Agile multi-modal tracking with dependent measurements

We investigate the target tracking problem of adapting asymmetric multi-modal sensing operation platforms using radio frequency (RF) radar and electro-optical (EO) sensors. Although the multi-modality framework allows for the integration of complementary information, there are many challenges to overcome, including targets with different energy returns, and information loss due to low signal-to-noise ratio (SNR) or due to dependent measurements from different sensors that are not appropriately processed. We develop the particle filter (PF) based recursive track before detect (TBD) algorithm for joint RF-EO tracking to avoid loss of information caused by matched filter thresholding at low SNR. A waveform optimization technique is integrated into the PF-TBD to allow for adaptive waveform selection. We also approximate distributions of parameters of dependent RF and EO measurements using the embedded exponential family (EEF) approach to further improve target detection and tracking performance.