Maximum likelihood (ML) based localization algorithm for multi-static passive radar using range-only measurements

Passive radar has been finding increasing application and use in last decade. Passive radar uses the "opportunistic" commercial signals for their operation. The passive radar does provide good Doppler resolution but suffer from bad range resolution. One way to improve the range resolution is to use passive radar in a multi-static fashion. By using the data collected at various multi-static sites, the range resolution can be improved. Therefore, there is a need for an algorithm for target localization using these multi-static measurements. Typically, these algorithms use range-only measurements. Various multi-static configurations are possible in passive radar setup. However, this paper will only consider the setup with single receiver, and multiple spatially distributed transmitter locations. Localization algorithm based on Maximum likelihood (ML) estimate will be presented, along with its efficient implementation using gradient and Newton's decent algorithms. The performance bounds for ML estimate in terms of Fisher information are also given. All the algorithms are verified using simulated data in 2D settings.