🅼 oscfar.cfar

Functions

1. 🅼 oscfar.cfar
   1. 🅵 oscfar.cfar.os_cfar_1d
   2. 🅵 oscfar.cfar.variable_window_cfar
   3. 🅵 oscfar.cfar.variable_window_os_cfar_indices

🅵 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:

• detected_peaks_indices (np.ndarray): Indices where peaks were detected. threshold (np.ndarray): Threshold for each input cell (NaN at edges where CFAR is not computed).

  🅵 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:

• detections (np.ndarray): Boolean array of detections (True) and non-detections (False).

  🅵 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:

• detection_indices (np.ndarray): Indices where detections occurred.