We implement a distributed Bragg reflector (DBR) laser diode (LD) package with low-magnetic field generation. The package consists of a commercial 795 nm DBR LD chip, a thermo-electric cooler (TEC), a thermistor, a flexible printed circuit board (FPCB) which cancels the magnetic field emitted by the TEC current flow, and a non-magnetic aluminum case. We confirm that the magnetic dipole moment of our low-magnetic package body is about three orders of magnitude smaller than that of a commercial DBR laser package. Moreover, it is shown that our compensating FPCB, the effectiveness of which is supported by computer simulations, reduces the magnetic field magnitude by a factor of 2.2. The FPCB also reduces the magnetic field gradient emitted by the TEC current flow so that gradient-induced spin relaxations are suppressed in applications. A portable optically pumped atomic magnetometer (OPAM) utilizing two low-magnetic packages as light sources is reported as an application of the package and shows a 0.30 pT/Hz1/2 level magnetic sensitivity at a 69 μT external magnetic field; in contrast, the OPAM utilizing the commercial packages showed a magnetic sensitivity of 0.87 pT/Hz1/2.
To detect targets for autonomous navigation of unmanned ground vehicle, mounted sensors are required to work all-weather condition. In this point of view, the FMCW radar is quietly appropriate. In this paper, we present development results of target signal simulator for multi-beam type FMCW radar. A target signal simulator make pseudo target signals which simulates multiple moving targets. And we describe how to make hit information for each target in multi-beam type radar. The developed methods are utilized for target tracking device. Moreover it can be applied to similar target signal simulator.
Recently many studies of Radar systems mounted on ground vehicles for autonomous driving, SLAM (Simultaneous localization and mapping) and collision avoidance are reported. In near field, several hits per an object are generated after signal processing of Radar data. Hence, clustering is an essential technique to estimate their shapes and positions precisely. This paper proposes a method of grouping hits in range-doppler domains into clusters which represent each object, according to the pre-defined rules. The rules are based on the perceptual cues to separate hits by object. The morphological connectedness between hits and the characteristics of SNR distribution of hits are adopted as the perceptual cues for clustering. In various simulations for the performance assessment, the proposed method yielded more effective performance than other techniques.
In this paper, we proposed a method of fast track merging which is the foundation of track to track association technique. The existing method of track merging is performed throughout comparison between tracks to tracks. Therefore, it has heavy calculation time. In our research, we developed a method for fast clustering by using nearest neighbor measurement identification. The simulation results show that the proposed method is more faster than previous method about 3.3%. We expect that this method could be effectively used in multi-target tracking particularly in heavy clutter environment.
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