# Passive Detection of Low-Altitude Signal Sources Using an Improved Cross-Correlation Algorithm
## 1 Introduction  
The detection and management of low-altitude signal sources have recently attracted significant research interest [1]. An unmanned aerial vehicle (UAV) is a typical low-altitude signal source which communicates with a controller using radio frequency (RF) signals [2]. According to a Consumer Electronics Association (CEA) survey, global sales of UAVs reached 69 million in 2015 and may exceed 1 billion by 2020. In addition, UAV costs are decreasing, while the number of applications is increasing [3; 4; 5; 6]. Civilian UAVs have become widespread and are now affecting air traffic, disrupting operations, and violating privacy laws [7]. Many techniques have been proposed to detect aerial targets including laser scanners, acoustic detectors, infrared thermal cameras, and visual observations. Acoustic detection is only effective over short distances of 300 m or less [2]. Distinguishing different low-altitude signal sources beyond 1.5 km is difficult using infrared thermal cameras and visual observations [8]. A laser scanner transmits a pulse signal and measures the propagation time, so active target detection is employed [9]. Thus, these techniques are not suitable for passive detection of low-altitude signal sources at distances up to 3 km. This motivates the development of new methods in this paper. Passive radar can be used to detect signal sources by sensing the corresponding RF signals [10]. Linear fusion has been employed previously [11] for target recognition in a passive multistatic radar system, and bistatic range measurements have been used to find the position of a target [12]. In [13], the location of a continuous wave signal source was estimated passively using the phase variance. Passive estimation of the position of a high-altitude aircraft was achieved in [14] using a satellite signal. The detection of moving targets in multipath environments with a passive radar was studied in [15]. In [16], the location of a target was obtained using a passive coherent method with the help of an illuminator. These approaches were employed only for ground and high-altitude target detection and most require the assistance of a satellite or mobile transmitter. The passive detection of RF signals from low-altitude signal sources has not been adequately investigated, particularly when auxiliary information or transmitters are not available. Thus, in this paper, the RF communication signals from a target such as a UAV are considered for the passive detection of low-altitude sources. Cross-correlation is a common signal detection method in radar systems and has been employed in both the frequency and time domains [17; 18; 19; 20]. In [17], a Gaussian mixture model was proposed to sense double-talk using cross-correlation in the frequency domain. Cross-correlation and a finite state machine were employed in [18] to detect vehicles parked indoors. In [19], an algorithm for phase offset estimation was developed using the Hilbert transform and signal cross-correlation. Cross-correlation has also been used for seismic monitoring [20]. In this paper, cross-correlation in the time-frequency domain is employed for the passive detection of low-altitude signal sources. Several parameters can be estimated for a signal source, such as the direction of arrival (DOA), frequency range, signal-to-noise ratio (SNR), and the distance between the source and receiver [21; 22; 23; 24; 25]. A DOA estimation algorithm for low-altitude targets was proposed in [21] which employs a microphone array [21]. In [22], adaptive frequency estimation was implemented using a data-selection strategy. The SNR was obtained when the useful signal occupies a separate range in the frequency domain and all other components are clutter [23]. In [24], the distance was estimated using the received signal strength indicator (RSSI). Parameter estimation for low-altitude signal sources is considered here. In this paper, an improved cross-correlation method for the passive detection of low-altitude signal sources is proposed. The contributions are as follows. 1.