šŸ…¼ oscfar.cfar

Functions

  1. šŸ…µ oscfar.cfar.os_cfar_1d
  2. šŸ…µ oscfar.cfar.variable_window_cfar
  3. šŸ…µ oscfar.cfar.variable_window_os_cfar_indices

šŸ…µ oscfar.cfar.os_cfar_1d 

def os_cfar_1d(data, guard_cells, train_cells, rank_k, threshold_factor):

Performs 1D Ordered Statistic Constant False Alarm Rate (OS-CFAR) detection.

Parameters:

  • data (np.ndarray): 1D array of input data (must be in linear power, not dB).
  • guard_cells (int): Number of guard cells on EACH side of the CUT.
  • train_cells (int): Number of training cells on EACH side of the CUT.
  • rank_k (int): The rank (1-based index) of the sorted training cell values to use for noise estimation (1 <= rank_k <= N). N = 2 * train_cells is the total number of training cells. A common choice is around 0.75 * N.
  • threshold_factor (float): The scaling factor (alpha) to multiply the noise estimate by to get the threshold.

Returns:

  • tuple: A tuple containing:
  • detected_peaks_indices (np.ndarray): Indices where peaks were detected.
  • threshold (np.ndarray): The calculated threshold for each cell. (Same size as input data, padded with NaNs at edges where CFAR wasnā€™t computed).

šŸ…µ oscfar.cfar.variable_window_cfar 

def variable_window_cfar(data, guard_cells, min_window, max_window, homogeneity_threshold):

A basic implementation of a Variable Window CFAR detector using a split-window approach.

Parameters:

  • data (np.ndarray): The input signal data (1D).
  • guard_cells (int): The number of guard cells on each side of the CUT.
  • min_window (int): The minimum number of reference cells on each side.
  • max_window (int): The maximum number of reference cells on each side.
  • homogeneity_threshold (float): A threshold to determine if the reference windows are considered homogeneous.

Returns:

  • np.ndarray: A boolean array indicating detections (True) and non-detections (False).

šŸ…µ oscfar.cfar.variable_window_os_cfar_indices 

def variable_window_os_cfar_indices(data, guard_cells, min_window, max_window, k_rank, homogeneity_threshold, threshold_factor):

A basic implementation of a Variable Window OS-CFAR detector returning detection indices.

Parameters:

  • data (np.ndarray): The input signal data (1D).
  • guard_cells (int): The number of guard cells on each side of the CUT.
  • min_window (int): The minimum number of reference cells on each side.
  • max_window (int): The maximum number of reference cells on each side.
  • k_rank (int): The rank of the order statistic to use for noise estimation.
  • homogeneity_threshold (float): A threshold to determine if the reference windows are considered homogeneous.
  • threshold_factor (float): Factor multiplied by the noise estimate for the threshold.

Returns:

  • np.ndarray: An array of indices where detections occurred.