Michael Barjenbruch

Instantaneous full-motion estimation of arbitrary objects using dual Doppler radar

Based on high-resolution radars a new approach for determining the full 2D-motion state (yaw rate, longitudinal and lateral speed) of an extended rigid object in a single measurement is proposed. The system does not rely on any model assumptions and is independent of the exact position, expansion and orientation of the object. In comparison to related methods it is not based on temporal filtering, e.g. a Kalman Filter. These methods are subject to an initialization phase and depend heavily on compliance of the underlying dynamic model. In contrast to temporal filtering, the proposed approach reduces the time to react to critical situations that occur in many safety and advanced driving assistance applications. This paper analyzes the velocity profile (radial velocity over azimuth angles) of the object received by two Doppler radar sensors. The approach can handle white noise and systematic variations (e.g. micro-Doppler of wheels) in the signal. The proposed system is applied to predict the driving path of traffic participants. Measurement results are presented for a set-up with two 77 GHz automotive radar sensors.

A fast probabilistic ego-motion estimation framework for radar

This paper presents a fast, joint spatial- and Doppler velocity-based, probabilistic approach for ego-motion estimation for single and multiple radar-equipped robots. The normal distribution transform is used for the fast and accurate position matching of consecutive radar detections. This registration technique is successfully applied to laser-based scan matching. To overcome discontinuities of the original normal distribution approach, an appropriate clustering technique provides a globally smooth mixed-Gaussian representation. It is shown how this matching approach can be significantly improved by taking the Doppler information into account. The Doppler information is used in a density-based approach to extend the position matching to a joint likelihood optimization function. Then, the estimated ego-motion maximizes this function. Large-scale real world experiments in an urban environment using a 77 GHz radar show the robust and accurate ego-motion estimation of the proposed algorithm. In the experiments, comparisons are made to state-of-the-art algorithms, the vehicle odometry, and a high-precision inertial measurement unit.

Instantaneous ego-motion estimation using Doppler radar

The growing use of Doppler radars in the automotive field and the constantly increasing measurement accuracy open new possibilities for estimating the motion of the ego-vehicle. The following paper presents a robust and self-contained algorithm to instantly determine the velocity and yaw rate of the ego-vehicle. The algorithm is based on the received reflections (targets) of a single measurement cycle. It analyzes the distribution of their radial velocities over the azimuth angle. The algorithm does not require any preprocessing steps such as clustering or clutter suppression. Storage of history and data association is avoided. As an additional benefit, all targets are instantly labeled as stationary or non-stationary.

Clustering improved grid map registration using the normal distribution transform

Grid map registration is an important field in mobile robotics. Applications in which multiple robots are involved benefit from multiple aligned grid maps as they provide an efficient exploration of the environment in parallel. In this paper, a normal distribution transform (NDT)-based approach for grid map registration is presented. For simultaneous mapping and localization approaches on laser data, the NDT is widely used to align new laser scans to reference scans. The original grid quantization-based NDT results in good registration performances but has poor convergence properties due to discontinuities of the optimization function and absolute grid resolution. This paper shows that clustering techniques overcome disadvantages of the original NDT by significantly improving the convergence basin for aligning grid maps. A multi-scale clustering method results in an improved registration performance which is shown on real world experiments on radar data.

Joint spatial- and Doppler-based ego-motion estimation for automotive radars

An ego-motion estimation method based on the spatial and Doppler information obtained by an automotive radar is proposed. The estimation of the motion state vector is performed in a density-based framework. Compared to standard vehicle odometry the approach is capable to estimate the full two dimensional motion state with three degrees of freedom. The measurement of a Doppler radar sensor is represented as a mixture of Gaussians. This mixture is matched with the mixture of a previous measurement by applying the appropriate egomotion transformation. The parameters of the transformation are found by the optimization of a suitable join metric. Due to the Doppler information the method is very robust against disturbances by moving objects and clutter. It provides excellent results for highly nonlinear movements. Real world results of the proposed method are presented. The measurements are obtained by a 77GHz radar sensor mounted on a test vehicle. A comparison using a high-precision inertial measurement unit with differential GPS support is made. The results show a high accuracy in velocity and yaw-rate estimation.

A method for interference cancellation in automotive radar

In this paper a method for interference detection and cancellation for automotive radar systems is proposed. With the growing amount of vehicles equipped with radar sensors, interference mitigation techniques are getting more and more important to maintain good interoperability. Based on the time domain signal of a 76 GHz chirp sequence radar the interfering signals of FMCW radar sensors are identified. This is performed by image processing methods applied to the time-frequency-image. With the maximally stable extremal regions algorithm the interference pattern in the signal is identified. Once the disturbed samples are known they are zeroed. To avoid any ringing effects in the processed radar image the neighborhood of affected samples is smoothed using a raised cosine window. The effectiveness of the proposed method is demonstrated on real world measurements. The method reveals weak scattering centers of the vehicle, which are occluded by interference otherwise.

Probabilistic ego-motion estimation using multiple automotive radar sensors

Abstract For automotive applications, an accurate estimation of the ego-motion is required to make advanced driver assistant systems work reliably. The proposed framework for ego-motion estimation involves two components: The first component is the spatial registration of consecutive scans. In this paper, the reference scan is represented by a sparse Gaussian Mixture model. This structural representation is improved by incorporating clustering algorithms. For the spatial matching of consecutive scans, a normal distributions transform-based optimization is used. The second component is a likelihood model for the Doppler velocity. Using a hypothesis for the ego-motion state, the expected radial velocity can be calculated and compared to the actual measured Doppler velocity. The ego-motion estimation framework of this paper is a joint spatial and Doppler-based optimization function which shows reliable performance on real world data and compared to state-of-the-art algorithms.

Memory efficient spectral estimation on parallel computing architectures

A method for spectral estimation is proposed. It is based on the multidimensional extensions of the RELAX algorithm. The fast Fourier transform is replaced by multiple Chirp-Z transforms. Each transform has a much shorter length than the transform in the original algorithm. This reduces the memory requirements significantly. At the same time a high degree of parallelism is preserved. A detailed analysis of the computational requirements is given. Finally, the proposed method is applied to automotive radar measurements. It is shown, that the multidimensional spectral estimation resolves multiple scattering centers on an extended object.