Target Recovery from Randomly Distributed Radars using Compressed Sensing Techniques

Reißland T, Kölpin A, Weigel R (2017)

Publication Language: English

Publication Type: Conference contribution, Abstract of lecture

Publication year: 2017

Event location: Miltenberg DE


In highly automated industrial applications radar sensor systems already play an important role. For future applications, good knowledge about the three-dimensional environment of physically acting industrial systems will be beneficial, if not necessary. For this reason, the use of distributed radar systems to gain precise knowledge of the systems environment and its work pieces is proposed.

In the literature only few approaches for three-dimensional radar target recovery in the short range can be found. Furthermore, these approaches presume different kinds of regular patterns in which the radar sensors must be placed. This is obviously contrary to the requirements of e.g. industrial robots which can be moved on arbitrary trajectories. The most challenging problem when it comes to target recovery from randomly distributed radar sensors is the high computational effort which has to be spent.

In this work an algorithm is presented which aims at recovering several point targets from as few as possible radar measurements. The whole work is based on simulations and analytical considerations. Each radar uses well-known frequency modulated continuous waveform signals to gain information in range direction. As stated in the title the recovering is carried out by a compressed sensing algorithm. To use this algorithm the whole problem has to be transformed into a linear system of equations, small enough to be solved by commercially available computers in an amount of time which is suited for the application. This linear equation eventually consists of the received radar signals, a measurement matrix and the reflectivity of assumed targets located in a-priori defined voxels. As the number of voxels which have to be taken into account increases drastically with the desired resolution, an iterative approach had to be developed to limit the algorithms requirements in terms of computational effort. Finally, different suited recovery algorithms were compared in terms of robustness.

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How to cite


Reißland, T., Kölpin, A., & Weigel, R. (2017, September). Target Recovery from Randomly Distributed Radars using Compressed Sensing Techniques. Paper presentation at Kleinheubacher Tagung, Miltenberg, DE.


Reißland, Torsten, Alexander Kölpin, and Robert Weigel. "Target Recovery from Randomly Distributed Radars using Compressed Sensing Techniques." Presented at Kleinheubacher Tagung, Miltenberg 2017.

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