An object position estimating apparatus which estimates positions of M objects in a real space, M being an integer greater than or equal to 2, characterized by: a memory configured to store N dimensional characteristic vectors for each of the M objects, N being an integer greater than or equal to 3, each characteristic vector including, as N components, measurements of closeness for each object to N reference points in the real space with M external microphones, as the M objects, collecting sounds from the N reference points to measure the closeness based on acoustic wave arrival time or amplitude of a specific frequency component in an output signal of each microphone for each sound of the collected sounds from the N reference points, or with N external microphones, as the N reference points, collecting sounds from the M objects to measure the closeness based on acoustic wave arrival time or amplitude of a specific frequency component in an output signal of each microphone for each sound of the collected sounds from the M objects; and a processor configured to output a position estimation result based on the characteristic vectors stored in the memory, wherein the processor is further configured to: calculate a norm of a difference between the characteristic vectors of two objects for every pair from among the M objects; store the calculated norms as elements of a dissimilarity matrix with M rows and M columns in the memory; and estimate positions of the M objects in the real space by performing predetermined data processing on the basis of the dissimilarity matrix, the predetermined data processing being to apply a multidimensional scaling method to the dissimilarity matrix to estimate the positions of the M objects in the real space, and to output position estimation result. a memory configured to store N dimensional characteristic vectors for each of the M objects, N being an integer greater than or equal to 3, each characteristic vector including, as N components, measurements of closeness for each object to N reference points in the real space with M external microphones, as the M objects, collecting sounds from the N reference points to measure the closeness based on acoustic wave arrival time or amplitude of a specific frequency component in an output signal of each microphone for each sound of the collected sounds from the N reference points, or with N external microphones, as the N reference points, collecting sounds from the M objects to measure the closeness based on acoustic wave arrival time or amplitude of a specific frequency component in an output signal of each microphone for each sound of the collected sounds from the M objects; and a processor configured to output a position estimation result based on the characteristic vectors stored in the memory, wherein the processor is further configured to: calculate a norm of a difference between the characteristic vectors of two objects for every pair from among the M objects; store the calculated norms as elements of a dissimilarity matrix with M rows and M columns in the memory; and estimate positions of the M objects in the real space by performing predetermined data processing on the basis of the dissimilarity matrix, the predetermined data processing being to apply a multidimensional scaling method to the dissimilarity matrix to estimate the positions of the M objects in the real space, and to output position estimation result. calculate a norm of a difference between the characteristic vectors of two objects for every pair from among the M objects; store the calculated norms as elements of a dissimilarity matrix with M rows and M columns in the memory; and estimate positions of the M objects in the real space by performing predetermined data processing on the basis of the dissimilarity matrix, the predetermined data processing being to apply a multidimensional scaling method to the dissimilarity matrix to estimate the positions of the M objects in the real space, and to output position estimation result.