Multi-Target/Multi-Sensor Tracking using Only Range and Doppler Measurements

Abstract

A new approach is described for combining range and Doppler data from multiple radar platforms to perform multi-target detection and tracking. In particular, azimuthal measurements are assumed to be either coarse or unavailable, so that multiple sensors are required to triangulate target tracks using range and Doppler measurements only. Increasing the number of sensors can cause data association by conventional means to become impractical due to combinatorial complexity, i.e., an exponential increase in the number of mappings between signatures and target models. When the azimuthal resolution is coarse, this problem will be exacerbated by the resulting overlap between signatures from multiple targets and clutter. In the new approach, the data association is performed probabilistically, using a variation of expectation-maximization (EM). Combinatorial complexity is avoided by performing an efficient optimization in the space of all target tracks and mappings between tracks and data. The full, multi-sensor, version of the algorithm is tested on simulated data. The results demonstrate that accurate tracks can be estimated by exploiting spatial diversity in the sensor locations. Also, as a proof-of-concept, a simplified, single-sensor range-only version of the algorithm is tested on experimental radar data acquired with a stretch radar receiver. These results are promising, and demonstrate robustness in the presence of nonhomogeneous clutter.

Language and Cognition Interaction Neural Mechanisms

“…Lower layer models may require continuous parametric models, like laryngeal models of phonemes [57]. These can be learned from language sounds using parametric models [58–69] similar to a preceding section on perception. …”

Moving target detection and characterization with circular SAR

“…The target PDF is therefore obtained by substituting (5) into (4). The clutter model uses Gaussian feature model.…”

“…More specifically, we are tailoring DL to the problem of simultaneous detection, classification, and tracking of ground targets with respect to radar data collected from a circular airborne path. Previously, we used DL to develop algorithms for detection and tracking targets in conventional GMTI radar data having low signal-tointerference ratios with significant improvement [2][3][4][5][6]. In the following sections we present our general approach to target characterization and describe exploitation using the Gotcha radar experimental data, which was collected during a series of tests conducted by AFRL at Wright-Patterson Air Force base [7] [8].…”

Application of dynamic logic algorithm to analyze AFRL gotcha data

“…In addition, application of DL directly to analyze SAR raw data can reduce the computational cost but requires the conceptual interpretation for analyzed results. Recently, application of DL [2,3] has demonstrated its merit in terms of assisting ISR systems to simultaneously detect, track and classify moving targets. DL can also be applied to analyze radar, electro-optical, and acoustical data and to exploit useful information for layered sensing to create situational awareness during irregular warfare which includes electronic warfare and cyber attack.…”