This paper presents a fixed-point implementation of the preprocessing using a field programmable gate array (FPGA), which is required for a multipath joint angle and delay estimation (JADE) used in road traffic applications. This paper lays the foundation for many model-based parameter estimation methods. Here, a simulation of a vehicle-based localization system application for protecting vulnerable road users, which were equipped with appropriate transponders, is considered. For such safety critical applications, the robustness and real-time capability of the localization is particularly important. Additionally, a motivation to use a fixed-point implementation for the data preprocessing is a limited computing power of the head unit of a vehicle. This study aims to process the raw data provided by the localization system used in this paper.

The data preprocessing applied includes a wideband calibration of the physical localization system, separation of relevant information from the received sampled signal, and preparation of the incoming data via further processing. Further, a channel matrix estimation was implemented to complete the data preprocessing, which contains information on channel parameters, e.g., the positions of the objects to be located.

In the presented case of a vehicle-based localization system application we assume an urban environment, in which multipath propagation occurs. Since most methods for localization are based on uncorrelated signals, this fact must be addressed. Hence, a decorrelation of incoming data stream in terms of a further localization is required. This decorrelation was accomplished by considering several snapshots in different time slots. As a final aspect of the use of fixed-point arithmetic, quantization errors are considered. In addition, the resources and runtime of the presented implementation are discussed; these factors are strongly linked to a practical implementation.

The objective of a localization system is to determine the relative position
of an object to be located. Different applications have brought forth several
localization methods

The aforementioned challenges must be effectively implemented. In contrast to previous studies, this study focuses on a practical implementation of data preprocessing required for JADE, focusing on a safety critical vehicle-based localization system used in road traffic applications.

The paper is organized as follows: first, a transponder-based localization system for protecting vulnerable road users is presented. Here, the boundary conditions are defined, such as the required throughput of the localization defined by the application. Next, we study the localization unit. System errors, which occur in a practical application in contrast to theoretical considerations, are identified. These errors can be taken into account within the signal processing. For this purpose, a preprocessing step will be introduced to account for the chosen identified non-idealities. This preprocessing step is the focus of this paper. Subsequent localization methods can build on this step. A hardware architecture for compensating these non-idealities is presented. A field programmable gate array (FPGA) is used as the hardware platform. We discuss the key components as well as resource requirement and run time of the architecture. The architecture are assessed in terms of the safety critical application. Finally, a localization method is applied which is based on the presented fixed-point preprocessing. Thus it is possible to make a statement on the impact of fixed-point implementation on the subsequent localization result.

The urban environment is described by a multipath propagation channel, as
shown in Fig.

Multipath propagation channel for location applications in the urban environment.

The sampled received signal is represented in a compact form by

Finally, the system requirements are considered. The system should be
designed for at least 20 users, as such a number reflects the urban
environment. In addition, an update of 50 Hz is assumed, which results in an
access time of

This section presents the system design of a cooperative sensor system,
focusing on an application out of the localization field. First, a block
diagram of the system is shown. The function of each component is summarized,
and differences with respect to a theoretical consideration are identified.
Finally, an efficient preprocessing implementation is presented.
Figure

Block diagram of a localization unit as a part of a cooperative sensor system for protection of vulnerable road users.

On a localization request, the transponder unit responds with a digital
modulated transmission sequence

In contrast to the theoretical considerations, the localization unit consists
of non-ideal components. Generally, antenna elements do not have
omnidirectional characteristics. In addition, coupling exists between
individual antenna elements

Next, an implementation of data preprocessing for a model-based parameter estimation is presented, which lays the foundation for further model-based estimation methods.

Figure

Block diagram of an implementation of a detector for sequence separation for a localization system.

The detection is based on a sample-wise comparison of a received input
sequence

Next, the calibration of the physical system is considered, denoted by
step 2. Figure

Block diagram of an implementation of a wideband calibration for a localization system.

The calibration matrix is stored in the random-access memory (RAM). Next, the
actual calibration process is considered, denoted by step 2.2. Applying
calibration weights

Finally, the channel matrix is estimated, denoted by step 3. The starting
point forms Eq. (

Block diagram of the implementation of an efficient channel matrix estimation approach for a localization system. (1) Complex hardware multiplier. (2) Line selection pointer.

The required pseudo-inverse signal matrix

A multiplexer selects the

Performance evaluation for the presented implementation of a real-time capable data preprocessing for model-based parameter estimation.

It is assumed that the complex fading amplitudes are constant during one
snapshot, and that they vary from one snapshot to the next. This is valid for
the assumed scenario. Thus, decorrelation is performed by averaging multiple
channel matrices from different snapshots, which are uncorrelated in terms of
complex fading. Alternatively, additional techniques must be applied, e.g.,
spatial – and temporal smoothing

In this section, the system performance is discussed. The results are
summarized in Table

In terms of the application, the system design is real time-capable. The FFT,
which is used in step 2, determines the latency and the run time of the
presented data preprocessing. If required, a part of the fast hardware
multiplier DSP48E1 can be replaced by using look-up tables (LUTs). However,
this approach has a decreased clock frequency

In Table

Comparison of different FFT architectures in terms of a performance evaluation of the presented implementation of a real-time capable data preprocessing for model-based parameter estimation.

The speed advantage of using a radix-4 FFT architecture, compared to a
radix-2, architecture results in an increased resource requirement, which
increases disproportionately. In this case, the application cost and benefits
are out of proportion, since the runtime requirement is complied with both
architectures

This section focuses on the application of the presented data preprocessing
for the case of model-based parameter estimation. For this purpose, the
incoming data

The local maxima provide information regarding the position

Spectral Function of JADE-MUSIC in the case of the presented data preprocessing approach.

Data preprocessing is performed as described above. In this case, a direct
path and an additional multipath are assumed. The object is located at

In this study, a real time-capable system design of the data preprocessing
for a vehicle-based localization system used in road traffic applications was
presented. As an implementation platform, an FPGA was used. The objective was
the compensation of hardware imperfections compared to the theoretical
consideration of a localization system. This preprocessing approach includes
a separation of relevant information from the incoming data stream, a
wideband calibration of the physical system and a channel estimation, as well
as a decorrelation of the correlated signal components, which are contained
in the incoming data stream because of a multipath assumption of the wireless
channel. In this paper, the implementation aspects, including resources and
run time aspects, were discussed. It was demonstrated that FFT, which is used
for a frequency-selective calibration, is computationally intensive. For this
purpose, two architectures (radix-2 and radix-4) were compared. Depending on
the requirements, a balance between the resources and run time was found to
be required. Using the presented architecture real-time capability was
achieved. Each snapshot incurred a processing time of approximately
9.2

Due to the digital signal processing in fixed-point arithmetic (processing word width of 12 bit), quantization errors occurred. The used word width was found to be sufficient for a subsequent localization. The proposed implementation was demonstrated to be well-suited for the application of protecting vulnerable road users. As a next step, it will be useful to account for the errors originating from the antenna array, for example, the consideration of mutual coupling. This will also impact the channel matrix estimation and localization results.

Data used in this paper is available upon request to Timo Patelczyk (timo.patelczyk@tum.de).This work was supported by the German Research Foundation (DFG) and the Technische Universität München within the funding programme Open Access Publishing. Edited by: J. Anders Reviewed by: two anonymous referees