Ultrawideband (UWB) technology has many advantages compared to its narrowband counterpart in many applications. We present a new compact low-cost UWB radar for indoor and through-wall scenario. The focus of the paper is on the development of the signal processing algorithms for ranging and tracking, taking into account the particular properties of the UWB CMOS transceiver and the radiation characteristics of the antennas. Theoretical analysis for the algorithms and their evaluations by measurements are presented in the paper. The ranging resolution of this UWB radar has achieved 1-2 mm RMS accuracy for a moving target in indoor environment over a short range, and Kalman tracking algorithm functions well for the through-wall detection.

Due to its quite unique properties, ultrawideband (UWB) technology finds many applications in different areas, such as UWB sensor network for precise ranging and geolocation, UWB radar and imaging systems with superior penetration and high resolution, and super sensitive UWB radio astronomy [

From the radar perspective, UWB systems exhibit several distinct advantages over the narrowband counterparts [

We present a new compact and low-cost UWB radar system for ranging and tracking of moving objects in indoor and through-wall environments. The UWB radar consists of a commercially available Novelda R2A UWB CMOS chip transceiver [

The compact UWB radar system for indoor and through-wall ranging and tracking.

The ranging resolution of this UWB radar by applying the algorithm has achieved an accuracy of 1-2 mm RMS error for a moving target in indoor environments over a short range, and the Kalman filter tracking algorithm functions well for a through-wall detection.

In this paper, the system is briefed in Section

The UWB radar system consists of two SGBT antennas and one Novelda transceiver; see Figure

Presentation of the operating principle of the compact UWB radar system.

For memory efficiency, each measurement by the Novelda transceiver is taken within a fixed time frame (referred to as the measurement frame, or simply the frame), with an off-set time delay predefined by user. Each measurement has 128 samples. The time interval (or step) between two consecutive samples is 27.8 ps, corresponding to the sample resolution of 4.167 mm in the wave propagation direction. The duration of one frame is then

The received signals can be amplified by user-defined values in the transceiver. The signal sample data are then sent to the microcontroller for data processing.

The self-grounded Bow-Tie (SGBT) antenna, shown in Figure

The SGBT antenna is chosen for this UWB radar system due to the following characteristics: (1) very good time response; (2) compact size and low profile; (3) UWB constant directional radiation beam; (4) good UWB reflection coefficient; (5) better penetration ability than other UWB antennas [

The size of the SGBT antenna is

The comparison between the self-grounded Bow-Tie antenna and a Vivaldi antenna with a size of

The measured time-domain response of the UWB radar system is shown in Figure

Measured time-domain impulse response of the UWB radar system when the two antennas are separated by 250 mm in the face-to-face configuration.

Note that in the antenna setup of the system, it is required that the target should be a certain distance away from the antennas for this radar system to have an accurate ranging, because then the target pulse can be distinguished clearly from the direct-coupling pulse between the antennas. Since the pulse used in this work has a width of about 0.5 ns (Figure

Figure

Signal system model, where the received signal

In the Novelda transceiver, each measurement consists of 128 samples. The received signal

Let vectors

The Novelda transceiver has a particular property: the received signal at each measurement may be rescaled and shifted by a bias. This means that even in a static environment, the clutter map may change with the measurement. Without losing generality, each clutter measurement

Due to the property of (

Suppose that

According to (

An example of the clutter mapping is shown in Figure

100 consecutive measurements of a moving metal plate (

The choice of the number of measurements

As a reference, the direct mean method is also used for the clutter mapping (referred to as the direct-mean method) for each measurement, which is defined as

Three ranging techniques have been investigated. The first two are sample based, which means that the ranging is obtained by finding the sample index of the pulse front in the received target signal. The last one is a fractional-sample method, in order to get a higher ranging resolution.

The received pulse signature

In order to maintain the robustness over a range of objects, the pulse signature is obtained by

Referring to Figure

The pulse signature is defined by the main lobes (inside the pulse signature window) of the averaged target signals over 20 different targets measured in anechoic chamber. The same window sliding through the target signal sequence is used for STFT in pulse-spectrum signature matching.

Then, the cross-correlation

In a traditional radar problem, this classic approach is referred to as matched filter [

The UWB signal spans a large spectrum, and it has been observed in this work that the spectrum of the Novelda transceiver together with the SGBT antennas within a main bandwidth (such as 2–4 GHz in this work) does not severely vary with different scenarios. In other words, the changes of the spectrum with respect to different targets and environments appear mainly in a few frequencies, while the change of the pulse shape in time domain can be significant. It is then natural to take advantage of this spectrum-stable property, instead of using only time-domain analysis. A joint time-frequency domain ranging method, the pulse-spectrum signature matching, is therefore introduced, as follows.

Within each window, the short-time Fourier transform in (

In general, we can define the STFT window function by

The different spectrums when the STFT window slides through the target signal. The solid black line presents the spectrum signature.

