Long-time coherent integration for low SNR target via particle filter in track-before-detect

International audienceThis article addresses the problem of detecting and tracking a target at very low SNR via track-before-detect. Since in this framework targets are undetectable on one single time step, coherent integration must be performed over time to gather sufficient energy and obtain good detection performance. As this integration must be done along a given target trajectory, we propose to apply a particle filter to perform the estimation over time. This particle filter samples the target delay, velocity and amplitude to perform a coherent processing. Two detection tests based on the particles are then proposed to solve the detection problem. Finally we propose also a new particle filter inspired by a GLRT strategy that does not need to sample the amplitude. Both filters converge on the true target state at very low SNR, the latter converging faster than the former

Multitarget likelihood for Track-Before-Detect applications with amplitude fluctuations

International audienceTrack-Before-Detect methods jointly detect and track one or several targets from raw sensor measurements. They often require the computation of the measurement likelihood conditionally to the hidden state that depends on the complex amplitudes of the targets. Since these amplitudes are unknown and fluctuate over time this likelihood must be marginalized over the complex amplitude (i.e. phase and modulus). It has been demonstrated in [1] that this marginalization can be done analytically over the phase in the monotarget case. In this article, we first propose to extend the marginalization to the modulus in a monotarget setting, and we show that closed-forms can be obtained for fluctuations of type Swerling 1 and 3. Second, we demonstrate that, in a multitarget setting, a closed-form can be obtained for the Swerling 1 case. For Swerling 0 and 3 models, we propose some approximation to alleviate the computation. Since many articles consider the case of squared modulus measurements, we also consider this specific case in mono and multitarget settings with Swerling 0, 1 and 3 fluctuations. Finally, we compare the performance in estimation and detection for the different cases studied and we show the gain, both in detection and estimation, of the complex measurement method over the squared modulus method, for any fluctuation model