This work presents a novel fusion mechanism for estimating the three-dimensional trajectory of a moving target using images collected by multiple imaging sensors. The proposed projective particle filter avoids the explicit target detection prior to fusion. In projective particle filter, particles that represent the posterior density (of target state in a high-dimensional space) are projected onto the lower-dimensional observation space. Measurements are generated directly in the observation space (image plane) and a marginal (sensor) likelihood is computed. The particles states and their weights are updated using the joint likelihood computed from all the sensors. The 3D state estimate of target (system track) is then generated from the states of the particles. This approach is similar to track-before-detect particle filters that are known to perform well in tracking dim and stealthy targets in image collections. Our approach extends the track-before-detect approach to 3D tracking using the projective particle filter. The performance of this measurement-level fusion method is compared with that of a track-level fusion algorithm using the projective particle filter. In the track-level fusion algorithm, the 2D sensor tracks are generated separately and transmitted to a fusion center, where they are treated as measurements to the state estimator. The 2D sensor tracks are then fused to reconstruct the system track. A realistic synthetic scenario with a boosting target was generated, and used to study the performance of the fusion mechanisms.
In this work we study the problem of detecting and tracking challenging targets that exhibit low signal-to-noise ratios (SNR). We have developed a particle filter-based track-before-detect (TBD) algorithm for tracking such dim targets. The approach incorporates the most recent state estimates to control the particle flow accounting for target dynamics. The flow control enables accumulation of signal information over time to compensate for target motion. The performance of this approach is evaluated using a sensitivity analysis based on varying target speed and SNR values. This analysis was conducted using high-fidelity sensor and target modeling in realistic scenarios. Our results show that the proposed TBD algorithm is capable of tracking targets in cluttered images with SNR values much less than one.
Target detection and tracking with passive infrared (IR) sensors can be challenging due to significant degradation and corruption of target signature by atmospheric transmission and clutter effects. This paper summarizes our efforts in phenomenology modeling of boosting targets with IR sensors, and developing algorithms for tracking targets in the presence of background clutter. On the phenomenology modeling side, the clutter images are generated using a high fidelity end-to-end simulation testbed. It models atmospheric transmission, structured clutter and solar reflections to create realistic background images. The dynamics and intensity of a boosting target are modeled and injected onto the background scene. Pixel level images are then generated with respect to the sensor characteristics. On the tracking analysis side, a particle filter for tracking targets in a sequence of clutter images is developed. The particle filter is augmented with a mechanism to control particle flow. Specifically, velocity feedback is used to constrain and control the particles. The performance of the developed “adaptive” particle filter is verified with tracking of a boosting target in the presence of clutter and occlusion.
Proc. SPIE. 8050, Signal Processing, Sensor Fusion, and Target Recognition XX
KEYWORDS: Detection and tracking algorithms, Matrices, Digital filtering, Error analysis, Computer simulations, Monte Carlo methods, Time metrology, Bismuth, Electronic filtering, Filtering (signal processing)
In many applications where communication delays are present, measurements with earlier time stamps can arrive
out-of-sequence, i.e., after state estimates have been obtained for the current time instant. To incorporate such
an Out-Of-Sequence Measurement (OOSM), many algorithms have been proposed in the literature to obtain or
approximate the optimal estimate that would have been obtained if the OOSM had arrived in-sequence. When
OOSM occurs repeatedly, approximate estimations as a result of incorporating one OOSM have to serve as the
basis for incorporating yet another OOSM. The question of whether the "approximation of approximation" is
well behaved, i.e., whether approximation errors accumulate in a recursive setting, has not been adequately
addressed in the literature. This paper draws attention to the stability question of recursive OOSM processing
filters, formulates the problem in a specific setting, and presents some simulation results that suggest that such
filters are indeed well-behaved. Our hope is that more research will be conducted in the future to rigorously
establish stability properties of these filters.