The final estimated target index

The above-discussed ranging approaches are sample based. The best achievable ranging resolution is therefore half the sample resolution:

Suppose that the target sample index

However, since the main portion of the signal does not change significantly and therefore the pulse signature

The above expression is valid for all frequency points within the bandwidth defined by the spectrum signature window, that is,

Adaptive calibration is a necessity for this UWB radar system due to the following.

The estimated distance

Now, we utilize the direct coupling between the two antennas to eliminate the unknown parameter

Assume that the distance between the two antennas

A Kalman filter [

The state

For two-dimensional space, a pair of the UWB radar systems are set up orthogonally, as shown in Figure

Setup for 2-dimensional tracking.

By extending the Kalman filter to the two-dimensional case, the state and the observation vectors are defined as

More details about Kalman filter equations and update can be found in [

All algorithms developed for clutter mapping, ranging, and tracking are evaluated by the measurements.

Both the SVD and the direct-mean methods have been evaluated in an indoor environment with and without moving targets.

Without moving targets, the measurement consists of only the clutter and noise:

The evaluation is carried out in terms of the root-mean-square error (RMSE) between the clutter map

Figure _{Clutter} values. From the figure, it can be observed that the SVD clutter mapping gives smaller RMSE values than the direct-mean method does, stating the superiority of the SVD clutter mapping.

RMSE values for evaluation of the SVD and the direct-mean method without moving target.

With the presence of moving targets in the clutter, the evaluation becomes complicated since both the clutter map and the target signal vary. An off-line emulating method is therefore introduced for the evaluation of this case as follows.

A moving target is measured in the anechoic chamber at our lab as

The emulated measurements are shown in Figure

Emulated measurement matrix by adding the measurements of a moving target in anechoic chamber to the measurements of a clutter without moving targets.

RMSE values for evaluation of the SVD and the direct-mean clutter mappings with a moving target.

The off-line emulating method is used for the evaluation of our ranging algorithms, since we do not have any other means to obtain the accurate ranging values of a moving object than manually measuring a static object by a ruler.

A clutter, an indoor environment without moving targets, is measured first. The measurement matrix

Then, a metal ball with a diameter of 20 mm is placed into the environment at 50 different locations statically. At each location, one measurement is performed, referred to as

The emulating measurement matrix

Figure

Comparison of the ranging techniques in terms of RMSE between the real and estimated position for each measurement

Algorithm | Without clutter map removal | With clutter map removal |
---|---|---|

Pulse signature matching | 75.4 (mm) | 48.1 (mm) |

Pulse-spectrum signature matching | 7.2 (mm) | 2.6 (mm) |

Pulse-spectrum signature matching + subsample delay estimation | 5.0 (mm) | 1.4 (mm) |

Evaluation of the ranging algorithms with the clutter map removal.

Evaluation of the ranging algorithms without the clutter map removal.

The ranging evaluation starts from 250 mm, which is longer than the distance of 200 mm between the Tx and the Rx antennas, as explained in Section

From Figure

The pulse-spectrum signature matching is a fast algorithm, since it involves only linear transformations (STFT) and calculations of cross-correlation. This makes the algorithm a very useful one in real-time applications, such as tracking.

It is difficult for us to evaluate the tracking algorithm quantitatively, since the off-line method is not valid for real-time applications, and we do not have other means to get the accurate values of the position and the velocity of a moving target. The qualitative evaluation is accordingly applied.

Figure

The setup for the evaluation of the one-dimensional tracking algorithm through a concrete corridor wall.

Figures

The estimated position by the Kalman filter tracking algorithm for the one-dimensional see-through-wall evaluation.

The estimated velocity by the Kalman filter tracking algorithm for the one-dimensional see-through-wall evaluation.

It should be noted that if the target is not moving orthogonally to the wall, the one-dimensional tracking algorithm with one radar device can only discriminate the distance between the target and the antennas, but not the positions.

The two-dimensional tracking algorithm is evaluated also qualitatively. Figure

An orthogonal measurement setup for two-dimensional tracking (left) and the graphical user interface implemented in Matlab (right), where the red dot indicates the location of the target.

A compact UWB radar system for indoor and through-wall ranging and tracking of moving objects has been built up by using the compact self-grounded Bow-Tie antennas and the low-cost Novelda transceiver. Robust and accurate algorithms for ranging and tracking have been developed. The evaluation by measurements shows that the ranging resolution of this UWB system has achieved to

The main contribution of this UWB radar system is that the system itself has commercially low-cost, compact size. Equipped with the ranging algorithms, it provides a very high time domain resolution. Furthermore, the penetration ability together with the high resolution and the compact system size gives possibilities for many applications. However, the trade-off here is that due to the small size of the system, the dynamic range is relatively low, and therefore, it can only be used for short-range applications.

This work is a collaboration between Imego AB (The Institute of Micro and Nanotechnology in Gothenburg) and Chalmers University of Technology. The authors would like to thank Kenneth Kalmstrom, Lars Landen, Peter Bjorkholm, Jakob Blomgren, Dimitar Kolev, and Sohaib Maalik for their help in the project. This work has been supported in part by The Swedish Foundation for Strategic Research (SSF) within the Strategic Research Center Charmant.