5]. Therefore, in the future it is expected that high-resolution radars in the mmWave frequency band will become widely adopted, leading to the dense utilization of numerous radars.…”
In recent years, high-resolution 77GHz band automotive radar, which is indispensable for autonomous driving, has been extensively investigated. In the future, as vehicle-mounted CS (chirp sequence) radars become more and more popular, intensive inter-radar wideband interference will become a serious problem, which results in undesired miss detection of targets. To address this problem, learning-based wideband interference mitigation method has been proposed, and its feasibility has been validated by simulations. In this paper, firstly we evaluated the tradeoff between interference mitigation performance and model training time of the learning-based interference mitigation method in a simulation environment. Secondly, we conducted extensive inter-radar interference experiments by using multiple 77GHz MIMO (Multiple-Input and Multiple-output) CS radars and collected real-world interference data. Finally, we compared the performance of learning-based interference mitigation method with existing algorithm-based methods by real experimental data in terms of SINR (signal to interference plus noise ratio) and MAPE (mean absolute percentage error).
With the intelligent development of vehicles, the number of vehicles equipped with millimeter‐wave (mmWave) radars is increasing, and the possibility of interference between radars is rising dramatically. In automatic driving, it will be common for target detection to be affected by multiple interfering radars. Addressing the mutual interference challenges, an adaptive interference detection method based on support vector machines (SVMs) is proposed. First, a window selection is performed on the received signal and features describing the difference between the normal signal and the interference are extracted. Then, we use a nonlinear SVM to distinguish between the interference and the normal signal. After completing the localization of the interference, we use an autoregressive (AR) prediction model to reconstruct the target echo signal. Results from both multiple interference simulation scenarios and real experimental scenarios show that the accuracy of interference localization and the effect of interference mitigation of the proposed method outperforms the mainstream methods.
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