# Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar
## 1 Introduction  
Passive bistatic radar (PBR) has attracted the attention of many researchers in the recent years, due to its low cost, convert operation, and no need for frequency allocations [1, 2, 3, 4, 5, 6]. Various kinds of illuminators of opportunity are exploited as transmitters of the passive bistatic radar for target detection, such as FM radio [7, 8, 9], Digital Video Broadcasting-Terrestrial [10, 11], WIFI [12, 13], and GSM [14]. The key problem of passive bistatic radar is that the illuminators are not radar-designed, and the transmitting power is low, which leads to a limited probing distance. When compared with the aforementioned illuminators, the Global Navigation Satellite Systems (GNSS) signal [15, 16] are more attractive for its global coverage and spatial diversity. Each point on the earth is illuminated by 6-8 GNSS satellites simultaneously at any time for each constellation, and a minimum of 24 satellites are guaranteed if four GNSS constellations are in full capacity [17]. Moreover, each GNSS constellation operates on code or frequency division, and the signals that are transmitted by every satellite can be effectively discriminated. However, two major issues should be considered for GNSS-based passive radar. One is that satellite signals are continuous wave, and the target echoes are always interfered by the direct-path wave and multipath echoes; the other is that the low power level substantially limits the target detection range. The direct-path wave and multipath echoes have almost zero Doppler, and the power of them is much higher than that of the target echo, and the weak target echo may be submerged by them. Hence, clutter cancellation is an essential step in passive radar signal processing. In this paper, direct-path wave and multipath echoes are referred as clutter. A variety of methods have been proposed for clutter cancellation. The adaptive filtering approach, which contains least mean square (LMS) [18], normalized least mean square (NLMS) [19], recursive least square (RLS) [20], and so on, is the most commonly used. However, these methods suffer from heavy computational burden and the filter orders are always uncertain. A trade-off must be made between the converge rate and running time. Another classical approach for clutter cancellation is the extensive cancellation algorithm (ECA) [21], which is derived from the least squares (LS) method and subspace projection technique. The captured signal is projected onto a subspace that is orthogonal to the clutter subspace. However, the computational burden of the method is high. To overcome this limitation, many modified algorithms have been proposed, such as the batches version of ECA (ECA-B) [22], ECA-S (ECA-sliding) [23], and so on. Besides, some researchers focus on the ground clutter cancellation [24; 25]. The ground clutter removal is achieved using the framing based mean removal and subspace decomposition [24]. In [25], the clutter position is extracted by implementing a correlation-based method and it is then used to remove the radar self-motion effects. In addition to clutter cancellation, long time integration is also an important step in target detection. The low effective isotropic radiated power (EIRP) leads to the target detection challenge [26]. In order to counteract the low signal level, the target echoes need to be integrated over long time integrations to obtain a promising maximum detectable range. Additionally, the integration times are always up to dozens of seconds. However, the subsequent issue that arises with the increase of integrated time is that the Doppler frequency may migrate through Doppler resolution cells, which degrade the target detection performance. Moreover, for air moving targets, the motion of the target is always maneuvering and the Doppler frequency is time-varying severely during the coherent processing interval (CPI).